Advancing Forest Fragmentation Analysis: A Systematic Review of Evolving Spatial Metrics, Software Platforms, and Remote Sensing Innovations

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Remmel², Carlos A. Rivas³, Adriano Mazziotta⁴, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7684385/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Landscape Ecology → Version 1 posted 10 You are reading this latest preprint version Abstract Context Forest fragmentation—the breakup of continuous habitat into isolated patches—alters landscape processes and biodiversity. Rapid advances in sensors and computing have diversified diagnostic methods, but comparability and ecological linkage remain uneven. Objectives Synthesize 138 methodological studies (1990–2025) to: (i) chart shifts in metric families, including emerging 3-D approaches; (ii) assess how data and processing choices shape indicator performance; and (iii) distill limits and reporting practices that improve portability. Methods We reviewed studies using lidar/TLS and Sentinel-2 inputs, change detection, and indicators implemented in landscapemetrics, GuidosToolbox, and Google Earth Engine, tracing transitions from patch/edge metrics to morphology-aware roles, connectivity, fixed-window density, and 3-D/voxel measures. Results The field is moving toward morphology-aware roles, multiscale connectivity, fixed-scale density, and vertical structure. Five recurring limits are: scale sensitivity and habitat-amount confounding; region-tuned parameters that hinder transfer; scarce field validation of global/automated products; weak or inconsistent biotic links of structural metrics; and incomplete reporting that curbs reproducibility. Gaps include uneven tropical coverage and limited 2-D/3-D cross-walks. Priorities are transparent parameterization and sensitivity checks, precise documentation of spatial/detector settings, region-specific benchmarking, shareable workflows, and integration of field data. Summary Standardizing documentation, validation, and cross-scale linkages can improve the reliability of fragmentation measures for monitoring and conservation. Emphasis should be on refining and harmonizing existing methods rather than proposing new indices forest fragmentation landscape metrics change detection lidar open-source workflows methodological synthesis Figures Figure 1 Figure 2 Figure 3 1. Introduction Forest fragmentation—the division of continuous forest into smaller, more isolated patches—creates edge environments, reshapes ecological processes, and can accelerate biodiversity loss (McGarigal & Marks, 1995 ; Heilman et al., 2002 ; Riitters et al., 2007; Bennett & Radford, 2008 ). Over two-thirds of global forests now lie within 1 km of an edge (Haddad et al., 2015 ; Siegel et al., 2024 ), with pressures most pronounced in tropical and subtropical regions (Lung & Schaab, 2006 ; Giriraj et al., 2010 ). Fragmentation is distinct from habitat loss: loss reduces area, whereas fragmentation concerns how a fixed amount of forest is arranged by patch size, shape, and isolation (Fahrig, 2003 , 2019 , 2024 ; Fardila et al., 2017 ). Connectivity—the degree to which landscape structure facilitates or restricts movement—adds a further interpretive layer (Bogaert et al., 2000 ; Vogt et al., 2007 ; Lausch et al., 2015 ). Although patch-scale edge effects are well documented, landscape-scale responses do not always follow directly from local patterns (Fletcher et al., 2018 ; Fahrig, 2024 ), underscoring the need for scale-declared, method-transparent assessments that can be linked to ecological data where available (Bennett & Radford, 2008 ). Methodologically, practice has progressed from early patch/edge/shape tallies to morphology- and connectivity-aware approaches, fixed-window density measures, and first-generation 3-D/voxel indicators. FRAGSTATS and Patch Analyst established a common language for pattern quantification (McGarigal & Marks, 1995 ; Elkie et al., 1999 ). Role-based morphology—exemplified by morphological spatial pattern analysis (MSPA)—made cores, edges, bridges, corridors, and perforations legible at reporting scales (Vogt et al., 2007 ; Wickham et al., 2010 ). Advances in remote sensing, including lidar/TLS for canopy structure, together with robust time-series change detection (e.g., Vegetation Change Tracker, LandTrendr, Two-Thresholds Method), have enabled consistent disturbance trajectories that feed downstream indicators (Maier et al., 2006 ; Huang et al., 2010 ; Zald et al., 2016 ; Kennedy et al., 2018 ; Giannetti et al., 2020 ). Open, scriptable ecosystems and cloud platforms— landscapemetrics , PyLandStats, GuidosToolbox, and Google Earth Engine—now support auditable pipelines from data ingestion to indicators. In parallel, neutral landscape generators (e.g., Landscape Generator; flsgen) provide realistic, controlled mosaics to test sensitivity and to separate composition from configuration (van Strien et al., 2016 ; Peura et al., 2018 ; Justeau-Allaire et al., 2022 ). Despite this expansion in capability, five recurring issues complicate inference and comparability across studies: (i) sensitivity to grain and extent and the attendant conflation with habitat amount; (ii) regional dependence of thresholds and assumptions; (iii) gaps in external/empirical validation—especially for global products and emergent 3-D indicators; (iv) loose coupling to biological responses; and (v) incomplete parameter reporting (O’Neill et al., 1999 ; Hernando et al., 2017 ; Zatelli et al., 2019 ; Fletcher et al., 2018 ; Vergara et al., 2021 ; Feleha et al., 2025 ). Recent families—fixed-window density measures such as Forest Area Density (FAD), role-based morphology paired with graph metrics, and voxel/3-D approaches—address parts of this problem yet introduce assumptions that must be declared and tested. This review analyzes 138 methodological studies (1990–2025) to: map the evolution from patch-based measures to connectivity, shape complexity, fixed-window density, and emerging 3-D approaches; evaluate how remote sensing and time-series/change-detection methods—together with compositing/segmentation choices and cloud platforms—condition the accuracy and comparability of fragmentation indicators; and diagnose common limitations and summarize practical reporting elements (e.g., grain/extent, edge width, window size, detector settings, MMU, validation) that make results more portable across regions and scales. 2. Methodology 2.1 Literature search We followed PRISMA 2020 (Page et al., 2021 ) to ensure a transparent, reproducible process. We targeted peer-reviewed journal articles published 1990–2025 and ran searches from 3 Oct 2024 to 9 Sep 2025 using Publish or Perish (Harzing, 2010 ) across Google Scholar, Scopus, and Web of Science. We limited results to English and article document types; conference papers, theses, books/chapters, and reports were excluded. Studies were included if they proposed, evaluated, or systematically applied methods/metrics/workflows for forest fragmentation (e.g., structural/configurational metrics, change-detection feeding fragmentation indicators, graph/MSPA/fixed-window/3-D approaches), and excluded if purely ecological case studies without methodological contribution or non-forest contexts unless methods were demonstrated for forests. To keep scope forest-specific we filtered out terms such as “urban,” “animal*,” and “bird*” unless paired with “forest.” Database searches returned 1,160 records; Litmaps/Connected Papers, citation chasing, and colleague recommendations added 46, for 1,206 records prior to deduplication. Title/abstract and full-text screening applied the criteria above; reasons for exclusion are shown in PRISMA Fig. 1, and verbatim database search strings are provided in Supplementary Table S1 . 2.2 Screening Process We removed 313 duplicate records using Rayyan (Ouzzani et al., 2016 ), leaving 847 unique articles. Title/abstract/keyword screening excluded 233 items without a methodological focus, 152 outside a forest context, and 251 that reported ecological impacts only without a fragmentation-methods component. We sought 211 full texts; 5 could not be retrieved, leaving 206 for full-text assessment. In parallel, we identified 46 additional records via reference tracking (Litmaps, Connected Papers) and colleague recommendations, which were included at the full-text stage. 2.3 Study Selection Full texts were assessed against predefined criteria: (i) proposes, evaluates, or systematically applies methods to measure forest fragmentation (spatial/configurational metrics, remote-sensing/change-detection feeding fragmentation indicators, graph/MSPA/fixed-window/3-D approaches); (ii) forest context or forest-reporting subset; (iii) peer-reviewed journal article; (iv) publication 1990–2025; (v) English, full text available; and (vi) unique contribution. Of the 206 database-sourced articles, 96 were excluded: 24 background/narrative, 28 not methodological, 10 not forest-related, 17 redundant (repetitive case applications of standard landscape-metrics workflows—e.g., FRAGSTATS or Patch Analyst—without methodological novelty or evaluation), and 17 other reasons (insufficient methodological detail or peripheral scope). Of the 46 additional records, 12 were excluded at full text (5 impact-focused only, 5 outside scope/publication window, 2 background). The final set comprised 138 methodological studies: 110 from databases and 34 from other sources (see PRISMA Fig. 1). 2.4 Limitations This review is limited to English-language, peer-reviewed journal articles. While this may omit some regional contributions, English dominates publication in this domain, and citation chasing in our included papers did not repeatedly surface non-English methodological keystones. Our search emphasized method/tool terms (e.g., FRAGSTATS, GuidosToolbox) to capture studies that develop or evaluate fragmentation methods; broader ecological terms would have inflated returns with impact-only studies and diluted the methodological focus. This emphasis could favor papers that explicitly name software; we countered it through snowballing (Litmaps, Connected Papers) and colleague recommendations. By design, we prioritized methods-focused work and excluded descriptive case studies that simply reuse existing metrics, which limits ecological context but preserves a clear methods synthesis. Finally, full-text screening was conducted by one reviewer using predefined criteria and Rayyan, with co-author oversight at inclusion; this does not remove selection bias entirely, but it provides a consistent and auditable screen. 3. Results 3.1 Scope and Organization This review synthesizes 138 studies (1990–2025) on methodological approaches to quantifying forest fragmentation. We organize findings along three linked components: (i) data sources (remote-sensing and ground-based inputs), (ii) change-detection methods (time-series analyses that derive disturbance/transition signals), and (iii) landscape pattern indicators (metrics of configuration and connectivity). In practice, data sources feed change detection, which then supports indicator calculation (Fig. 2). The columns in Fig. 2 group periods by sensor-era shifts, showing a progression from early, primarily Landsat-based work to multi-sensor approaches that integrate increasingly higher spatial and temporal resolution sensors, including lidar, to improve temporal fidelity and structural sensitivity. 