Impact of Mining Ban: Degradation to revival Assessing Forest cover change in Dharbandora Goa

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Shetkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8962349/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In Recent decades, the forest cover has vulnerable to anthropogenic interventions which has resulted in degradation of forest. Goa, being a part of Western Ghats, a prime biodiversity hotspot, characterized by its unique biodiversity and ecological significance has witnessed dynamic changes in land cover patterns over the past few decades mainly due to infrastructure and development activities, of which mining has been prominent. Therefore, the paper aims to study the effect of mining ban (pre and post) on forest cover by considering two time periods, 2012 and 2022. Remotely sensed data from NRSC IRS LISS III (2012 and 2022) were obtained from Bhoonidhi portal. Image processing techniques, including unsupervised classification, were applied using ERDAS and ArcGIS software to delineate six LULC categories: agricultural land, barren land, built-up areas, natural vegetation, mining zones, and water bodies. Comparative analysis quantified spatial and temporal changes. Unplanned and uncontrolled exploration of mining ore has resulted in degradation of forest resources. The findings reveal substantial shifts in LULC following the mining ban. Natural vegetation increased from 249.07 km² (65.84%) in 2012 to 276.05 km² (72.98%) in 2022, while mining zones declined from 10.11 km² (2.67%) to 2.11 km² (0.55%). Water bodies expanded from 1.32 km² to 6.87 km², indicating ecological restoration. Conversely, agricultural land, barren land, and built-up areas showed declines, reflecting reduced human activity and soil degradation linked to mining. The mining ban in Goa has positively influenced forest recovery and water body revival in Dharbandora taluka Environmental Engineering Forestry Mining NRSC Landuse Landcover Remote Sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The intricate relationship between land use and land cover (LULC) plays a pivotal role in understanding regional resources (Gadgil & et al., 2011). In recent decades, ecosystems have faced unprecedented challenges due to various anthropogenic interventions, leading to the degradation of forests (Reddy, Jha, & Dadhwal, 2016 ). Consequently, effective management strategies are urgently required to ensure sustainable development (Dong, et al., 2019 ). Natural resource sustainability is ensured by the dynamic interactions between various biophysical factors that make up the landscape (TV & S, 2018 ). Functions of each ecosystem are largely determined by complex interactions between biological, economic, social, and cultural entities that depend on the landscape's structure. Massive changes in LULC over the past century have resulted in biodiversity loss (Deb, et al., 2018 ). Human interference, driven by population growth and resource demand, has negatively impacted forest worldwide (Drummond & Loveland, 2010 ). The primary cause of forest loss and biodiversity are due to increase in growing human population (Forester & Machlist, 1996 ), and their demand for food, and natural resources. (Ramachandran, et.al; 2018 ). Monitoring natural resources through Remote sensing and GIS has become essential for r managing and monitoring environmental changes (Reddy & et,al., 2013). Land management (Lautetu et al., 2022 ) and LULC change is primarily a local event (Fu, et.al; 2015 ), since the characteristics of such change can vary dramatically from one region to another. India is one of the top 12 mega-biodiversity nations, accounts for six percent of the world’s forests covering approximately area of 692,027 km2 (Rawat & Rawat, 2025 ). With a population of 1.4 billion resource pressure is immense (Devi & et,al., 2018). Thus, it attracts to assess the impact of Land use change alteration on forests of the country (Bilyaminu & et,al., 2021). Land conversion and extreme events causing differential land cover modifications and conversions across the country is at flux (Kumar & et.al., 2014). Mining, while vital for economic growth has significantly degraded forests (Garai & Narayana, 2018 ). However, its operations often result in significant environmental degradation and disruption of ecosystems (Prakash & Gupta, 1998 ). Mining activities loosen the structure of the soil making it more prone to vulnerability (Basuki et al., 2023 ). Goa, rich in iron ore deposits, relied heavily on mining until its ban in 2014. Talukas like Dharbandora, Bicholim, Sattari, and Sanguem located on the foothills of Western ghats(Chaturvedi et al., 2011 ) are rich in iron ore deposits. The mining in Goa is predominant practice before the liberation. Which has been restricted since 2014 in the state. The study investigates the impact of mining ban in Dharbandora taluka. Highlighting shifts from agriculture to mining and subsequent environmental consequences. Study Area Goa, situated between latitudes 14° 53’ 57’’N and 15° 47’ 