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Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s. [ABSTRACT FROM AUTHOR]

In recent years, Togo's protected areas have been subject to a worrying process of anthropisation. This has led to a decline in forest ecosystems, their biodiversity and their functions. The general objective of this study is to assess the state of conservation of the classified forest of Amou-Mono between 1986 and 2021. More specifically, it aims to: (i) characterize the dynamics of land use from 1986, 2000, 2013 and 2021 and, (ii) assess the impact of changes in land use on the potential for carbon sequestration. A multi-date classification of Landsat images for the years 1986, 2000, 2013 and 2021 made it possible to quantify and assess changes in occupation. The dynamics of land use between 1986 and 2021 is mainly reflected by a regression of dense/riparian forests (81.40%) and open forests/wooded savannahs (67.79%) and the progression of wooded/shrubby Savannah (172.93%). Anthropogenic formations: Fields/fallow and Agglomerations/Bare soils, absent in 1986, occupy in 2021 respective proportions of 38.1 % and 2.19%. Human activities, particularly logging and agricultural clearing, are causing fragmentation and regression of forest ecosystems. Unless action is taken to reverse the trend, the conservation of biodiversity and ecosystem functions in this classified forest will be increasingly threatened. [ABSTRACT FROM AUTHOR]

Land use/cover change (LULCC) is an integral part of global environmental change and is influenced by both natural and socioeconomic factors. This study aims to comprehensively analyze land use and land cover (LULC) in Kwazulu-Natal and Mpumalanga provinces in eastern South Africa from 1995 to 2020 and to identify the driving force behind LULCC. Utilizing Landsat series satellite imagery as a data source and based on the Google Earth Engine (GEE) platform and eCognition software 9.0, two different classification methods, pixel-based classification and object-oriented classification, were adopted to gather LULC data every five years. The spatiotemporal characteristics of the data were then analyzed. Using an optimal parameter-based geodetector (OPGD), this study explored the driving factors of LULCC in this region. The results show the following: (1) Of the two classification methods examined, the object-oriented classification had higher accuracy, with an overall accuracy of 80–90%. The pixel-based classification had lower accuracy, with an overall accuracy of 62.33–72.14%. (2) From 1995 to 2020, the area of farmland in the study area showed a fluctuating increase, while the areas of forest and grassland declined annually. The area of constructed land increased annually, whereas the areas of water and unused land fluctuated over time. (3) Socioeconomic factors generally had greater explanatory power than natural factors, with population growth and economic development being the main drivers of LULCC in the region. This study provides a reliable scientific basis for the formulation of sustainable land resource development strategies in the area, as well as for the management and implementation of urban and rural planning, ecological protection, and environmental governance by relevant departments. [ABSTRACT FROM AUTHOR]

Spatially explicit reconstruction of historical land cover change is a prerequisite for a more comprehensive understanding of environmental changes. Anthropogenic activities have dramatically altered the land cover of the conterminous United States (CONUS), encroaching heavily on the primary vegetation. However, few datasets exist that depict the historical trajectory of forest and grassland cover changes in CONUS over the last millennium, and previous efforts have only focused on reconstructions for the last four centuries. By integrating remote sensing-derived land use/cover change (LUCC) data and potential vegetation data, we determined the potential extent of natural forest (PENF) and grassland (PENG) in CONUS. Based on a qualitative analysis of the trends and driving forces of forest and grassland changes, we devised a method of subtracting reconstructed historical cropland (1000–2000 AD) and built-up land (1850–2000 AD) from PENG and PENF to reconstruct a 5 min × 5 min grid dataset of forest and grassland cover at 13 time-points over the past millennium. The results showed that forest and grassland cover in CONUS underwent a slow decline (1000–1600 AD), an accelerated decline (1600–1800 AD), a dramatic decline (1800–1950 AD), and finally, a recovery (1950–2000 AD) over the study period. The modelled forest fraction decreased from 49% in 1000 AD to 33% in 2000 AD, representing a 32% area reduction, whereas the modelled grassland fraction decreased from 37% to 22%, representing a 42% area reduction. The reduction occurred primarily in the last 200 years, with forest and grassland reductions accounting for 86% and 97% of the total reduction over the millennium, respectively. Spatially, more than 80% of the land was originally covered by forests and grasslands, and the loss occurred mainly in the eastern CONUS and Great Plains over the past millennium. After the 1930s, farmland abandonment began in central and eastern CONUS, simultaneously with environmental protection laws. Federal government regeneration programs for forest and grassland resources and the Shelterbelt Project all contributed to a slowdown in forest and grassland decline and recovery in cover. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) is a major concern of global environmental change, particularly the case, in developing countries. In the Central Ethiopian Rift Valley (CRV), unprecedented changes in LULC have been observed over the last decades. This study aims to scrutinize the dynamics of the CRV over a timespan of three decades (1984–2013). Satellite imageries were acquired from Multispectral Scanner System, Thematic Mapper, and Enhanced Thematic mapper for the years 1984, 1995, and 2013 respectively. The data were obtained from the United States Geological Survey and Ethiopian Geospatial Information Institute. In addition, Focus Group Discussions and Key Informants Interviews were undertaken to support analysis and interpretation of LULC anomalies, identify drivers, and associated impacts. Geospatial technologies were used to analyze the extent of LULC change across the study period. An object-oriented supervised classification and a change detection technique were applied to compartmentalize the change into six LULC classes. Results revealed that farmlands and built-up areas have shown continuous progressive expansion at the expense of bareland and forestland. Farmland and built-up area in 1984 were expanded from 33.3% and 2.6% to 40.6% and 8.2% in 2013, respectively. In contrast, forestland and bareland coverages in 1984 were dwindled from 11.7% and 17.7% to 4.2% and 10.1% in 2013, respectively. Comparatively, shrubland and waterbody were also slightly augmented by 1.9% and 0.2% from 1984 to 2013. Therefore, the result suggested that policymakers and development actors must take appropriate actions to minimize the progressive decline of forest cover in the milieu. [ABSTRACT FROM AUTHOR]