3.2 Evolution of Landscape Pattern Indicators Initial investigations in the 1990s emphasized two-dimensional assessments of forest patches, including patch counts, area metrics, edge lengths, and core area delineations (Ripple et al., 1991 ; McGarigal & Marks, 1995 ; Jorge & Garcia, 1997 ; Walker & Kenkel, 1998 ). These methodologies, while systematically replicable, offered limited insight into ecological connectivity or species dispersal potential. By the early 2000s, the introduction of effective mesh size provided a more nuanced metric for evaluating landscape subdivision (Jaeger, 2000 ). Subsequent developments around 2007 incorporated role-based classifications—such as core areas, edge zones, and connective bridges—enhancing the spatial resolution of forest network analyses (Vogt et al., 2007 ; Tejaswi, 2007 ). Post-2010, connectivity-focused indicators, including Probability of Connectivity (PC) and Integral Index of Connectivity (IIC), gained prominence, featuring in approximately 25% of studies, particularly for informing wildlife corridor design (Estreguil & Mouton, 2009 ; Ye et al., 2020 ; Lin et al., 2021 ; Ramezani & Ramezani, 2021 ). Recent advancements have diversified into three categories: (1) fixed-scale density metrics, exemplified by Forest Area Density (FAD), enabling inter-regional comparability (Vogt & Caudullo, 2025 ); (2) shape and connectivity indices, such as Forest Fragmentation Index (FFI) and Local Connectedness (LCFD), which account for patch morphology and local linkages while mitigating data inaccuracies (Alage et al., 2025 ); and (3) three-dimensional assessments leveraging lidar to elucidate canopy structure and vertical connectivity, addressing limitations of planar analyses (Zald et al., 2016 ; Nowosad & Stepinski, 2021 ; Remmel, 2022 ; Zhen et al., 2023 ; Lin et al., 2024 ). A comprehensive overview of these tools is presented in Table 1 and Supplementary Table S2. Table 1 Evolution of toolsets for forest fragmentation—concise view. Grouped by period, showing key tools, their contributions, and tags. Full details in Supplementary Table S2. Period Exemplary toolsets What it adds Key refs Pre-2000 FRAGSTATS; Patch Analyst; pMAP Baseline patch/class metrics (area, edge, shape, core); early proximity/contagion (RS/GIS) McGarigal & Marks ( 1995 ); Elkie et al. ( 1999 ) Khoros Simulated patterns; metric correlation; early eco-response tests Hargis et al. ( 1999 ) 2000–2009 GUIDOS/APACK MSPA roles; moving-window (FAD/entropy); continental mapping (QGIS/Prog) Vogt et al. ( 2007 ); Wulder et al. ( 2008 ); Soverel et al. ( 2010 ) ERDAS/IDRISI/eCognition object-based image analysis ( OBIA ) segmentation; Lidar-derived metrics; early CA–Markov Maier et al. ( 2006 ); Meddens et al. ( 2008 ) ArcIMS + FRAGSTATS Web-mapping + classical metrics (Web-GIS) Wang ( 2002 ); Southworth et al. ( 2004 ) 2010–2019 LFT Core/edge/perforated/patch; morph segmentation (ArcGIS) Kopecká & Nováček ( 2010 ); Conefor + Circuitscape/Linkage Connectivity (PC/IIC/dI; circuit/least-cost corridors) (Graph/Circuit) Saura & Torné ( 2009 ); McRae et al. ( 2008 ) landscapemetrics; motif; PyLandStats; LecoS; ShrinkShape2 Reproducible pipelines; pattern signatures; rotation-invariant shape (R/QGIS/Py) Hesselbarth et al. ( 2019 ); Remmel ( 2015 ) PolyFrag; FRAGSTATS v3.3 Vector-aware metrics; custom edge width (GIS) MacLean & Congalton ( 2013 ) Feeders: VCT; LandTrendr; CCDC; TTM Stable time-series disturbance detection (GEE/RS) Huang et al. ( 2010 ); 2020–2025 GUIDOS—GuidosToolbox; GWB—Graph-Based Workflow MSPA expansions; distance/similarity (Jensen–Shannon multiscale) (QGIS/C/GDAL) Vogt & Riitters ( 2017 ); Vogt et al. ( 2022 ); Dutt et al., ( 2024 ) FAD–FOS pipelines Fixed-scale density classes; policy-scale comparability Vogt & Caudullo ( 2025 ) Patternbits; geodiv; Intra Config elements & KL; gradient surface metrics; CWA intra-patch connectivity (R) Remmel ( 2020 , 2022 ); Justeau-Allaire et al. ( 2024 ) VecLI; VARLI; LDTtool/LDT4QGIS Vector indices; composition/configuration change typologies; perimeter-area fixes (Py/QGIS) Yao et al. ( 2022 ); Huang et al. ( 2024 ) flsgen Neutral landscapes with controlled fragmentation (API/R/CLI) Justeau-Allaire et al. ( 2022 ) ENVI + GeoDa; MapBiomas + IDRISI (FFCI) PCA/ANN/CA–MC composite fragmentation; forecasting (RS) Lin et al. ( 2024 ); Moreira et al. ( 2024 ); Wu et al. ( 2024 ) Fiji/ImageJ2 + ComsystanJ 3-D voxel fragmentation; fractal dim; succolarity (3-D) Andronache ( 2024 ) ESIS/Imalys; AMAPVox Hybrid PMM–GM; TLS-PAI; phenology impacts (3-D/TLS) Selsam et al. ( 2024 ); Nunes et al. ( 2022 ) ProNet scripts PA-network connectivity metric; simple bounded index (Py/R) Theobald et al. ( 2022 ) LandTrendr (apps) Recent provincial apps to 2025 (feeder in practice) Qiu et al. ( 2025 ) 3.3 Change-Detection Methods Change detection supplies the time-stamped events that feed downstream fragmentation indicators; contemporary practice matches the detector family to the disturbance regime (abrupt vs. gradual) and documents compositing/parameter choices. Trajectory segmentation Vegetation Change Tracker (VCT) formalizes long Landsat histories with strict filtering, and LandTrendr fits piecewise trends to locate breakpoints and recovery segments at scale on cloud platforms (Huang et al., 2010 ; Kennedy et al., 2018 ). These approaches work best when long, relatively clean series are available and when both loss and recovery matter. Tri-date detectors When time series are sparse or disturbances are short and sharp, calibrated three-date methods perform well. The Two-Thresholds Method (TTM) applies paired loss and recovery thresholds on ΔNBR (delta Normalized Burn Ratio), and 3I3D uses Sentinel-2 vector angles and magnitudes to flag clear-cuts with minimal tuning (Giannetti et al., 2020 ; Francini et al., 2021 ). Continuous/harmonic models Continuous Change Detection and Classification (CCDC) models seasonal cycles and longer-term trends, capturing gradual or compound deviations that step/tri-date detectors may miss; implementations in Google Earth Engine enable regional coverage (Gorelick et al., 2017 ; Mahapatra et al., 2025 ). Across all families, data handling choices strongly shape outputs. Compositing strategies (e.g., Best Available Pixel vs. medoid) trade noise suppression against radiometric consistency and day-of-year proximity, which can shift estimated break timing and raise edge-adjacent false positives if not reported (Francini et al., 2023 ). Sensor stacks have moved from Landsat-only to Landsat + Sentinel-2, with commercial very-high-resolution small-sat constellations (e.g., Dove/Skysat) used selectively for fine-scale confirmation and lidar for canopy structure/validation (Zald et al., 2016 ; Nunes et al., 2022 ). Global baselines such as Global Forest Change provide standardized context but require local checks for omission/commission—especially in coppice, selective logging, and fire landscapes (Hansen et al., 2013 ). Recent provincial deployments (e.g., Guangdong) route detector outputs directly into fragmentation indices and driver analyses (Qiu et al., 2025 ). 3.4 Software and Reproducibility The earliest implementations of fragmentation metrics were deeply tied to GIS workstations. In the Cascade Range, Ripple et al. ( 1991 ) used the pMAP GIS to introduce GISfrag, one of the first spatially explicit fragmentation indices, combining proximity mapping with edge removal to estimate interior habitat. By the late 1990s, ArcView GIS linked directly to FRAGSTATS outputs, allowing stand attributes to be translated into spatial metrics in boreal systems (Vernier & Cumming, 1999 ). National-scale studies soon followed: Heilman et al. ( 2002 ) integrated FRAGSTATS with ArcGIS and TIGER road data to derive intactness scores, while Wang ( 2002 ) prototyped ArcIMS as an early web-based GIS for fragmentation services. Continuous and discrete classifications were also tested in Western Honduras, where Southworth et al. ( 2004 ) combined FRAGSTATS with local indicators of spatial association in GIS, showing how socioeconomic context shaped patterns. Remote sensing platforms were integrated next: Lung and Schaab ( 2006 ) paired ERDAS IMAGINE time-series clustering with moving-window GIS metrics in Kenyan rainforests, and Maier et al. ( 2006 ) combined airborne laser scanning with object-based segmentation in eCognition and ArcGIS to relate canopy structure to fragmentation indices. At continental scales, Wickham et al. ( 2008 ) advanced multi-scale forest density mapping using GIS-based moving windows on NLCD data, highlighting scale sensitivity. Legacy metric calculators such as FRAGSTATS and Patch Analyst codified patch/class metrics and seeded reproducibility by standardizing formulas (McGarigal & Marks, 1995 ; Elkie et al., 1999 ). The GuidosToolbox lineage expanded role-based morphology (MSPA) and fixed-scale density (FAD), making edge, core, and corridor classes directly comparable across regions (Vogt et al., 2007 ; Vogt & Riitters, 2017 ; Vogt et al., 2022 ). In India’s Western Ghats, Ramachandra, Setturu, and Chandran ( 2016 ) applied FRAGSTATS with Riitters’ indices to quantify biodiversity-rich fragmentation, illustrating how classical software remained embedded in regional GIS workflows. Predictive modeling extended this further, with IDRISI ’s CA–Markov used alongside FRAGSTATS to forecast degradation trajectories (Malhi et al., 2020 ). Since ~ 2018, open ecosystems in R and Python have standardized reproducible workflows. landscapemetrics and motif embed FRAGSTATS-style indices in tidy pipelines, while PyLandStats , LecoS , and ShrinkShape2 extend analysis into Python/QGIS contexts and provide rotation-robust shape descriptors (Hesselbarth et al., 2019 ; Nowosad, 2021 ; Bosch, 2019 ). Vector-native frameworks (e.g., VecLI ) and raster–vector integrators (e.g., VARLI ) mitigate biases from rasterization, and connectivity platforms such as Conefor and Circuitscape now link directly to morphology roles. General-purpose GIS platforms have become orchestration hubs: QGIS (with Processing , GRASS GIS , and SAGA ), ArcGIS Pro (via ModelBuilder and ArcPy ), and companion spatial-statistical software such as GeoDa allow analysts to integrate patch metrics, network measures, and machine-learning scripts within auditable environments. Increasingly, these analyses are distributed through cloud infrastructures like Google Earth Engine , which couples detectors to downstream metrics while preserving reproducible logs. Reproducibility now extends beyond tool choice to parameter transparency. Analysts increasingly report grain, extent, edge width, compositing policy, and detector settings, and share code or notebooks alongside outputs. This mitigates reporting inconsistency (L5 in Fig. 4) by making studies portable and comparable, while enabling sensitivity checks—such as varying window sizes or compositing rules—without re-engineering full workflows (Marchesan et al., 2018 ; Yao et al., 2022 ; Huang et al., 2024 ; Munhoz et al., 2025 ). Overall, the trajectory has been from workstation calculators to documented, interoperable pipelines that allow independent verification and cross-study synthesis. 3.5 Advances in New Methods (post-2016) This subsection emphasizes methodological expansions since 2016, grouped into arcs that show how capabilities accreted. 2016–2019: from 2-D patterns to structure and information. The first shift was explicit incorporation of vertical structure. Lidar-based methods captured canopy height and porosity, reframing connectivity as three-dimensional rather than planar (Zald et al., 2016 ; Remmel, 2018 ). In parallel, information-theoretic approaches gained ground: Nowosad and Stepinski ( 2019 ) described landscapes as configuration distributions rather than lists of indices, and Remmel ( 2020 ) formalized hyper-local configuration elements for pattern diagnostics. During this period, GIS platforms still anchored workflows, with Ramachandra et al. ( 2016 ) using FRAGSTATS within ArcGIS and PCA environments to analyze forest hotspots in India. 2020–2022: connectivity inside patches and networks between them. Connectivity refinements unfolded at multiple scales. Within patches, Complexity-Weighted Patch Area (CWA) and related formulations weighted area by form/roughness, capturing intra-patch navigability in ways comparable to classical graph indices (Justeau-Allaire et al., 2024 ). At broader scales, ProNet provided a bounded, report-ready index for protected-area systems (Theobald et al., 2022 ). Representation also matured: vector-native indices ( VecLI ) reduced raster biases, and GuidosToolbox/Workbench introduced multiscale distance–similarity operators that integrate directly with MSPA roles (Vogt, 2015 ; Yao et al., 2022 ; Vogt et al., 2022 ). Integration with GIS remained central, with predictive CA–Markov modeling in IDRISI tied to FRAGSTATS outputs for long-term forecasts (Malhi et al., 2020 ). 