59’’ N and longitudes 73° 40’ 54’’ and 74° 53’11’’ E, covers an area of 3702 sq. km. Bounded by Maharashtra in the north, Karnataka in the east and south, and flanked by the Arabian Sea to the west, Goa is mainly divided into two districts, consisting of 12 talukas. (Kumbhar et al., 2025 ). Dharbandora, Goa’s youngest taluka, lies in the central-eastern regions. It comprises 5 villages comprising of 16 hamlets with undulating hills and valleys. Hydrological, the taluka plays a vital role in draining fresh water drainage. As per 2011 census it is estimated that the total population of taluka is 39,183 (Census, 2011) Data Source and Methodology Land use has a significant impact on forest characteristics, potentially causing large-scale changes in the context of forest cover. Land-use changes are recognized as fundamentally important to understanding a range of ecological, biophysical, social, and climate consequences (Drummond & Loveland, 2010 ). Remote Sensing (RS) and Geographic Information System (GIS) are cruicial tools for monitoring LULC changes (Kayet & Pathak, 2015 ). For this study, NRSC IRS LISS III data (2012and 2022) with a spatial resolution of 23.5m was obtained from Bhoonidhi portal. Bands 2, 3 and 4 were analyzed using ERDAS and ARCGIS software. Table 1 Spatial data sources Sr. No. Data type Date of production Resolution Source I. II. Liss III Liss III 2012-02-17 2022-10-27 23.5 m 23.5m Bhuvan Bhoonidhi Image Classification techniques (supervised and unsupervised) were applied, with NRSC level 1 classification is used to assign categories. Visual interpretation supported classification into six land use categories: agricultural land, barren land, built-up areas, natural vegetation, mining zones, and water bodies. Results and Discussion Mapping forest cover is important for assessing natural resource inventory and implement effective management strategies(Sharma et al., 2017 ) in any given region. Satellite remote sensing, being at the forefront of technological advancements, has become indispensable in quantifying and monitoring deforestation activities. The study evaluates the landuse landcover change from mining to post mining phase in Dharbandora taluka between 2012 and 2022. In the year 2012, a inclusive assessment of land use and land cover (LULC) in Dharbandora highlights a diverse landscape encompassing various types of landuses. As the region exhibited a significant stretch of densely vegetated areas spanning over 249.07 km², representing dense forest cover or lush vegetation. Conversely, water bodies were limited, covering merely 1.32 km², suggesting scarce aquatic resources. Mining activities were notably concentrated in the central region, accounting for 10.11 km² of the total area. Barren land, covering 34.00 km², indicated areas devoid of vegetation and unsuitable for agricultural purposes. Built-up areas, encompassing 27.67 km², indicated urban or developed zones within the region. Agricultural land, comprising 56.07 km², represented a substantial portion of the landscape dedicated to farming activities. This assessment underscores the diverse land uses within Dharbandora, reflecting a mosaic of natural environments and human activities shaping the region's landscape in 2012. In 2022, significant changes were observed in the LULC of the region. Natural vegetation dominated the landscape, covering the largest area of 276.05 km², indicating the preservation of substantial ecological areas. Agricultural land followed, with 44.74 km² dedicated to farming activities, showcasing the region's reliance on agriculture. However, the mining sector experienced a sharp decline, with only 2.11 km² allotted to mining regions due to a ban imposed in Goa in 2014. This regulatory measure led to a rapid reduction in mining activities. Water bodies contributed 6.87 km², highlighting the presence of essential aquatic resources. Conversely, barren land and built-up areas covered 25.75 km² and 22.71 km², respectively, signifying areas where natural vegetation has been lost or replaced by human infrastructure. These changes illustrate the dynamic interplay between human activities and natural processes shaping the region's landscape over the past decade. Table 2 Land Use Land Cover Distribution and Change (2012 and 2022) Class Name Area 2012 (sq.km) Area (%) Area 2022 (sq.km) Area (%) Variance (%) Change (sq.km} Agricultural Land 56.07 14.82 44.74 11.82 -3 -11.33 Barren Land 34.00 8.99 25.75 6.81 -2.18 -8.25 Built Up 27.67 7.31 22.71 6.00 -1.31 -4.96 Natural Vegetation 249.07 65.84 276.05 72.98 + 7.14 26.98 Mining 10.11 2.67 2.11 0.55 -2.12 -8 Water bodies 1.32 0.34 6.87 1.81 + 1.47 5.55 Total 378.24 100 378.24 100 The research delves into the intricate relationship between mining activities and the dynamic shifts in Land Use and Land Cover (LULC) patterns. Through meticulous analysis, six distinct LULC categories were delineated: Agricultural land, Barren land, Built-Up areas, Natural Vegetation, Mining zones, and water bodies. The study denotes notable changes in LULC both prior to onset of mining