Forest Cover Mapping Research Articles

Improved forest cover mapping by harmonizing multiple land cover products over China

ABSTRACT Fine resolution land cover products are becoming increasingly more available for many regions. These products, however, may not meet the quality requirements of many applications. This study provides an approach for improving land cover mapping by leveraging existing products and clear view Landsat composites. Assessments using independent reference datasets revealed that the CAF-LC30 2020 product derived using this approach over China was more accurate than four existing land cover products. Its overall accuracy with field observations was 2.94% to 10.28% higher than those of the four existing land cover products in northeast China and was 2.10% to 8.18% better across China. It provided a more accurate representation of the land cover types in many regions where the existing land cover products had large classification errors. Forest areas calculated using the CAF-LC30 2020 for the 31 provinces and autonomous regions and municipalities (PARM) in mainland China were better correlated with those reported by the most recent National Forest Inventory (NFI) survey than areas calculated using the other four existing land cover products. Therefore, the CAF-LC30 2020 product should be a better alternative for understanding China’s forests in 2020 than the other four existing land cover products.

Beyond tree cover: Characterizing southern China's forests using deep learning

AbstractMapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time‐series of images. This knowledge is important, because ecological and economic values differ between forests. A new generation of low cost very high spatial resolution satellite images and the advent of deep learning enables improved abilities for distinguishing objects based on their structure, which could potentially also be applied to map different forest classes related to type, species and use. Here we use GF‐1 images at 2 m resolution and map six forest classes including different planted species for the karst region in southwest China, covering 806,900 km2. We compare the results with field data and show that accuracies range between 78% and 90%. We show a dominance of plantations (15%) and secondary forests (70%), and only remnants of natural forests (6%). The possibility to map forest classes based on their crown structure derived from low cost very high‐resolution satellite imagery paves the road towards sustainable forest management and restoration activities, supporting the creation of connected habitats, increasing biodiversity and improved carbon storage. No temporal information is needed for our approach, which saves costs and leads to rapid results that can be updated at a high temporal frequency.

Generation of the Forest Cover Map of the Qinghai–Tibet Plateau Based on the Multisource Dataset and Random Forest

The Qinghai–Tibet Plateau (QTP) plays a significant role in global climate change and biodiversity conservation. As the third pole of the Earth, it has a wide range and complex terrain. QTP has a vertical distribution of vegetation, and its forest ecosystems play a key role in the region. Forest extraction in this region is still a comprehensive problem because of the phenological periods of different forest types in distinct regions of the QTP and the characteristics of frequent rain and cloudy weather in the south. Taking these factors into consideration, multiple features, including reflectance, spectral indices, statistical backscattering coefficients, topographic slope, and aspect, derived from a multisource dataset incorporating optical remote sensing data, synthetic radar, and digital elevation models, were applied to extract forest in the QTP based on the random forest (RF) classification method. As more than 30 features were involved, the 5-folded cross-validation method was used to determine the optimal parameters and features for the RF model. Using 14,919 forest samples and a multifeature optimized RF classification model, a 10-m resolution forest cover map of QTP in 2021 was generated based on the Sentinel series of satellite datasets and digital elevation model datasets on the Google Earth Engine (GEE) platform. After verification, the overall accuracy of the forest cover map generated in this article is 98.3%, and the Kappa coefficient is 0.95, which is better than the European Space Agency (ESA) WorldCover forest layer.

Fusion of Sentinel 1 and Alos Palsar Data to Separate Palm Oil Plantations from Forest Cover Mapping using Pauli Decomposition Approach

In this work, a multi-sensor approach to extract misclassified oil palm plantations from forest cover map using a modified Pauli Decomposition technique is presented. The proposed method includes the generation of a primary forest cover map built using a Landsat-based Normalized Difference Fraction Index, and then the palm oil plantation is filtered out using scattering mechanisms through the Modified Pauli Decomposition technique based on the fusion of Sentinel 1 and Alos Palsar data. Accuracy assessment of the final product, produces accuracy values of 0.946 for forest class, while the classification accuracy for non-forest class is 0.92.