2023–2025: composites, edges, density at policy scale, and controlled experiments. Recent years have seen consolidation across pattern, trajectory, and driver domains. Composite indices such as the Forest Fragmentation Comprehensive Index (FFCI) combine spectral change, configuration, and context to separate loss from recovery (Wu et al., 2024 ; Lin et al., 2024 ). The Forest Edge Index (FEI) standardizes edge-centric states for driver analyses, and the Multiscale Similarity Index (MSI) applies Jensen–Shannon similarity to benchmark observed mosaics against fully forested references (Zhen et al., 2023 ; Netzel et al., 2024 ). At the policy scale, FAD–FOS pipelines have matured into tools for inter-regional comparability with explicit spatial supports (Vogt & Caudullo, 2025 ). Methodological frameworks have also tightened raster–vector integration ( VARLI ) and coupled detailed indicators with machine learning to attribute processes (Huang et al., 2024 ; Lin et al., 2024 ). Where time series underpin inference, operational feeders such as provincial LandTrendr applications now pipe disturbance segments directly into fragmentation indicators, and sensitivity tests quantify how fixed-scale choices affect outcomes—relevant to scale sensitivity (L1 in Fig. 4) (Qiu et al., 2025 ; Zhang et al., 2025 ). Neutral generators such as flsgen permit stress-testing of metrics under controlled fragmentation mosaics before transfer to real landscapes (Justeau-Allaire et al., 2022 ). Recent case studies also demonstrate tighter GIS integration: Zhang et al. ( 2024 ) combined GuidosToolbox , Conefor , and ArcGIS to construct ecological security patterns; Lin et al. ( 2024 ) fused FRAGSTATS , ENVI , and GeoDa with machine learning in R; and Netzel et al. ( 2024 ) used GDAL/OGR and custom C code to implement MSI. Together these highlight how GIS platforms are not superseded but remain the backbone environments in which fragmentation innovations are operationalized. 4. Discussion — overview Across 138 studies, forest-fragmentation methods progress from patch tallies to role-aware, connectivity-explicit, and increasingly 3-D representations. We interpret this trajectory through five recurring limitations (L1–L5) that affect transferability: L1 —scale sensitivity and habitat-amount conflation; L2 —region-specific thresholds and assumptions; L3 —weak empirical validation (especially for global products); L4 —limited linkage to biological responses; and L5 —incomplete parameter reporting. Figure 3 is a schematic of these limitations; evidence and implications appear below. 4.1 Tracing the evolution of metric suitability Ripple et al. ( 1991 ) showed early on that GIS-derived indices could reveal fragmentation trajectories, and the FRAGSTATS era formalized patch, edge, shape, and core metrics for reproducible mapping (McGarigal & Marks, 1995 ; Walker & Kenkel, 1998 ; Remmel & Csillag, 2003 ; Sun & Southworth, 2013 ). The central weakness— L1 —is that many indices vary with grain and extent, so differences can reflect pixel size or window choice rather than ecological change (O’Neill et al., 1999 ; Long et al., 2010; Pe’er et al., 2013 ). Remmel ( 2009 ) explains part of the mechanism: coincidence matrices summarize composition well but capture little about configuration unless augmented, making composition–configuration conflation likely when one class dominates. MSPA reframed maps into roles—cores, edges, bridges, corridors, perforations—useful at reporting-unit and continental scales (Vogt et al., 2007 ; Estreguil & Mouton, 2009 ; Wickham et al., 2010 ). Connectivity metrics followed. Roberts et al. ( 2000 ) and later Lin et al. ( 2021 ) and Theobald et al. ( 2022 ) translated dispersal and resistance assumptions into graph-based indicators (PC, IIC, dI; ProNet ) with clearer decision relevance, while surfacing L2 (context dependence of species/guild parameters) and L4 (the gap to observed responses). Recent families aim to curb conflation and tighten links to process. Fixed-scale density (FAD/FOS) declares spatial support up front, stabilizing inter-regional comparisons (Vogt & Caudullo, 2025 ). Information-theoretic and local-connectedness measures separate form complexity from neighborhood linkage (Peptenatu et al., 2023 ; Alage et al., 2025 ). And voxel/3-D approaches bring canopy permeability and edge penetration into scope, advancing structure–function hypotheses but raising data and validation demands (Remmel, 2020 , 2022 ). Across these arcs, results travel best when spatial support, thresholds, and connectivity parameters are stated and stress-tested; otherwise, method settings masquerade as ecological differences (Wang et al., 2012 ; Fahrig, 2019 ; Nunes et al., 2022 ; Zhang et al., 2025 ). 4.2 Transforming accessibility through open-source, GIS, and cloud ecosystems Open, scriptable ecosystems have turned isolated metric runs into auditable pipelines. In R, landscapemetrics and motif expose FRAGSTATS-style indices within reproducible workflows; in Python, PyLandStats and LecoS fill a similar role; and GuidosToolbox with its Graph-based Workflow Builder scales role-based morphology for large reporting units (Hesselbarth et al., 2019 ; Nowosad, 2021 ; Vogt & Riitters, 2017 ; Vogt et al., 2022 ). General-purpose GIS— QGIS (with Processing , GRASS GIS , SAGA ) and ArcGIS Pro (ModelBuilder, ArcPy )—now acts as the orchestration layer where parameters are modeled, batched, and versioned, while GeoDa provides spatial-autocorrelation diagnostics. Google Earth Engine has democratized compositing and change detection at scale without bespoke infrastructure (Wulder et al., 2008 ; Coops et al., 2010 ; Gorelick et al., 2017 ). Two practice gaps persist. First, L5 : key parameters are too often omitted—grain, extent, edge rules, windowing, compositing policy, detector settings—with our screen suggesting roughly a sixth of papers miss at least one (Hernando et al., 2017 ; Zatelli et al., 2019 ). Second, L3 : reliance on global products without local checks (e.g., Global Forest Change ) risks omission/commission errors in selective logging, coppice, and fire mosaics (Hansen et al., 2013 ; Nunes et al., 2022 ). Addressing both rarely requires new software; it requires concise parameter logs and validation notes attached to each map product. Ramachandra et al. ( 2016 ) offer a good template, combining FRAGSTATS and PCA in ArcGIS to surface regional drivers in India’s Western Ghats. Machine learning extends these pipelines from description to attribution. Zanella et al. ( 2017 ) and Zhen et al. ( 2023 ) demonstrate how Random Forest applied to PD, LPI, Division, or FFCI composites can illuminate drivers, while Moreira et al. ( 2024 ) and Lin et al. ( 2024 ) show forward scenarios via ANN or CA-Markov. The same features that add explanatory power raise the bar for transparency: credible ML use reports features and neighborhoods, data partitioning and cross-validation, model settings and interpretability steps, and—crucially—external or hold-out checks (Hansen et al., 2013 ; Hernando et al., 2017 ; Zatelli et al., 2019 ). 4.3 Elevating fidelity with advanced data sources Multi-sensor regimes sharpen structural detection and help separate composition from configuration (Maier et al., 2006 ; Long et al., 2010; Zald et al., 2016 ; Mshelia et al., 2022 ). Airborne and terrestrial laser scanning reveal vertical heterogeneity that 2-D indicators miss—central for permeability and microclimate—while detector families ( VCT , LandTrendr , TTM , tri-temporal Sentinel-2) stabilize disturbance trajectories before indicators are computed (Huang et al., 2010 ; Kennedy et al., 2018 ; Giannetti et al., 2020 ; Francini et al., 2021 , 2023 ). These gains amplify familiar trade-offs: finer spatial and temporal support heightens L1 sensitivities and can invite over-interpretation without ecological corroboration (Ostapowicz et al., 2008 ; Fahrig, 2024 ). Voxel/3-D formulations promise tighter links to structure and biomass but are data-hungry and demand stronger validation beyond instrumented sites (Remmel, 2022 ; Mazziotta et al., 2025 ). In practice, high-fidelity inputs work best when paired with explicit parameter disclosure and targeted field or higher-resolution checks. 4.4 Bridging research and practice: targeted fixes for L1–L5 Validation first (addresses L3, L4). Mac Nally ( 2008 ) argued for design-based estimation when mapping prevalence; following that guidance, fragmentation workflows should link indicators to field plots, biodiversity proxies, or independent structure data and report design-based or model-assisted area estimates where feasible (Nunes et al., 2022 ). Standardized reporting (addresses L1, L5). Rather than rely on defaults, specify the spatial support and thresholds that govern outputs: grain, extent, edge width, and window size for rolling measures (for FAD, stabilization typically occurs at tens to low hundreds of pixels in dissected landscapes: Zatelli et al., 2019 ; Zhang et al., 2025 ); forest definitions and MMU (e.g., FAO/HRL-FTY; Vogt & Caudullo, 2025 ); compositing policy (Best Available Pixel vs. medoid, target phenology, sensor priority, despiking; Francini et al., 2023 ); and detector settings— VCT masking/IFZ, LandTrendr segmentation/recovery, TTM cross-validation, tri-temporal Sentinel-2 cut-offs (Huang et al., 2010 ; Kennedy et al., 2018 ; Giannetti et al., 2020 ; Francini et al., 2021 ). Region-tuned implementations (addresses L2). Calibrate thresholds, windows, and resistance/dispersal assumptions to local disturbance regimes and canopy architecture so metrics reflect regional realities rather than imported defaults (Geri et al., 2010 ; Rosa et al., 2017 ; Kozak et al., 2018 ; Osewe et al., 2022 ). Ramachandra et al. ( 2016 ) exemplify this tuning in a biodiversity hotspot. Multi-scale integration (addresses L1, L4). Combine complementary families to reduce conflation and expose process: pair MSPA roles with graph metrics for movement potential; deploy fixed-window FAD–FOS for policy comparability; use INCOMA/gradient surfaces for heterogeneous mosaics; and add voxel morphology where vertical connectivity matters (Nowosad & Stepinski, 2021 ; Remmel, 2022 ; Vogt & Caudullo, 2025 ). Open, cloud-based reproducibility (enables L1–L5). Share R/Python/GEE workflows— landscapemetrics , motif, geodiv , VecLI/VARLI , LDT4QGIS —so parameters are visible, versioned, and re-runnable, making sensitivity checks straightforward and enabling like-for-like comparisons (Mairota et al., 2013 ; Hesselbarth et al., 2019 , 2021 ; Yao et al., 2022 ; Smith et al., 2021 ; Paixão & Machado, 2023 ). 5. Summary statements Across three decades and 138 studies, fragmentation analysis has shifted from patch/edge tallies to role-aware, connectivity-explicit, and increasingly three-dimensional descriptions. The most useful way to read that shift is as a linked chain—data sources → change detectors → pattern indicators—where choices made upstream condition what indicators can say downstream. Five limitations repeatedly shape inference. Scale sensitivity and habitat-amount dependence remain the main source of comparability problems (L1). Parameters tuned in one region do not travel cleanly to another (L2). Adoption of global or automated layers without local checks is still common (L3). Structural metrics are too rarely tied to biological responses (L4). And key settings—spatial support, edge rules, compositing policy, detector thresholds—are inconsistently reported (L5). What works in practice is incremental rather than novel. Declaring spatial support (e.g., fixed-window density) stabilizes comparisons; pairing role-based morphology with connectivity metrics clarifies movement options; and, where vertical structure matters, targeted lidar/TLS or voxel summaries add needed realism. The common denominator is transparent parameterization with light-weight sensitivity checks, so that apparent differences reflect landscapes rather than hidden settings. Evidence gaps are evident. Validation is often lacking and could benefit from design-aware, independent approaches; methods might be more effective when tuned to regional disturbance regimes and canopy architecture; and clearer cross-walks between 2-D indicators and 3-D/voxel measures could enhance understanding. Geographic coverage also remains uneven, with several tropical regions under-represented. Looking ahead, future progress may hinge less on new indices and more on refining existing ones through precise specification and validation. Key considerations include: (i) documenting spatial support and detector settings as metadata, (ii) exploring region-balanced benchmarking and neutral-landscape challenges to assess sensitivity, (iii) developing simple, shareable workflows for inspection, and (iv) integrating field or high-resolution data where possible. Adopting these approaches could strengthen fragmentation measures’ reliability for ecological interpretation, monitoring, and decision-making amid accelerating habitat change. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed specific sections to the manuscript and assisted with critical revisions. Sanjana Dutt led the conceptualization, systematic review, and overall coordination of the manuscript, including preparation of the first and revised drafts. Tarmo Remmel, Carlos Rivas, Adriano Mazziotta and Mieczysław Kunz contributed to review refinement and provided input on structure, interpretation, and revisions. All authors read and approved the final version of the manuscript. Use of Generative AI During the preparation of this work, Sanjana Dutt used OpenAI’s ChatGPT and xAI’s Grok to assist with language editing and structural refinement of the manuscript. Napkin AI was used to assist with preliminary visualization design for one figure. All AI-generated content was reviewed, edited, and finalized by the author, who takes full responsibility for the integrity and accuracy of the presented work. Data Availability No new data were generated or analyzed in this study. All information is based on published literature cited within the manuscript. References Adamczyk, J., & Tiede, D. (2017) ZonalMetrics—a Python toolbox for zonal landscape structure analysis. Computers & Geosciences, 99, 91–99. https://doi.org/10.1016/j.cageo.2016.11.005 Alage, I. L., Tan, Y., Akande, A. W., Olugbenga, H. J., Suprijanto, A., & Lodhi, M. K. (2025). Fractal Metrics and Connectivity Analysis for Forest and Deforestation Fragmentation Dynamics. 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1","display":"","copyAsset":false,"role":"figure","size":189068,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram for a systematic review of methods to assess forest fragmentation (1990–2025).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7684385/v1/4e4c421f4283fa89b4132c04.png"},{"id":93777198,"identity":"71452e05-62a8-431a-99cd-f7b0ec84ba46","added_by":"auto","created_at":"2025-10-17 12:39:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153225,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of forest-fragmentation methods (1990–2025). Rows depict the chain from data sources (bottom) → change-detection methods (middle) → landscape pattern indicators (top). Columns group eras by sensor availability and resolution, illustrating the transition from early single-sensor paradigms to multi-sensor integrations (including lidar) that enhance change detection and indicator robustness.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7684385/v1/bf25083a31ed674fcb2a3fc9.jpeg"},{"id":93775475,"identity":"1c29c0dc-911a-4168-a97f-d454108bdf8f","added_by":"auto","created_at":"2025-10-17 12:31:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1125219,"visible":true,"origin":"","legend":"\u003cp\u003eVisual summary of five limitations (\u003cstrong\u003eL1–L5\u003c/strong\u003e) in forest-fragmentation methods: L1 scale sensitivities; L2 regional parameterization; L3 validation gaps; L4 metric–biology linkage; L5 reporting inconsistency. Interpretation is developed in 4.1–4.4.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7684385/v1/31f24b0107aa334211e01dcb.png"},{"id":108437687,"identity":"9ed9db27-cd47-4f2f-a66a-152fcdab3d8f","added_by":"auto","created_at":"2026-05-04 16:02:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1945060,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7684385/v1/b10f862d-16fa-4087-81b8-41d4577fb6b6.pdf"},{"id":93775468,"identity":"8bbaa3c9-df79-43ea-a8b1-3e3fe7a91298","added_by":"auto","created_at":"2025-10-17 12:31:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":24703,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource1SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7684385/v1/17a22b89da0bf69fd553d3ed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing Forest Fragmentation Analysis: A Systematic Review of Evolving Spatial Metrics, Software Platforms, and Remote Sensing Innovations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForest fragmentation—the division of continuous forest into smaller, more isolated patches—creates edge environments, reshapes ecological processes, and can accelerate biodiversity loss (McGarigal \u0026amp; Marks, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Heilman et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Riitters et al., 2007; Bennett \u0026amp; Radford, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Over two-thirds of global forests now lie within 1 km of an edge (Haddad et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Siegel et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with pressures most pronounced in tropical and subtropical regions (Lung \u0026amp; Schaab, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Giriraj et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Fragmentation is distinct from habitat loss: loss reduces area, whereas fragmentation concerns how a fixed amount of forest is arranged by patch size, shape, and isolation (Fahrig, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fardila et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Connectivity—the degree to which landscape structure facilitates or restricts movement—adds a further interpretive layer (Bogaert et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Vogt et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lausch et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although patch-scale edge effects are well documented, landscape-scale responses do not always follow directly from local patterns (Fletcher et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fahrig, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), underscoring the need for scale-declared, method-transparent assessments that can be linked to ecological data where available (Bennett \u0026amp; Radford, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMethodologically, practice has progressed from early patch/edge/shape tallies to morphology- and connectivity-aware approaches, fixed-window density measures, and first-generation 3-D/voxel indicators. FRAGSTATS and Patch Analyst established a common language for pattern quantification (McGarigal \u0026amp; Marks, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Elkie et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Role-based morphology—exemplified by morphological spatial pattern analysis (MSPA)—made cores, edges, bridges, corridors, and perforations legible at reporting scales (Vogt et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wickham et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Advances in remote sensing, including lidar/TLS for canopy structure, together with robust time-series change detection (e.g., Vegetation Change Tracker, LandTrendr, Two-Thresholds Method), have enabled consistent disturbance trajectories that feed downstream indicators (Maier et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zald et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kennedy et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Giannetti et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Open, scriptable ecosystems and cloud platforms—\u003cem\u003elandscapemetrics\u003c/em\u003e, PyLandStats, GuidosToolbox, and Google Earth Engine—now support auditable pipelines from data ingestion to indicators. In parallel, neutral landscape generators (e.g., Landscape Generator; flsgen) provide realistic, controlled mosaics to test sensitivity and to separate composition from configuration (van Strien et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Peura et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Justeau-Allaire et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite this expansion in capability, five recurring issues complicate inference and comparability across studies: (i) sensitivity to grain and extent and the attendant conflation with habitat amount; (ii) regional dependence of thresholds and assumptions; (iii) gaps in external/empirical validation—especially for global products and emergent 3-D indicators; (iv) loose coupling to biological responses; and (v) incomplete parameter reporting (O’Neill et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hernando et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zatelli et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fletcher et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vergara et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Feleha et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent families—fixed-window density measures such as Forest Area Density (FAD), role-based morphology paired with graph metrics, and voxel/3-D approaches—address parts of this problem yet introduce assumptions that must be declared and tested.\u003c/p\u003e\u003cp\u003eThis review analyzes 138 methodological studies (1990–2025) to:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003emap the evolution from patch-based measures to connectivity, shape complexity, fixed-window density, and emerging 3-D approaches;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eevaluate how remote sensing and time-series/change-detection methods—together with compositing/segmentation choices and cloud platforms—condition the accuracy and comparability of fragmentation indicators; and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ediagnose common limitations and summarize practical reporting elements (e.g., grain/extent, edge width, window size, detector settings, MMU, validation) that make results more portable across regions and scales.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003ch2\u003e2.1 Literature search\u003c/h2\u003e\u003cp\u003eWe followed PRISMA 2020 (Page et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to ensure a transparent, reproducible process. We targeted peer-reviewed journal articles published 1990–2025 and ran searches from 3 Oct 2024 to 9 Sep 2025 using Publish or Perish (Harzing, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) across Google Scholar, Scopus, and Web of Science. We limited results to English and article document types; conference papers, theses, books/chapters, and reports were excluded. Studies were included if they proposed, evaluated, or systematically applied methods/metrics/workflows for forest fragmentation (e.g., structural/configurational metrics, change-detection feeding fragmentation indicators, graph/MSPA/fixed-window/3-D approaches), and excluded if purely ecological case studies without methodological contribution or non-forest contexts unless methods were demonstrated for forests. To keep scope forest-specific we filtered out terms such as “urban,” “animal*,” and “bird*” unless paired with “forest.” Database searches returned 1,160 records; Litmaps/Connected Papers, citation chasing, and colleague recommendations added 46, for 1,206 records prior to deduplication. Title/abstract and full-text screening applied the criteria above; reasons for exclusion are shown in PRISMA Fig.\u0026nbsp;1, and verbatim database search strings are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.2 Screening Process\u003c/h2\u003e\u003cp\u003eWe removed 313 duplicate records using Rayyan (Ouzzani et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), leaving 847 unique articles. Title/abstract/keyword screening excluded 233 items without a methodological focus, 152 outside a forest context, and 251 that reported ecological impacts only without a fragmentation-methods component. We sought 211 full texts; 5 could not be retrieved, leaving 206 for full-text assessment. In parallel, we identified 46 additional records via reference tracking (Litmaps, Connected Papers) and colleague recommendations, which were included at the full-text stage.\u003c/p\u003e\u003ch2\u003e2.3 Study Selection\u003c/h2\u003e\u003cp\u003eFull texts were assessed against predefined criteria: (i) proposes, evaluates, or systematically applies methods to measure forest fragmentation (spatial/configurational metrics, remote-sensing/change-detection feeding fragmentation indicators, graph/MSPA/fixed-window/3-D approaches); (ii) forest context or forest-reporting subset; (iii) peer-reviewed journal article; (iv) publication 1990–2025; (v) English, full text available; and (vi) unique contribution.