operations spanning the years from 2012 to 2022. The findings unveil a series of significant alterations in the landscape composition over the specified timeframe. Agricultural land, once occupying 14.82% of the area, witnessed a discernible decline, shrinking to 11.89% due to degradation of the land because of over-exploitation of mining activities with a variation of -3%, agricultural land was used as a dumping site for the mineral ore extracted from the mines and lack of source of irrigation. Similarly, Barren land and Built-Up areas experienced diminishing proportions, declining from 8.99% and 7.31% to 6.81% and 6.00%, which observed the variation of -2.28% and − 1.31% respectively. In contrast, the extents of Natural Vegetation and water bodies underwent marked expansion, increasing from 65.84% to 72.98% and 0.34% to 1.81%, with change of + 7.14% and + 1.47% during the same span wherein people/workers tend to migrate resulting in decline of the infrastructural activities further depleting the built-up land and inclination in vegetation and water bodies. Notably, the analysis highlights a notable reduction in mining zones, with their coverage dwindling from 2.67% to a mere 0.55% (-2.12%). Such fluctuations underscore the profound impact of mining activities on the regional landscape, manifesting in significant alterations to land use and cover dynamics. These findings not only contribute to a deeper understanding of the environmental consequences of mining but also provide crucial insights for sustainable land management and conservation strategies within the study area. Conclusion The intricate relationship between land use and land cover (LULC) is crucial for understanding regional resources, especially in the face of unprecedented human interventions, particularly in forest ecosystems. India, including regions like Dharbandora taluka in Goa, has witnessed significant alterations in forest landscapes due to shifts from agriculture to mining, leading to profound changes in land use and cover. This transition not only degrades soil resources but also impacts local livelihoods, prompting a shift towards mining-based occupations. Utilizing Remote Sensing (RS) and Geographic Information System (GIS) technologies, alongside NRSC IRS data, allows for comprehensive analysis of LULC changes. The study of Dharbandora taluka's LULC changes from 2012 to 2022 reveals significant alterations, including declines in agricultural and built-up areas due to mining impacts, while natural vegetation and water bodies expand. Notably, mining zones experience a substantial reduction, underscoring the need for sustainable land management strategies to mitigate the profound impact of mining on regional land use dynamics and ensure long-term environmental sustainability. Declarations Acknowledgment The author gratefully acknowledges the use of artificial intelligence (AI) tools in refinement of this research paper. AI-assisted platforms like Scolar AI were used. References Basuki B, Sulistiawati N, Verdian D, Naely Z (2023) The sensitivity level of landslide risk using Geographic Information System on the slopes of Mount Argopura, East Java, Indonesia. J Degraded Min Lands Manage 11(1):4949–4959. https://doi.org/10.15243/jdmlm.2023.111.4949 Bilyaminu H,et,al (2021) Monitoring Land Use and Land Cover Change of Forest Ecosystems of Shendurney Wildlife Sanctuary, Western Ghats, India. 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J Clean Prod 1562–1578. https://doi.org/10.1016/j.jclepro.2018.10.291 Drummond MA, Loveland TR (2010) Land-use Pressure and a Transition to Forest-cover Loss in the Eastern United States. Bioscience 50(4):286–298 Forester DJ, Machlist GE (1996) Modeling Human Factors That Affect the Loss of Biodiversity. Conserv Biol 10(4):1253–1263. https://doi.org/10.1046/j.1523-1739.1996.10041253.x Fu Y, Lu X, Zhao Y, Zeng X, Xia L (2015) Assessment Impacts of Weather and Land Use/Land Cover (LULC) Change on Urban Vegetation Net Primary Productivity (NPP): A Case Study in Guangzhou, China. Remote Sens 5(8):4125–4144. https://doi.org/10.3390/rs5084125 Gadgil M et al (2011) Mapping ecologically sensitive, significant and salient areas of Western Ghats: proposed protocols and methodology. Curr Sci 100:175–181 Garai D, Narayana AC (2018) Land use/land cover changes in the mining area of Godavari coal fields of South India. Egypt J Remote Sens Space Sci 21(3):375–381. https://doi.org/10.1016/j.ejrs.2018.01.002 GSBB, (. G (2019–2020) State Action Plan on Climate Change for The State of Goa. Kayet N, Pathak K (2015) Remote Sensing and GIS Based Land use/Land cover Change Detection Mapping in Saranda Forest, Jharkhand, India. Int Res J Earth Sci 3(10):1–6 Kumar R,et.al (2014) Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecol Ind 45:445–455. https://doi.org/10.1016/j.ecolind.2014.05.003 Kumbhar D, Sawant N, Yedage A (2025) Prioritization of Sub Watershed of the Mandovi Basin of Goa. J Technol 13(1):451–461 Lautetu LM, Hasibuan HS, Tambunan RP (2022) Land management on small islands based on settlement distribution patterns: studies on Sulabesi Island, Indonesia. J Degraded Min Lands Manage 9(4):3653. https://doi.org/10.15243/jdmlm.2022.094.3653 Prakash A, Gupta PR (1998) Land use mapping and change detecgtion in a coal mining area- A case study in the Coalfield India. int J Remote Sens 3:391–410 Ramachandran RM, Roy PS, Chakravarthi V, Sanjay J, Joshi PK (2018) Long-term land use and land cover changes (1920–2015) in Eastern Ghats, India: Pattern of dynamics and challenges in plant species conservation. Ecol Ind 85:21–36. 