Mapping snow cover in forests using optical remote sensing, machine learning and time-lapse photography

The accurate spatial information of snow cover is useful for understanding the impact of global warming, and it is of high significance for hydrological disaster prediction, water resources management, and climate change research. The Normalized Difference Snow Index (NDSI) based approach has been used extensively around the world for mapping snow, and they displayed high accuracy in open areas. However, capturing snow cover in forests remains problematic due to the obstruction effects of the forest canopy, which causes the snow cover area to be seriously underestimated. In this paper, we present a new algorithm based on machine learning (ML) technology to improve the accuracy of binary snow cover (BSC) mapping in forests, using the remotely sensed surface reflectance and ground truth data. A time-lapse photography network with a two-hour resolution was established in the eastern Qilian Mountains in northwestern China to obtain the ground truth data both in forests and open areas. We trained Random Forests (RF) with the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from bands 1–7 to generate BSC results (RF-BSC). Then we evaluated RF-BSC and the NDSI-derived BSC maps with three different NDSI thresholds (i.e., 0.10, 0.29, and 0.40) against ground-truth data. The results indicate that the proposed algorithm has a high performance in forest BSC mapping in this area, compared to the NDSI-threshold approach. The RF-BSC can retrieve 67% of all real forest snow pixels, while the NDSI-based BSC can only detect 8–14%. We also find that the performance of the algorithm seems to be sensitive to changes in solar illumination conditions and forest coverage. This study suggests that machine learning with the fusion of optical remote sensing and ground-based observations is an effective approach for improving the accuracy of forest snow cover mapping at regional scales.

Forest Degradation Impact on Soil Organic Carbon - A Geospatial Approach

Soil organic carbon (SOC) is one of the soil health indicators. Forest soils are negatively impacted by biotic interference and the authors impact from forest canopy cover amenable to Earth Observation data. We mapped forest cover types and canopy density using remote sensing data. Sample design and size were based on the stratification and homogeneity map. The soil samples were collected from three depths using the pit method for analysis and SOC stock estimation. A linear and positive correlation between SOC and forest cover density was observed, indicating the negative impact of forest degradation on SOC. The maximum SOC stock of 61.16 ± SE3.96 Mg ha -1 was observed in top 30 cm soil in Sal forests with >80% canopy density and minimum (25.64 Mg ha -1 ) in Sal-mixed Teak forests with 40-60% canopy density. Total SOC ranged from 68.98 ± SE11.38 Mg ha -1 in the Hill Sal forest with 10-40% canopy density to 179.05 ± SE11.20 Mg ha -1 in pure Sal forests with >80% canopy density. It revealed that forest canopy degradation due to biotic interference caused loss of SOC in almost all the soil layers. SOC geospatial map shows areas for protection and reforestation/enrichment planting to enhance soil carbon stock.

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.

Mapping fractional woody cover in an extensive semi-arid woodland area at different spatial grains with Sentinel-2 and very high-resolution data

Woody canopy cover is an essential variable to characterize and monitor vegetation health, carbon accumulation and land–atmosphere exchange processes. Remote sensing-based global woody and forest cover maps are available, yet with varying qualities. In arid and semi-arid areas, existing global products often underestimate the presence of woody cover due to the sparse woody cover and bright soil background. Case studies on smaller regions have shown that a combination of collected field data and medium-to-high resolution free satellite data (e.g., Landsat / Sentinel-2) can provide woody cover estimates with practically-sufficient accuracies. However, most earlier studies focused on comparably small regions and relied on costly field data. Here, we present a fully remote sensing-based work-flow to derive woody cover estimates over an area covering more than 0.5 million km2. The work-flow is showcased over the Zagros Mountains, a semi-arid mountain range covering western Iran, the northeast of Iraq and some smaller fraction of southeast Turkey. We use the Google Earth Engine to create homogeneous Sentinel-2 mosaics of the region using data from several years. These data are combined with reference woody cover values derived by a semi-automatic procedure from Google® and Bing® very high resolution (VHR) imagery. Several random forest (RF) models at different spatial grains were trained and at each grain validated with iterative splits of the reference data into training and validation sets (100 repetitions). Best results (considering the trade-off between model performance and spatial detail) were obtained for the model with 40 m spatial grain which showed stable relationships between the VHR-derived reference data and the Sentinel-2 based estimates of woody cover density. The model resulted in median values of coefficient of determination (R2) and RMSE of 0.67 and 0.11, respectively. Our work-flow is potentially also applicable to other arid and semi-arid regions and can contribute to improve currently available global woody cover products, which often perform poorly in semi-arid and arid regions. Comparisons between our woody cover products with common global woody or forest-cover products indicate a clear superiority of our approach. In future studies, these results may be further improved by taking into account regional differences in the drivers of woody-cover patterns along the environmental gradient of the Zagros area.