\u003c/p\u003e\u003cp\u003eOf the 206 database-sourced articles, 96 were excluded: 24 background/narrative, 28 not methodological, 10 not forest-related, 17 redundant (repetitive case applications of standard landscape-metrics workflows—e.g., FRAGSTATS or Patch Analyst—without methodological novelty or evaluation), and 17 other reasons (insufficient methodological detail or peripheral scope).\u003c/p\u003e\u003cp\u003eOf the 46 additional records, 12 were excluded at full text (5 impact-focused only, 5 outside scope/publication window, 2 background). The final set comprised 138 methodological studies: 110 from databases and 34 from other sources (see PRISMA Fig.\u0026nbsp;1).\u003c/p\u003e\u003ch2\u003e2.4 Limitations\u003c/h2\u003e\u003cp\u003eThis review is limited to English-language, peer-reviewed journal articles. While this may omit some regional contributions, English dominates publication in this domain, and citation chasing in our included papers did not repeatedly surface non-English methodological keystones. Our search emphasized method/tool terms (e.g., FRAGSTATS, GuidosToolbox) to capture studies that develop or evaluate fragmentation methods; broader ecological terms would have inflated returns with impact-only studies and diluted the methodological focus. This emphasis could favor papers that explicitly name software; we countered it through snowballing (Litmaps, Connected Papers) and colleague recommendations. By design, we prioritized methods-focused work and excluded descriptive case studies that simply reuse existing metrics, which limits ecological context but preserves a clear methods synthesis. Finally, full-text screening was conducted by one reviewer using predefined criteria and Rayyan, with co-author oversight at inclusion; this does not remove selection bias entirely, but it provides a consistent and auditable screen.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Scope and Organization\u003c/h2\u003e\u003cp\u003eThis review synthesizes 138 studies (1990\u0026ndash;2025) on methodological approaches to quantifying forest fragmentation. We organize findings along three linked components: (i) data sources (remote-sensing and ground-based inputs), (ii) change-detection methods (time-series analyses that derive disturbance/transition signals), and (iii) landscape pattern indicators (metrics of configuration and connectivity). In practice, data sources feed change detection, which then supports indicator calculation (Fig.\u0026nbsp;2). The columns in Fig.\u0026nbsp;2 group periods by sensor-era shifts, showing a progression from early, primarily Landsat-based work to multi-sensor approaches that integrate increasingly higher spatial and temporal resolution sensors, including lidar, to improve temporal fidelity and structural sensitivity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Evolution of Landscape Pattern Indicators\u003c/h2\u003e\u003cp\u003eInitial investigations in the 1990s emphasized two-dimensional assessments of forest patches, including patch counts, area metrics, edge lengths, and core area delineations (Ripple et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; McGarigal \u0026amp; Marks, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Jorge \u0026amp; Garcia, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Walker \u0026amp; Kenkel, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). These methodologies, while systematically replicable, offered limited insight into ecological connectivity or species dispersal potential. By the early 2000s, the introduction of effective mesh size provided a more nuanced metric for evaluating landscape subdivision (Jaeger, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Subsequent developments around 2007 incorporated role-based classifications\u0026mdash;such as core areas, edge zones, and connective bridges\u0026mdash;enhancing the spatial resolution of forest network analyses (Vogt et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tejaswi, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePost-2010, connectivity-focused indicators, including Probability of Connectivity (PC) and Integral Index of Connectivity (IIC), gained prominence, featuring in approximately 25% of studies, particularly for informing wildlife corridor design (Estreguil \u0026amp; Mouton, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ye et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ramezani \u0026amp; Ramezani, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent advancements have diversified into three categories: (1) fixed-scale density metrics, exemplified by Forest Area Density (FAD), enabling inter-regional comparability (Vogt \u0026amp; Caudullo, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); (2) shape and connectivity indices, such as Forest Fragmentation Index (FFI) and Local Connectedness (LCFD), which account for patch morphology and local linkages while mitigating data inaccuracies (Alage et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); and (3) three-dimensional assessments leveraging lidar to elucidate canopy structure and vertical connectivity, addressing limitations of planar analyses (Zald et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nowosad \u0026amp; Stepinski, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Remmel, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhen et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A comprehensive overview of these tools is presented in Table\u0026nbsp;1 and Supplementary Table S2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvolution of toolsets for forest fragmentation\u0026mdash;concise view. Grouped by period, showing key tools, their contributions, and tags. Full details in Supplementary Table S2.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExemplary toolsets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhat it adds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKey refs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePre-2000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFRAGSTATS; Patch Analyst; pMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBaseline patch/class metrics (area, edge, shape, core); early proximity/contagion (RS/GIS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMcGarigal \u0026amp; Marks (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); Elkie et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhoros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimulated patterns; metric correlation; early eco-response tests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHargis et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e2000\u0026ndash;2009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGUIDOS/APACK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMSPA roles; moving-window (FAD/entropy); continental mapping (QGIS/Prog)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVogt et al. (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); Wulder et al. (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); Soverel et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eERDAS/IDRISI/eCognition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eobject-based image analysis \u003cb\u003e(\u003c/b\u003eOBIA\u003cb\u003e)\u003c/b\u003e segmentation; Lidar-derived metrics; early CA\u0026ndash;Markov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaier et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); Meddens et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArcIMS\u0026thinsp;+\u0026thinsp;FRAGSTATS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeb-mapping\u0026thinsp;+\u0026thinsp;classical metrics (Web-GIS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWang (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); Southworth et al. (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003e2010\u0026ndash;2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLFT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCore/edge/perforated/patch; morph segmentation (ArcGIS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKopeck\u0026aacute; \u0026amp; Nov\u0026aacute;ček (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e);\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConefor\u0026thinsp;+\u0026thinsp;Circuitscape/Linkage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConnectivity (PC/IIC/dI; circuit/least-cost corridors) (Graph/Circuit)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSaura \u0026amp; Torn\u0026eacute; (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); McRae et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elandscapemetrics; motif; PyLandStats; LecoS; ShrinkShape2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReproducible pipelines; pattern signatures; rotation-invariant shape (R/QGIS/Py)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHesselbarth et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Remmel (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolyFrag; FRAGSTATS v3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVector-aware metrics; custom edge width (GIS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMacLean \u0026amp; Congalton (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeeders: VCT; LandTrendr; CCDC; TTM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStable time-series disturbance detection (GEE/RS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHuang et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e);\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003e\u003cb\u003e2020\u0026ndash;2025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGUIDOS\u0026mdash;GuidosToolbox; GWB\u0026mdash;Graph-Based Workflow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMSPA expansions; distance/similarity (Jensen\u0026ndash;Shannon multiscale) (QGIS/C/GDAL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVogt \u0026amp; Riitters (\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Vogt et al. (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Dutt et al., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFAD\u0026ndash;FOS pipelines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFixed-scale density classes; policy-scale comparability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVogt \u0026amp; Caudullo (\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatternbits; geodiv; Intra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConfig elements \u0026amp; KL; gradient surface metrics; CWA intra-patch connectivity (R)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRemmel (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Justeau-Allaire et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVecLI; VARLI; LDTtool/LDT4QGIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVector indices; composition/configuration change typologies; perimeter-area fixes (Py/QGIS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYao et al. (\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Huang et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eflsgen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeutral landscapes with controlled fragmentation (API/R/CLI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJusteau-Allaire et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eENVI\u0026thinsp;+\u0026thinsp;GeoDa; MapBiomas\u0026thinsp;+\u0026thinsp;IDRISI (FFCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCA/ANN/CA\u0026ndash;MC composite fragmentation; forecasting (RS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLin et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Moreira et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Wu et al. (\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFiji/ImageJ2\u0026thinsp;+\u0026thinsp;ComsystanJ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3-D voxel fragmentation; fractal dim; succolarity (3-D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAndronache (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESIS/Imalys; AMAPVox\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHybrid PMM\u0026ndash;GM; TLS-PAI; phenology impacts (3-D/TLS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSelsam et al. (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Nunes et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProNet scripts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePA-network connectivity metric; simple bounded index (Py/R)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTheobald et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandTrendr (apps)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecent provincial apps to 2025 (feeder in practice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQiu et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Change-Detection Methods\u003c/h2\u003e\u003cp\u003eChange detection supplies the time-stamped events that feed downstream fragmentation indicators; contemporary practice matches the detector family to the disturbance regime (abrupt vs. gradual) and documents compositing/parameter choices.