10.1016/j.ecolind.2017.10.012 Rawat RS, Rawat VRS (2025) Climate Change and Forest Sector in India. In Textbook of Forest Science (pp. 253–285). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-8289-5_12 Reddy CS,et,al (2013) Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS. Natl Remote Sens Centre, 1–4 Reddy CS, Jha CS, Dadhwal VK (2016) Assessment and monitoring of long-term forest cover changes (1920–2013) in Western Ghats biodiversity hotspot. J Earth Syst Sci 125:103–114 Sawant N (2022) Goa's Landscape through maps, vol 1. Dnyanmangal Publication Distribution, Solapur Sharma J, Upgupta S, Jayaraman M, Chaturvedi RK, Bala G, Ravindranath NH (2017) Vulnerability of Forests in India: A National Scale Assessment. Environ Manage 60(3):544–553. https://doi.org/10.1007/s00267-017-0894-4 TV R, S B (2018) J Remote Sens GIS 07(01). 10.4172/2469-4134.1000227 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8962349","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596617867,"identity":"67eeb15c-8c7d-4175-b1e4-65069136d299","order_by":0,"name":"Deepak Kumbhar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDCCA8cYDiT+sWEwAPMKoIIEtDA++NiQBtViQJQWNmbDmQ2HUbXgBXwHj6VJ8+44L2/O3mP8msfAxp6B/QDj4QI8WiQPHDsmzXvmtuHOnjNm1jwGaYkNPAkMh2fg0WJw4HibNA/b7QSDG7nbjHkMDicwSDAwHOYhrOUcTMt/eyK0HDtsOLPtAEjL5sc8BgcYGwhpAfol8cGHM8mGG86c/8Y4xyA5sY0nsQGvFr4bxwwOJFTYyRscb0v+8KbCzp6f/fDhz/i0MEgcgDPZJMAkA2MDPg0MDPwIeeYP+JWOglEwCkbBSAUAZHtVwA2qqVsAAAAASUVORK5CYII=","orcid":"","institution":"Parvatibai Chowgule College of Arts and Science, Margao.","correspondingAuthor":true,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Kumbhar","suffix":""},{"id":596617868,"identity":"46e662f2-62c8-40c7-a799-b7ec6cd199dd","order_by":1,"name":"Rutvik D. 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In recent decades, ecosystems have faced unprecedented challenges due to various anthropogenic interventions, leading to the degradation of forests (Reddy, Jha, \u0026amp; Dadhwal, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consequently, effective management strategies are urgently required to ensure sustainable development (Dong, et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Natural resource sustainability is ensured by the dynamic interactions between various biophysical factors that make up the landscape (TV \u0026amp; S, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Functions of each ecosystem are largely determined by complex interactions between biological, economic, social, and cultural entities that depend on the landscape's structure.\u003c/p\u003e \u003cp\u003eMassive changes in LULC over the past century have resulted in biodiversity loss (Deb, et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Human interference, driven by population growth and resource demand, has negatively impacted forest worldwide (Drummond \u0026amp; Loveland, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The primary cause of forest loss and biodiversity are due to increase in growing human population (Forester \u0026amp; Machlist, \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e), and their demand for food, and natural resources. (Ramachandran, et.al; \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Monitoring natural resources through Remote sensing and GIS has become essential for r managing and monitoring environmental changes (Reddy \u0026amp; et,al., 2013). Land management (Lautetu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and LULC change is primarily a local event (Fu, et.al; \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), since the characteristics of such change can vary dramatically from one region to another.\u003c/p\u003e \u003cp\u003eIndia is one of the top 12 mega-biodiversity nations, accounts for six percent of the world’s forests covering approximately area of 692,027 km2 (Rawat \u0026amp; Rawat, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). With a population of 1.4\u0026nbsp;billion resource pressure is immense (Devi \u0026amp; et,al., 2018). Thus, it attracts to assess the impact of Land use change alteration on forests of the country (Bilyaminu \u0026amp; et,al., 2021). Land conversion and extreme events causing differential land cover modifications and conversions across the country is at flux (Kumar \u0026amp; et.al., 2014).