Maintaining forest cover to enhance temperature buffering under future climate change

Forest canopies buffer macroclimatic temperature fluctuations. However, we do not know if and how the capacity of canopies to buffer understorey temperature will change with accelerating climate change. Here we map the difference (offset) between temperatures inside and outside forests in the recent past and project these into the future in boreal, temperate and tropical forests. Using linear mixed-effect models, we combined a global database of 714 paired time series of temperatures (mean, minimum and maximum) measured inside forests vs. in nearby open habitats with maps of macroclimate, topography and forest cover to hindcast past (1970–2000) and to project future (2060–2080) temperature differences between free-air temperatures and sub-canopy microclimates. For all tested future climate scenarios, we project that the difference between maximum temperatures inside and outside forests across the globe will increase (i.e. result in stronger cooling in forests), on average during 2060–2080, by 0.27 ± 0.16 °C (RCP2.6) and 0.60 ± 0.14 °C (RCP8.5) due to macroclimate changes. This suggests that extremely hot temperatures under forest canopies will, on average, warm less than outside forests as macroclimate warms. This knowledge is of utmost importance as it suggests that forest microclimates will warm at a slower rate than non-forested areas, assuming that forest cover is maintained. Species adapted to colder growing conditions may thus find shelter and survive longer than anticipated at a given forest site. This highlights the potential role of forests as a whole as microrefugia for biodiversity under future climate change.

Tracking forest loss and fragmentation between 1930 and 2020 in Asian elephant (Elephas maximus) range in Nepal

Forest cover is the primary determinant of elephant distribution, thus, understanding forest loss and fragmentation is crucial for elephant conservation. We assessed deforestation and patterns of forest fragmentation between 1930 and 2020 in Chure Terai Madhesh Lanscape (CTML) which covers the entire elephant range in Nepal. Forest cover maps and fragmentation matrices were generated using multi-source data (Topographic maps and Landsat satellite images of 1930, 1975, 2000, and 2020) and spatiotemporal change was quantified. At present, 19,069 km2 forest cover in CTML is available as the elephant habitat in Nepal. Overall, 21.5% of elephant habitat was lost between 1930 and 2020, with a larger (12.3%) forest cover loss between 1930 and 1975. Area of the large forests (Core 3) has decreased by 43.08% whereas smaller patches (Core 2, Core 1, edge and patch forests) has increased multifold between 1930 and 2020. The continued habitat loss and fragmentation probably fragmented elephant populations during the last century and made them insular with long-term ramifications for elephant conservation and human-elephant conflict. Given the substantial loss in forest cover and high levels of fragmentation, improving the resilience of elephant populations in Nepal would urgently require habitat and corridor restoration to enable the movement of elephants.

Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar

Documenting the impacts of climate change and human activities on tropical rainforests is imperative for protecting tropical biodiversity and for better implementation of REDD+ and UN Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning (ML) have provided improved mapping of fine-scale changes in the tropics. However, approaches so far focused on feature extraction or the extensive tuning of ML parameters, hindering the potential of ML in forest conservation mapping by not using textural information, which is found to be powerful for many applications. Additionally, the contribution of shortwave infrared (SWIR) bands in forest cover mapping is unknown. The objectives were to develop end-to-end mapping of the tropical forest using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and to evaluate human impact on the environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. FCNN (U-Net) using spatial/textural information was implemented and compared with feature-fed pixel-based methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). Results show that the FCNN model outperformed other models with an accuracy of 90.9%, while SVM, RF, and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. When SWIR bands were excluded from the input data, FCNN provided superior performance over other methods with a 1.87% decrease in accuracy, while the accuracies of other models—SVM, RF, and DNN—decreased by 5.42%, 3.18%, and 8.55%, respectively. Spatial–temporal analysis showed a 0.7% increase in Evergreen Forest within the BNR and a 32% increase in tree cover within residential areas likely due to forest regeneration and conservation efforts. Other effects of conservation efforts are also discussed.