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrajectory segmentation\u003c/strong\u003e\u003cp\u003e\u003cem\u003eVegetation Change Tracker (VCT)\u003c/em\u003e formalizes long Landsat histories with strict filtering, and \u003cem\u003eLandTrendr\u003c/em\u003e fits piecewise trends to locate breakpoints and recovery segments at scale on cloud platforms (Huang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kennedy et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These approaches work best when long, relatively clean series are available and when both loss and recovery matter.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTri-date detectors\u003c/strong\u003e\u003cp\u003eWhen time series are sparse or disturbances are short and sharp, calibrated three-date methods perform well. The \u003cem\u003eTwo-Thresholds Method (TTM)\u003c/em\u003e applies paired loss and recovery thresholds on ΔNBR (delta Normalized Burn Ratio), and \u003cem\u003e3I3D\u003c/em\u003e uses Sentinel-2 vector angles and magnitudes to flag clear-cuts with minimal tuning (Giannetti et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Francini et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eContinuous/harmonic models\u003c/strong\u003e\u003cp\u003e\u003cem\u003eContinuous Change Detection and Classification (CCDC)\u003c/em\u003e models seasonal cycles and longer-term trends, capturing gradual or compound deviations that step/tri-date detectors may miss; implementations in \u003cem\u003eGoogle Earth Engine\u003c/em\u003e enable regional coverage (Gorelick et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mahapatra et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAcross all families, data handling choices strongly shape outputs. Compositing strategies (e.g., Best Available Pixel vs. medoid) trade noise suppression against radiometric consistency and day-of-year proximity, which can shift estimated break timing and raise edge-adjacent false positives if not reported (Francini et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sensor stacks have moved from Landsat-only to Landsat\u0026thinsp;+\u0026thinsp;Sentinel-2, with commercial very-high-resolution small-sat constellations (e.g., Dove/Skysat) used selectively for fine-scale confirmation and lidar for canopy structure/validation (Zald et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nunes et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Global baselines such as Global Forest Change provide standardized context but require local checks for omission/commission\u0026mdash;especially in coppice, selective logging, and fire landscapes (Hansen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Recent provincial deployments (e.g., Guangdong) route detector outputs directly into fragmentation indices and driver analyses (Qiu et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Software and Reproducibility\u003c/h2\u003e\u003cp\u003eThe earliest implementations of fragmentation metrics were deeply tied to GIS workstations. In the Cascade Range, Ripple et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) used the \u003cem\u003epMAP GIS\u003c/em\u003e to introduce GISfrag, one of the first spatially explicit fragmentation indices, combining proximity mapping with edge removal to estimate interior habitat. By the late 1990s, \u003cem\u003eArcView GIS\u003c/em\u003e linked directly to \u003cem\u003eFRAGSTATS\u003c/em\u003e outputs, allowing stand attributes to be translated into spatial metrics in boreal systems (Vernier \u0026amp; Cumming, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). National-scale studies soon followed: Heilman et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) integrated \u003cem\u003eFRAGSTATS\u003c/em\u003e with \u003cem\u003eArcGIS\u003c/em\u003e and TIGER road data to derive intactness scores, while Wang (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) prototyped \u003cem\u003eArcIMS\u003c/em\u003e as an early web-based GIS for fragmentation services. Continuous and discrete classifications were also tested in Western Honduras, where Southworth et al. (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) combined \u003cem\u003eFRAGSTATS\u003c/em\u003e with local indicators of spatial association in GIS, showing how socioeconomic context shaped patterns. Remote sensing platforms were integrated next: Lung and Schaab (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) paired \u003cem\u003eERDAS IMAGINE\u003c/em\u003e time-series clustering with moving-window GIS metrics in Kenyan rainforests, and Maier et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) combined airborne laser scanning with object-based segmentation in \u003cem\u003eeCognition\u003c/em\u003e and \u003cem\u003eArcGIS\u003c/em\u003e to relate canopy structure to fragmentation indices. At continental scales, Wickham et al. (\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) advanced multi-scale forest density mapping using GIS-based moving windows on NLCD data, highlighting scale sensitivity.\u003c/p\u003e\u003cp\u003eLegacy metric calculators such as \u003cem\u003eFRAGSTATS\u003c/em\u003e and \u003cem\u003ePatch Analyst\u003c/em\u003e codified patch/class metrics and seeded reproducibility by standardizing formulas (McGarigal \u0026amp; Marks, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Elkie et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The \u003cem\u003eGuidosToolbox\u003c/em\u003e lineage expanded role-based morphology (MSPA) and fixed-scale density (FAD), making edge, core, and corridor classes directly comparable across regions (Vogt et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Vogt \u0026amp; Riitters, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vogt et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In India\u0026rsquo;s Western Ghats, Ramachandra, Setturu, and Chandran (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) applied \u003cem\u003eFRAGSTATS\u003c/em\u003e with Riitters\u0026rsquo; indices to quantify biodiversity-rich fragmentation, illustrating how classical software remained embedded in regional GIS workflows. Predictive modeling extended this further, with \u003cem\u003eIDRISI\u003c/em\u003e\u0026rsquo;s CA\u0026ndash;Markov used alongside \u003cem\u003eFRAGSTATS\u003c/em\u003e to forecast degradation trajectories (Malhi et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSince ~\u0026thinsp;2018, open ecosystems in R and Python have standardized reproducible workflows. \u003cem\u003elandscapemetrics\u003c/em\u003e and motif embed FRAGSTATS-style indices in tidy pipelines, while \u003cem\u003ePyLandStats\u003c/em\u003e, \u003cem\u003eLecoS\u003c/em\u003e, and \u003cem\u003eShrinkShape2\u003c/em\u003e extend analysis into Python/QGIS contexts and provide rotation-robust shape descriptors (Hesselbarth et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nowosad, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bosch, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Vector-native frameworks (e.g., \u003cem\u003eVecLI\u003c/em\u003e) and raster\u0026ndash;vector integrators (e.g., \u003cem\u003eVARLI\u003c/em\u003e) mitigate biases from rasterization, and connectivity platforms such as \u003cem\u003eConefor\u003c/em\u003e and \u003cem\u003eCircuitscape\u003c/em\u003e now link directly to morphology roles. General-purpose GIS platforms have become orchestration hubs: \u003cem\u003eQGIS\u003c/em\u003e (with \u003cem\u003eProcessing\u003c/em\u003e, \u003cem\u003eGRASS GIS\u003c/em\u003e, and \u003cem\u003eSAGA\u003c/em\u003e), \u003cem\u003eArcGIS Pro\u003c/em\u003e (via ModelBuilder and \u003cem\u003eArcPy\u003c/em\u003e), and companion spatial-statistical software such as \u003cem\u003eGeoDa\u003c/em\u003e allow analysts to integrate patch metrics, network measures, and machine-learning scripts within auditable environments. Increasingly, these analyses are distributed through cloud infrastructures like \u003cem\u003eGoogle Earth Engine\u003c/em\u003e, which couples detectors to downstream metrics while preserving reproducible logs.\u003c/p\u003e\u003cp\u003eReproducibility now extends beyond tool choice to parameter transparency. Analysts increasingly report grain, extent, edge width, compositing policy, and detector settings, and share code or notebooks alongside outputs. This mitigates reporting inconsistency (L5 in Fig.\u0026nbsp;4) by making studies portable and comparable, while enabling sensitivity checks\u0026mdash;such as varying window sizes or compositing rules\u0026mdash;without re-engineering full workflows (Marchesan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Munhoz et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Overall, the trajectory has been from workstation calculators to documented, interoperable pipelines that allow independent verification and cross-study synthesis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Advances in New Methods (post-2016)\u003c/h2\u003e\u003cp\u003eThis subsection emphasizes methodological expansions since 2016, grouped into arcs that show how capabilities accreted.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2016–2019: from 2-D patterns to structure and information.\u003c/h3\u003e\n\u003cp\u003eThe first shift was explicit incorporation of vertical structure. Lidar-based methods captured canopy height and porosity, reframing connectivity as three-dimensional rather than planar (Zald et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Remmel, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In parallel, information-theoretic approaches gained ground: Nowosad and Stepinski (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) described landscapes as configuration distributions rather than lists of indices, and Remmel (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) formalized hyper-local configuration elements for pattern diagnostics. During this period, GIS platforms still anchored workflows, with Ramachandra et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) using \u003cem\u003eFRAGSTATS\u003c/em\u003e within \u003cem\u003eArcGIS\u003c/em\u003e and PCA environments to analyze forest hotspots in India.\u003c/p\u003e\n\u003ch3\u003e2020–2022: connectivity inside patches and networks between them.\u003c/h3\u003e\n\u003cp\u003eConnectivity refinements unfolded at multiple scales. Within patches, Complexity-Weighted Patch Area (CWA) and related formulations weighted area by form/roughness, capturing intra-patch navigability in ways comparable to classical graph indices (Justeau-Allaire et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At broader scales, ProNet provided a bounded, report-ready index for protected-area systems (Theobald et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Representation also matured: vector-native indices (\u003cem\u003eVecLI\u003c/em\u003e) reduced raster biases, and \u003cem\u003eGuidosToolbox/Workbench\u003c/em\u003e introduced multiscale distance\u0026ndash;similarity operators that integrate directly with MSPA roles (Vogt, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vogt et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Integration with GIS remained central, with predictive CA\u0026ndash;Markov modeling in \u003cem\u003eIDRISI\u003c/em\u003e tied to FRAGSTATS outputs for long-term forecasts (Malhi et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e2023–2025: composites, edges, density at policy scale, and controlled experiments.