\u003c/p\u003e \u003cp\u003eMining, while vital for economic growth has significantly degraded forests (Garai \u0026amp; Narayana, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, its operations often result in significant environmental degradation and disruption of ecosystems (Prakash \u0026amp; Gupta, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). Mining activities loosen the structure of the soil making it more prone to vulnerability (Basuki et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Goa, rich in iron ore deposits, relied heavily on mining until its ban in 2014. Talukas like Dharbandora, Bicholim, Sattari, and Sanguem located on the foothills of Western ghats(Chaturvedi et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) are rich in iron ore deposits. The mining in Goa is predominant practice before the liberation. Which has been restricted since 2014 in the state.\u003c/p\u003e \u003cp\u003eThe study investigates the impact of mining ban in Dharbandora taluka. Highlighting shifts from agriculture to mining and subsequent environmental consequences.\u003c/p\u003e\n\u003ch3\u003eStudy Area\u003c/h3\u003e\n\u003cp\u003eGoa, situated between latitudes 14° 53’ 57’’N and 15° 47’ 59’’ N and longitudes 73° 40’ 54’’ and 74° 53’11’’ E, covers an area of 3702 sq. km. Bounded by Maharashtra in the north, Karnataka in the east and south, and flanked by the Arabian Sea to the west, Goa is mainly divided into two districts, consisting of 12 talukas. (Kumbhar et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDharbandora, Goa’s youngest taluka, lies in the central-eastern regions. It comprises 5 villages comprising of 16 hamlets with undulating hills and valleys. Hydrological, the taluka plays a vital role in draining fresh water drainage. As per 2011 census it is estimated that the total population of taluka is 39,183 (Census, 2011)\u003c/p\u003e"},{"header":"Data Source and Methodology","content":"\u003cp\u003eLand use has a significant impact on forest characteristics, potentially causing large-scale changes in the context of forest cover. Land-use changes are recognized as fundamentally important to understanding a range of ecological, biophysical, social, and climate consequences (Drummond \u0026amp; Loveland, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Remote Sensing (RS) and Geographic Information System (GIS) are cruicial tools for monitoring LULC changes (Kayet \u0026amp; Pathak, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). For this study, NRSC IRS LISS III data (2012and 2022) with a spatial resolution of 23.5m was obtained from Bhoonidhi portal. Bands 2, 3 and 4 were analyzed using ERDAS and ARCGIS software.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial data sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDate of production\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eI.\u003c/p\u003e \u003cp\u003eII.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLiss III Liss III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2012-02-17\u003c/p\u003e \u003cp\u003e2022-10-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e23.5 m\u003c/p\u003e \u003cp\u003e23.5m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBhuvan Bhoonidhi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eImage Classification techniques (supervised and unsupervised) were applied, with NRSC level 1 classification is used to assign categories. Visual interpretation supported classification into six land use categories: agricultural land, barren land, built-up areas, natural vegetation, mining zones, and water bodies.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eMapping forest cover is important for assessing natural resource inventory and implement effective management strategies(Sharma et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in any given region. Satellite remote sensing, being at the forefront of technological advancements, has become indispensable in quantifying and monitoring deforestation activities. The study evaluates the landuse landcover change from mining to post mining phase in Dharbandora taluka between 2012 and 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the year 2012, a inclusive assessment of land use and land cover (LULC) in Dharbandora highlights a diverse landscape encompassing various types of landuses. As the region exhibited a significant stretch of densely vegetated areas spanning over 249.07 km\u0026sup2;, representing dense forest cover or lush vegetation. Conversely, water bodies were limited, covering merely 1.32 km\u0026sup2;, suggesting scarce aquatic resources. Mining activities were notably concentrated in the central region, accounting for 10.11 km\u0026sup2; of the total area. Barren land, covering 34.00 km\u0026sup2;, indicated areas devoid of vegetation and unsuitable for agricultural purposes. Built-up areas, encompassing 27.67 km\u0026sup2;, indicated urban or developed zones within the region. Agricultural land, comprising 56.07 km\u0026sup2;, represented a substantial portion of the landscape dedicated to farming activities. This assessment underscores the diverse land uses within Dharbandora, reflecting a mosaic of natural environments and human activities shaping the region's landscape in 2012.