Forest cover change in Onigambari reserve, Ibadan, Nigeria: Application of vegetation index and Markov chain techniques

Forest cover change (FCC) varies globally and is thus considered as one of the drivers of climate change. The present study identified the pattern of the FCC for the years 2010 and 2020 using vegetation index and Markov chain techniques. The Markov chain (MC) was utilized to predict the forest cover map for the year 2030. The vegetation index of Landsat 7 Enhanced thematic mapper plus (ETM+) and Landsat 8 Operational land images (OLI) were employed to assess the forest cover loss for the years 2010 and 2020. The validation result shows that the accuracy of the predicted forest cover map is more than 75 percent (%). The prediction result shows that if the current human activities continue such as deforestation, the forest cover will continue to be endangered and thus leading to a decrease in dense forest, plantation, and sparse vegetation by 20.9%, 16.1%, and 20% respectively. Hence, there is an urgent need to integrate bottom-up and participatory approaches between agriculture activities and forestry for socioeconomic development. This study will ensure sustainable forest management by assisting society, government and stakeholders.

Forest Covers of West Bengal: A District-Wise Review

Forest cover mapping exercise for the state ofWest Bengal reflects the districtwise status of forests and its present trends and provides inputs for monitoring of forests and its restoration. District-wise statistics assessed from the biennial reports of the Forest Survey of India reveal the changes in the forest cover for the districts ofWest Bengal from time to time. Specifications of forest related information like forest types, and myths and history are included for all districts. District-wise forest cover change matrix reflects dwindling of the South Bengal's forest cover alarmingly from the eighties of the last century; yet a shift to joint forest conservation by making stakeholders in forestry initiatives and social forestry creation managed by the forest department has yielded the present results. Plantation under the social forestry scheme in almost all the districts of West Bengal has started showing results for the growing stocks. District-wise break-up revealed that South 24 Parganas, Uttar Dinajpur, Murshidabad and Howrah have recorded decrease in forest cover, while Bankura, Paschim Medinipur, Purulia and Birbhum have recorded a rise.There is a net increase of 54.51 sq km in the forest cover from the reported area in India State of Forest Report 2019 in comparison to the data available in the assessment report published by Forest Survey of India in 2017.

Detecting vulnerability of humid tropical forests to multiple stressors

Detecting vulnerability of humid tropical forests to multiple stressors

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.

Assessing the tropical forest cover change in northern parts of Sonitpur and Udalguri District of Assam, India

Sonitpur and Udalguri district of Assam possess rich tropical forests with equally important faunal species. The Nameri National Park, Sonai-Rupai Wildlife Sanctuary, and other Reserved Forests are areas of attraction for tourists and wildlife lovers. However, these protected areas are reportedly facing the problem of encroachment and large-scale deforestation. Therefore, this study attempts to estimate the forest cover change in the area through integrating the remotely sensed data of 1990, 2000, 2010, and 2020 with the Geographic Information System. The Maximum Likelihood algorithm-based supervised classification shows acceptable agreement between the classified image and the ground truth data with an overall accuracy of about 96% and a Kappa coefficient of 0.95. The results reveal a forest cover loss of 7.47% from 1990 to 2000 and 7.11% from 2000 to 2010. However, there was a slight gain of 2.34% in forest cover from 2010 to 2020. The net change of forest to non-forest was 195.17 km2 in the last forty years. The forest transition map shows a declining trend of forest remained forest till 2010 and a slight increase after that. There was a considerable decline in the forest to non-forest (11.94% to 3.50%) from 2000–2010 to 2010–2020. Further, a perceptible gain was also observed in the non-forest to the forest during the last four decades. The overlay analysis of forest cover maps show an area of 460.76 km2 (28.89%) as forest (unchanged), 764.21 km2 (47.91%) as non-forest (unchanged), 282.67 km2 (17.72%) as deforestation and 87.50 km2 (5.48%) as afforestation. The study found hotspots of deforestation in the closest areas of National Park, Wildlife Sanctuary, and Reserved Forests due to encroachments for human habitation, agriculture, and timber/fuelwood extractions. Therefore, the study suggests an early declaration of these protected areas as Eco-Sensitive Zone to control the increasing trends of deforestation.