\u003c/h3\u003e\n\u003cp\u003eRecent years have seen consolidation across pattern, trajectory, and driver domains. Composite indices such as the Forest Fragmentation Comprehensive Index (FFCI) combine spectral change, configuration, and context to separate loss from recovery (Wu et al., \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Forest Edge Index (FEI) standardizes edge-centric states for driver analyses, and the Multiscale Similarity Index (MSI) applies Jensen\u0026ndash;Shannon similarity to benchmark observed mosaics against fully forested references (Zhen et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Netzel et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the policy scale, FAD\u0026ndash;FOS pipelines have matured into tools for inter-regional comparability with explicit spatial supports (Vogt \u0026amp; Caudullo, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Methodological frameworks have also tightened raster\u0026ndash;vector integration (\u003cem\u003eVARLI\u003c/em\u003e) and coupled detailed indicators with machine learning to attribute processes (Huang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhere time series underpin inference, operational feeders such as provincial \u003cem\u003eLandTrendr\u003c/em\u003e applications now pipe disturbance segments directly into fragmentation indicators, and sensitivity tests quantify how fixed-scale choices affect outcomes\u0026mdash;relevant to scale sensitivity (L1 in Fig.\u0026nbsp;4) (Qiu et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Neutral generators such as \u003cem\u003eflsgen\u003c/em\u003e permit stress-testing of metrics under controlled fragmentation mosaics before transfer to real landscapes (Justeau-Allaire et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent case studies also demonstrate tighter GIS integration: Zhang et al. (\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) combined \u003cem\u003eGuidosToolbox\u003c/em\u003e, \u003cem\u003eConefor\u003c/em\u003e, and \u003cem\u003eArcGIS\u003c/em\u003e to construct ecological security patterns; Lin et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) fused \u003cem\u003eFRAGSTATS\u003c/em\u003e, \u003cem\u003eENVI\u003c/em\u003e, and \u003cem\u003eGeoDa\u003c/em\u003e with machine learning in R; and Netzel et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used \u003cem\u003eGDAL/OGR\u003c/em\u003e and custom C code to implement MSI. Together these highlight how GIS platforms are not superseded but remain the backbone environments in which fragmentation innovations are operationalized.\u003c/p\u003e"},{"header":"4. Discussion — overview","content":"\u003cp\u003eAcross 138 studies, forest-fragmentation methods progress from patch tallies to role-aware, connectivity-explicit, and increasingly 3-D representations. We interpret this trajectory through five recurring \u003cem\u003elimitations (L1\u0026ndash;L5)\u003c/em\u003e that affect transferability: \u003cb\u003eL1\u003c/b\u003e\u0026mdash;scale sensitivity and habitat-amount conflation; \u003cb\u003eL2\u003c/b\u003e\u0026mdash;region-specific thresholds and assumptions; \u003cb\u003eL3\u003c/b\u003e\u0026mdash;weak empirical validation (especially for global products); \u003cb\u003eL4\u003c/b\u003e\u0026mdash;limited linkage to biological responses; and \u003cb\u003eL5\u003c/b\u003e\u0026mdash;incomplete parameter reporting. Figure\u0026nbsp;3 is a schematic of these limitations; evidence and implications appear below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Tracing the evolution of metric suitability\u003c/h2\u003e\u003cp\u003eRipple et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) showed early on that GIS-derived indices could reveal fragmentation trajectories, and the FRAGSTATS era formalized patch, edge, shape, and core metrics for reproducible mapping (McGarigal \u0026amp; Marks, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Walker \u0026amp; Kenkel, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Remmel \u0026amp; Csillag, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sun \u0026amp; Southworth, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The central weakness\u0026mdash;\u003cb\u003eL1\u003c/b\u003e\u0026mdash;is that many indices vary with grain and extent, so differences can reflect pixel size or window choice rather than ecological change (O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Long et al., 2010; Pe\u0026rsquo;er et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Remmel (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) explains part of the mechanism: coincidence matrices summarize composition well but capture little about configuration unless augmented, making composition\u0026ndash;configuration conflation likely when one class dominates.\u003c/p\u003e\u003cp\u003eMSPA reframed maps into roles\u0026mdash;cores, edges, bridges, corridors, perforations\u0026mdash;useful at reporting-unit and continental scales (Vogt et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Estreguil \u0026amp; Mouton, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wickham et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Connectivity metrics followed. Roberts et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and later Lin et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Theobald et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) translated dispersal and resistance assumptions into graph-based indicators (PC, IIC, dI; \u003cem\u003eProNet\u003c/em\u003e) with clearer decision relevance, while surfacing \u003cb\u003eL2\u003c/b\u003e (context dependence of species/guild parameters) and \u003cb\u003eL4\u003c/b\u003e (the gap to observed responses).\u003c/p\u003e\u003cp\u003eRecent families aim to curb conflation and tighten links to process. Fixed-scale density (FAD/FOS) declares spatial support up front, stabilizing inter-regional comparisons (Vogt \u0026amp; Caudullo, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Information-theoretic and local-connectedness measures separate form complexity from neighborhood linkage (Peptenatu et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alage et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). And voxel/3-D approaches bring canopy permeability and edge penetration into scope, advancing structure\u0026ndash;function hypotheses but raising data and validation demands (Remmel, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Across these arcs, results travel best when spatial support, thresholds, and connectivity parameters are stated and stress-tested; otherwise, method settings masquerade as ecological differences (Wang et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fahrig, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nunes et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Transforming accessibility through open-source, GIS, and cloud ecosystems\u003c/h2\u003e\u003cp\u003eOpen, scriptable ecosystems have turned isolated metric runs into auditable pipelines. In R, \u003cem\u003elandscapemetrics\u003c/em\u003e and motif expose FRAGSTATS-style indices within reproducible workflows; in Python, \u003cem\u003ePyLandStats\u003c/em\u003e and \u003cem\u003eLecoS\u003c/em\u003e fill a similar role; and \u003cem\u003eGuidosToolbox\u003c/em\u003e with its Graph-based Workflow Builder scales role-based morphology for large reporting units (Hesselbarth et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nowosad, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vogt \u0026amp; Riitters, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vogt et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). General-purpose GIS\u0026mdash;\u003cem\u003eQGIS\u003c/em\u003e (with \u003cem\u003eProcessing\u003c/em\u003e, \u003cem\u003eGRASS GIS\u003c/em\u003e, \u003cem\u003eSAGA\u003c/em\u003e) and \u003cem\u003eArcGIS Pro\u003c/em\u003e (ModelBuilder, \u003cem\u003eArcPy\u003c/em\u003e)\u0026mdash;now acts as the orchestration layer where parameters are modeled, batched, and versioned, while \u003cem\u003eGeoDa\u003c/em\u003e provides spatial-autocorrelation diagnostics. \u003cem\u003eGoogle Earth Engine\u003c/em\u003e has democratized compositing and change detection at scale without bespoke infrastructure (Wulder et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Coops et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gorelick et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTwo practice gaps persist. First, \u003cb\u003eL5\u003c/b\u003e: key parameters are too often omitted\u0026mdash;grain, extent, edge rules, windowing, compositing policy, detector settings\u0026mdash;with our screen suggesting roughly a sixth of papers miss at least one (Hernando et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zatelli et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, \u003cb\u003eL3\u003c/b\u003e: reliance on global products without local checks (e.g., \u003cem\u003eGlobal Forest Change\u003c/em\u003e) risks omission/commission errors in selective logging, coppice, and fire mosaics (Hansen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nunes et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Addressing both rarely requires new software; it requires concise parameter logs and validation notes attached to each map product. Ramachandra et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) offer a good template, combining \u003cem\u003eFRAGSTATS\u003c/em\u003e and PCA in \u003cem\u003eArcGIS\u003c/em\u003e to surface regional drivers in India\u0026rsquo;s Western Ghats.\u003c/p\u003e\u003cp\u003eMachine learning extends these pipelines from description to attribution. Zanella et al. (\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Zhen et al. (\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrate how Random Forest applied to PD, LPI, Division, or FFCI composites can illuminate drivers, while Moreira et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Lin et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show forward scenarios via ANN or CA-Markov. The same features that add explanatory power raise the bar for transparency: credible ML use reports features and neighborhoods, data partitioning and cross-validation, model settings and interpretability steps, and\u0026mdash;crucially\u0026mdash;external or hold-out checks (Hansen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hernando et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zatelli et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Elevating fidelity with advanced data sources\u003c/h2\u003e\u003cp\u003eMulti-sensor regimes sharpen structural detection and help separate composition from configuration (Maier et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Long et al., 2010; Zald et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mshelia et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Airborne and terrestrial laser scanning reveal vertical heterogeneity that 2-D indicators miss\u0026mdash;central for permeability and microclimate\u0026mdash;while detector families (\u003cem\u003eVCT\u003c/em\u003e, \u003cem\u003eLandTrendr\u003c/em\u003e, \u003cem\u003eTTM\u003c/em\u003e, tri-temporal Sentinel-2) stabilize disturbance trajectories before indicators are computed (Huang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kennedy et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Giannetti et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Francini et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These gains amplify familiar trade-offs: finer spatial and temporal support heightens \u003cb\u003eL1\u003c/b\u003e sensitivities and can invite over-interpretation without ecological corroboration (Ostapowicz et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fahrig, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Voxel/3-D formulations promise tighter links to structure and biomass but are data-hungry and demand stronger validation beyond instrumented sites (Remmel, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mazziotta et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In practice, high-fidelity inputs work best when paired with explicit parameter disclosure and targeted field or higher-resolution checks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Bridging research and practice: targeted fixes for L1\u0026ndash;L5\u003c/h2\u003e\u003cp\u003e\u003cb\u003eValidation first (addresses L3, L4).