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 2022, significant changes were observed in the LULC of the region. Natural vegetation dominated the landscape, covering the largest area of 276.05 km\u0026sup2;, indicating the preservation of substantial ecological areas. Agricultural land followed, with 44.74 km\u0026sup2; dedicated to farming activities, showcasing the region's reliance on agriculture. However, the mining sector experienced a sharp decline, with only 2.11 km\u0026sup2; allotted to mining regions due to a ban imposed in Goa in 2014. This regulatory measure led to a rapid reduction in mining activities. Water bodies contributed 6.87 km\u0026sup2;, highlighting the presence of essential aquatic resources. Conversely, barren land and built-up areas covered 25.75 km\u0026sup2; and 22.71 km\u0026sup2;, respectively, signifying areas where natural vegetation has been lost or replaced by human infrastructure. These changes illustrate the dynamic interplay between human activities and natural processes shaping the region's landscape over the past decade.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand Use Land Cover Distribution and Change (2012 and 2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea 2012 (sq.km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea 2022 (sq.km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariance (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChange (sq.km}\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgricultural Land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-11.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBarren Land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-2.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-8.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBuilt Up\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-1.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-4.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNatural Vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e65.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e276.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e72.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;7.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e26.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMining\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-2.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWater bodies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;1.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e378.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e378.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe research delves into the intricate relationship between mining activities and the dynamic shifts in Land Use and Land Cover (LULC) patterns. Through meticulous analysis, six distinct LULC categories were delineated: Agricultural land, Barren land, Built-Up areas, Natural Vegetation, Mining zones, and water bodies. The study denotes notable changes in LULC both prior to onset of mining operations spanning the years from 2012 to 2022.\u003c/p\u003e \u003cp\u003eThe findings unveil a series of significant alterations in the landscape composition over the specified timeframe. Agricultural land, once occupying 14.82% of the area, witnessed a discernible decline, shrinking to 11.89% due to degradation of the land because of over-exploitation of mining activities with a variation of -3%, agricultural land was used as a dumping site for the mineral ore extracted from the mines and lack of source of irrigation. Similarly, Barren land and Built-Up areas experienced diminishing proportions, declining from 8.99% and 7.31% to 6.81% and 6.00%, which observed the variation of -2.28% and \u0026minus;\u0026thinsp;1.31% respectively. In contrast, the extents of Natural Vegetation and water bodies underwent marked expansion, increasing from 65.84% to 72.98% and 0.34% to 1.81%, with change of +\u0026thinsp;7.14% and +\u0026thinsp;1.47% during the same span wherein people/workers tend to migrate resulting in decline of the infrastructural activities further depleting the built-up land and inclination in vegetation and water bodies.\u003c/p\u003e \u003cp\u003eNotably, the analysis highlights a notable reduction in mining zones, with their coverage dwindling from 2.67% to a mere 0.55% (-2.12%). Such fluctuations underscore the profound impact of mining activities on the regional landscape, manifesting in significant alterations to land use and cover dynamics. These findings not only contribute to a deeper understanding of the environmental consequences of mining but also provide crucial insights for sustainable land management and conservation strategies within the study area.