Forest Mapping Using Classification of Sentinel-2A Imagery for Forest Fire Danger Prediction: a Case Study

Timely and accurate effective forest cover mapping is a prerequisite for predicting forest fire danger. Remote sensing data have an undoubted advantage in mapping the forest cover of territories. The paper compares six trained classification algorithms in order to select the best Sentinel 2A image for a typical forestry in the Baikal region. The conducted comparative analysis has included comparison of the classification accuracy by parametric and nonparametric methods with the default parameters set in the ENVI software. The training sample (Samples data) has been created based on forest management materials. The overall accuracy and the Cohen's kappa coefficient have been used to assess general performance of each algorithm. The accuracy of mapping individual vegetation classes has been assessed using the accuracy of the producer and the user and their combination of F-score. The results of the study can be used when choosing a method for classifying forest vegetation in the Baikal zone and other similar areas by satellite imagery in order to predict forest fire danger.

Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016). TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

A new remote sensing-based carbon sequestration potential index (CSPI): A tool to support land carbon management

Integrating remote sensing into assessments of carbon stocks and fluxes has advanced our understanding of how global change affects landscapes and our capacity to support decision making about forest management. However, there remains a lack of detailed and actionable analyses conducted across widely ranging environmental conditions that are appropriate for tactical planning. We used airborne laser scanning data and multi-source satellite imagery to estimate forest aboveground carbon density and gross primary production, and to map forest cover across the main Hawaiian Islands. We used these measures to develop the Carbon Sequestration Potential Index (CSPI), which identifies where the potential for carbon sequestration following afforestation would be highest within a complex landscape of 304 management units. Variation in CSPI was high across islands and between ecosystems, with low values for cool, dry and largely intact forest systems and high values for warm, wet and largely non-forested systems. The CSPI provided a rapid, spatially-explicit and actionable assessment of Hawaiian forest reserves, which can help stewardship agencies contribute to state carbon neutrality goals through climate-smart and science-driven prescriptions that encompass conservation to restoration.

Forest landscape and its ecological quality: A stepwise spatiotemporal evaluation through patch-matrix model in Jhargram District, West Bengal State, India

Landscape conversion becomes a continuous process in a natural landscape for any strategic development. In a forest landscape mosaic, the conversion from non-forest land to forest land implies a constructive approach. Various bio-geographic processes are enriched and developed when the land was converted to forest land in a given landscape matrix. The present study evaluated how the increased forest cover improves the ecological quality of forest in Jhargram District of West Bengal State, India, from 1985 to 2015. The quality of forests includes dominance, fragmentation and connectivity, which are the basis ecological indicators of habitat structure. To address this issue, we extracted forest cover maps of 1985 and 2015 from land use/land cover classification. A grid framework was overlaid on these forest cover maps for patch-matrix model analysis. Reliable landscape ecological indices were used for the measurements of forest landscape quality in 1985 and 2015. Then a simple linear regression model was used to compare the results. Temporally, forest cover increased in Jhargram District from 1985 to 2015. The comparison of measurement indices depicts that although only a small amount of land was changed into forest land in the study area, this small change has greatly improved the structural compositional quality of the forest land. Compared with 1985, the forest land area increased by about 6930.56 ​hm2 in 2015. This increased forest cover improved the basic landscape ecological characters, such as inter patch connectivity, forest core area, forest habitat dependence, forest habitat dominance and forest edge effect. As a result, the ecosystem function in Jhargram District has been improved, which again attracts wildlife and enriches biodiversity.