\u003c/b\u003e Mac Nally (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) argued for design-based estimation when mapping prevalence; following that guidance, fragmentation workflows should link indicators to field plots, biodiversity proxies, or independent structure data and report design-based or model-assisted area estimates where feasible (Nunes et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStandardized reporting (addresses L1, L5).\u003c/b\u003e Rather than rely on defaults, specify the spatial support and thresholds that govern outputs: grain, extent, edge width, and window size for rolling measures (for FAD, stabilization typically occurs at tens to low hundreds of pixels in dissected landscapes: Zatelli et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); forest definitions and MMU (e.g., FAO/HRL-FTY; Vogt \u0026amp; Caudullo, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); compositing policy (Best Available Pixel vs. medoid, target phenology, sensor priority, despiking; Francini et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); and detector settings\u0026mdash;\u003cem\u003eVCT\u003c/em\u003e masking/IFZ, \u003cem\u003eLandTrendr\u003c/em\u003e segmentation/recovery, \u003cem\u003eTTM\u003c/em\u003e cross-validation, tri-temporal Sentinel-2 cut-offs (Huang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kennedy et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Giannetti et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Francini et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegion-tuned implementations (addresses L2).\u003c/b\u003e Calibrate thresholds, windows, and resistance/dispersal assumptions to local disturbance regimes and canopy architecture so metrics reflect regional realities rather than imported defaults (Geri et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rosa et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kozak et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Osewe et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ramachandra et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) exemplify this tuning in a biodiversity hotspot.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-scale integration (addresses L1, L4).\u003c/b\u003e Combine complementary families to reduce conflation and expose process: pair MSPA roles with graph metrics for movement potential; deploy fixed-window FAD\u0026ndash;FOS for policy comparability; use INCOMA/gradient surfaces for heterogeneous mosaics; and add voxel morphology where vertical connectivity matters (Nowosad \u0026amp; Stepinski, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Remmel, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vogt \u0026amp; Caudullo, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOpen, cloud-based reproducibility (enables L1\u0026ndash;L5).\u003c/b\u003e Share R/Python/GEE workflows\u0026mdash;\u003cem\u003elandscapemetrics\u003c/em\u003e, motif, \u003cem\u003egeodiv\u003c/em\u003e, \u003cem\u003eVecLI/VARLI\u003c/em\u003e, \u003cem\u003eLDT4QGIS\u003c/em\u003e\u0026mdash;so parameters are visible, versioned, and re-runnable, making sensitivity checks straightforward and enabling like-for-like comparisons (Mairota et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hesselbarth et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Paix\u0026atilde;o \u0026amp; Machado, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Summary statements","content":"\u003cp\u003eAcross three decades and 138 studies, fragmentation analysis has shifted from patch/edge tallies to role-aware, connectivity-explicit, and increasingly three-dimensional descriptions. The most useful way to read that shift is as a linked chain\u0026mdash;data sources \u0026rarr; change detectors \u0026rarr; pattern indicators\u0026mdash;where choices made upstream condition what indicators can say downstream.\u003c/p\u003e\u003cp\u003eFive limitations repeatedly shape inference. Scale sensitivity and habitat-amount dependence remain the main source of comparability problems (L1). Parameters tuned in one region do not travel cleanly to another (L2). Adoption of global or automated layers without local checks is still common (L3). Structural metrics are too rarely tied to biological responses (L4). And key settings\u0026mdash;spatial support, edge rules, compositing policy, detector thresholds\u0026mdash;are inconsistently reported (L5).\u003c/p\u003e\u003cp\u003eWhat works in practice is incremental rather than novel. Declaring spatial support (e.g., fixed-window density) stabilizes comparisons; pairing role-based morphology with connectivity metrics clarifies movement options; and, where vertical structure matters, targeted lidar/TLS or voxel summaries add needed realism. The common denominator is transparent parameterization with light-weight sensitivity checks, so that apparent differences reflect landscapes rather than hidden settings.\u003c/p\u003e\u003cp\u003eEvidence gaps are evident. Validation is often lacking and could benefit from design-aware, independent approaches; methods might be more effective when tuned to regional disturbance regimes and canopy architecture; and clearer cross-walks between 2-D indicators and 3-D/voxel measures could enhance understanding. Geographic coverage also remains uneven, with several tropical regions under-represented.\u003c/p\u003e\u003cp\u003eLooking ahead, future progress may hinge less on new indices and more on refining existing ones through precise specification and validation. Key considerations include: (i) documenting spatial support and detector settings as metadata, (ii) exploring region-balanced benchmarking and neutral-landscape challenges to assess sensitivity, (iii) developing simple, shareable workflows for inspection, and (iv) integrating field or high-resolution data where possible. Adopting these approaches could strengthen fragmentation measures\u0026rsquo; reliability for ecological interpretation, monitoring, and decision-making amid accelerating habitat change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAll authors contributed specific sections to the manuscript and assisted with critical revisions.\u003cbr\u003e\u0026nbsp;Sanjana Dutt led the conceptualization, systematic review, and overall coordination of the manuscript, including preparation of the first and revised drafts.\u003cbr\u003e\u0026nbsp;Tarmo Remmel, Carlos Rivas, Adriano Mazziotta and Mieczysław Kunz contributed to review refinement and provided input on structure, interpretation, and revisions.\u003cbr\u003e\u0026nbsp;All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eUse of Generative AI\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eDuring the preparation of this work, Sanjana Dutt used OpenAI’s ChatGPT and xAI’s Grok to assist with language editing and structural refinement of the manuscript. Napkin AI was used to assist with preliminary visualization design for one figure. All AI-generated content was reviewed, edited, and finalized by the author, who takes full responsibility for the integrity and accuracy of the presented work.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNo new data were generated or analyzed in this study. All information is based on published literature cited within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamczyk, J., \u0026amp; Tiede, D. (2017) ZonalMetrics\u0026mdash;a Python toolbox for zonal landscape structure analysis. 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Continuous change detection and classification of land cover using all available Landsat data. \u003cem\u003eRemote sensing of Environment\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e, 152\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2014.01.011\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2014.01.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"forest fragmentation, landscape metrics, change detection, lidar, open-source workflows, methodological synthesis","lastPublishedDoi":"10.21203/rs.3.rs-7684385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7684385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eContext\u003c/strong\u003e\u003cbr\u003e\nForest fragmentation—the breakup of continuous habitat into isolated patches—alters landscape processes and biodiversity. Rapid advances in sensors and computing have diversified diagnostic methods, but comparability and ecological linkage remain uneven.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003cbr\u003e\nSynthesize 138 methodological studies (1990–2025) to: (i) chart shifts in metric families, including emerging 3-D approaches; (ii) assess how data and processing choices shape indicator performance; and (iii) distill limits and reporting practices that improve portability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nWe reviewed studies using lidar/TLS and Sentinel-2 inputs, change detection, and indicators implemented in \u003cem\u003e\u003cstrong\u003elandscapemetrics, GuidosToolbox,\u003c/strong\u003e\u003c/em\u003eand\u003cem\u003e\u003cstrong\u003e Google Earth Engine,\u003c/strong\u003e\u003c/em\u003e tracing transitions from patch/edge metrics to morphology-aware roles, connectivity, fixed-window density, and 3-D/voxel measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nThe field is moving toward morphology-aware roles, multiscale connectivity, fixed-scale density, and vertical structure. Five recurring limits are: scale sensitivity and habitat-amount confounding; region-tuned parameters that hinder transfer; scarce field validation of global/automated products; weak or inconsistent biotic links of structural metrics; and incomplete reporting that curbs reproducibility. Gaps include uneven tropical coverage and limited 2-D/3-D cross-walks. Priorities are transparent parameterization and sensitivity checks, precise documentation of spatial/detector settings, region-specific benchmarking, shareable workflows, and integration of field data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary\u003c/strong\u003e\u003cbr\u003e\nStandardizing documentation, validation, and cross-scale linkages can improve the reliability of fragmentation measures for monitoring and conservation. Emphasis should be on refining and harmonizing existing methods rather than proposing new indices\u003c/p\u003e","manuscriptTitle":"Advancing Forest Fragmentation Analysis: A Systematic Review of Evolving Spatial Metrics, Software Platforms, and Remote Sensing Innovations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 12:31:40","doi":"10.21203/rs.3.rs-7684385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T20:43:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T16:40:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T15:00:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35910604444767153285913587120883990997","date":"2025-12-15T21:16:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84138480357998982602860773329040676738","date":"2025-11-27T16:25:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338131905985094691822381712241855094201","date":"2025-11-13T14:00:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T14:41:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T02:55:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-23T02:55:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Landscape Ecology","date":"2025-09-22T12:00:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"landscape-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"land","sideBox":"Learn more about [Landscape Ecology](https://www.springer.com/journal/10980)","snPcode":"10980","submissionUrl":"https://submission.nature.com/new-submission/10980/3","title":"Landscape Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0ee0e037-550d-4d07-8e47-1e9a21603d36","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:01:02+00:00","versionOfRecord":{"articleIdentity":"rs-7684385","link":"https://doi.org/10.1007/s10980-026-02354-7","journal":{"identity":"landscape-ecology","isVorOnly":false,"title":"Landscape Ecology"},"publishedOn":"2026-04-28 15:57:04","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2025-10-17 12:31:40","video":"","vorDoi":"10.1007/s10980-026-02354-7","vorDoiUrl":"https://doi.org/10.1007/s10980-026-02354-7","workflowStages":[]},"version":"v1","identity":"rs-7684385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7684385","identity":"rs-7684385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}