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe intricate relationship between land use and land cover (LULC) is crucial for understanding regional resources, especially in the face of unprecedented human interventions, particularly in forest ecosystems. India, including regions like Dharbandora taluka in Goa, has witnessed significant alterations in forest landscapes due to shifts from agriculture to mining, leading to profound changes in land use and cover. This transition not only degrades soil resources but also impacts local livelihoods, prompting a shift towards mining-based occupations. Utilizing Remote Sensing (RS) and Geographic Information System (GIS) technologies, alongside NRSC IRS data, allows for comprehensive analysis of LULC changes. The study of Dharbandora taluka's LULC changes from 2012 to 2022 reveals significant alterations, including declines in agricultural and built-up areas due to mining impacts, while natural vegetation and water bodies expand. Notably, mining zones experience a substantial reduction, underscoring the need for sustainable land management strategies to mitigate the profound impact of mining on regional land use dynamics and ensure long-term environmental sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThe author gratefully acknowledges the use of artificial intelligence (AI) tools in refinement of this research paper. AI-assisted platforms like Scolar AI were used.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBasuki B, Sulistiawati N, Verdian D, Naely Z (2023) The sensitivity level of landslide risk using Geographic Information System on the slopes of Mount Argopura, East Java, Indonesia. J Degraded Min Lands Manage 11(1):4949\u0026ndash;4959. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15243/jdmlm.2023.111.4949\u003c/span\u003e\u003cspan address=\"10.15243/jdmlm.2023.111.4949\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilyaminu H,et,al (2021) Monitoring Land Use and Land Cover Change of Forest Ecosystems of Shendurney Wildlife Sanctuary, Western Ghats, India. 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Environ Manage 60(3):544\u0026ndash;553. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00267-017-0894-4\u003c/span\u003e\u003cspan address=\"10.1007/s00267-017-0894-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTV R, S B (2018) J Remote Sens GIS 07(01). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4172/2469-4134.1000227\u003c/span\u003e\u003cspan address=\"10.4172/2469-4134.1000227\" 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":true,"hideJournal":true,"highlight":"","institution":"Parvatibai Chowgule College of Arts and Science (Autonomous)","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mining, NRSC, Landuse, Landcover, Remote Sensing","lastPublishedDoi":"10.21203/rs.3.rs-8962349/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8962349/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Recent decades, the forest cover has vulnerable to anthropogenic interventions which has resulted in degradation of forest. Goa, being a part of Western Ghats, a prime biodiversity hotspot, characterized by its unique biodiversity and ecological significance has witnessed dynamic changes in land cover patterns over the past few decades mainly due to infrastructure and development activities, of which mining has been prominent. Therefore, the paper aims to study the effect of mining ban (pre and post) on forest cover by considering two time periods, 2012 and 2022. Remotely sensed data from NRSC IRS LISS III (2012 and 2022) were obtained from Bhoonidhi portal. Image processing techniques, including unsupervised classification, were applied using ERDAS and ArcGIS software to delineate six LULC categories: agricultural land, barren land, built-up areas, natural vegetation, mining zones, and water bodies. Comparative analysis quantified spatial and temporal changes. Unplanned and uncontrolled exploration of mining ore has resulted in degradation of forest resources. The findings reveal substantial shifts in LULC following the mining ban. Natural vegetation increased from 249.07 km\u0026sup2; (65.84%) in 2012 to 276.05 km\u0026sup2; (72.98%) in 2022, while mining zones declined from 10.11 km\u0026sup2; (2.67%) to 2.11 km\u0026sup2; (0.55%). Water bodies expanded from 1.32 km\u0026sup2; to 6.87 km\u0026sup2;, indicating ecological restoration. Conversely, agricultural land, barren land, and built-up areas showed declines, reflecting reduced human activity and soil degradation linked to mining. The mining ban in Goa has positively influenced forest recovery and water body revival in Dharbandora taluka\u003c/p\u003e","manuscriptTitle":"Impact of Mining Ban: Degradation to revival Assessing Forest cover change in Dharbandora Goa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 14:06:50","doi":"10.21203/rs.3.rs-8962349/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59c1f01f-9af9-422e-9217-5d733cb6e555","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63563441,"name":"Environmental Engineering"},{"id":63563442,"name":"Forestry"}],"tags":[],"updatedAt":"2026-02-27T14:06:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 14:06:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8962349","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8962349","identity":"rs-8962349","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}