In the last 50 years, forest disturbances, caused mainly by insect outbreaks and windstorms, had a significant impact on the subalpine Norway spruce (Picea abies (L.) H. Karst.) stands across Europe. The high intensity of these factors often led to complete dieback of existing forest stands, as in Gorce National Park (Southern Poland). The aim of this study was to monitor land cover changes in subalpine Norway spruce stands and their dynamics in Gorce NP in the years 1977–2020 (43 years), with the use of archival remote sensing data. The study area was divided into two subareas: A—the Kudłoń and B—the Jaworzyna range. Changes were tracked in six defined land cover classes, based on available aerial orthophotos and Landsat (NASA) imagery, with the help of the authors' photointerpretation key. The results showed that almost 50% of old-growth Norway spruce stands died in the analyzed time period (50.9% in subarea A; 48.8% in subarea B). However, young forests appeared in almost 17% of the study area (20.7% and 14.2% in subarea A and B, respectively). The dynamics of land cover changes were different for the analyzed subareas; in subarea A Norway spruce dieback processes weakened at the end of the analyzed time period, whereas in subarea B they maintained high intensity. The process of old-growth Norway spruce stands dieback is still occurring in Gorce NP, but it does not result in the disappearance of the whole subalpine spruce forest ecosystem but is rather a generational change, due to emerging young forests. [ABSTRACT FROM AUTHOR]

Land includes vegetation and water bodies and provides the basis for human livelihoods through primary production, food and freshwater supply, and multiple other ecosystem services. The last three decades have recorded frequent drought events as well as rapid population growth, which has often resulted in adverse land use and land cover change (LULCC) in the Sahel of Sub-Saharan Africa. In order to propose sustainable land management strategies, it is a prerequisite to investigate the rate of LULCC and its driving factors in specific locations. This study investigated the case of Wacoro municipality in Mali using a combined approach of remote sensing, Geographic Information Systems, and focus group discussions. Satellite images and local people's perceptions on LULCC and drivers were collected and analyzed for the years 1990, 2000, 2010, and 2020. We found that the study area faced a rapid decrease in wooded savannah that was degraded and converted to shrub savannah and later to farmland and settlement. Changes were directly or indirectly related to the rapid population growth, high cotton price (which encouraged cropland expansion), drought, firewood extraction, and charcoal production, which was exacerbated by poverty. We suggest promoting integrated land management strategies that consider current and future livelihood needs and preserve the environment for the benefits of future generations. New agricultural policies, such as cotton price incentives, should always be accompanied by an assessment of their potential environmental impacts and design of adequate mitigation measures. [ABSTRACT FROM AUTHOR]

The wet evergreen forest ecological zone of Ghana is undergoing notable structural changes, although most maps erroneously depict the area to be an intact forest. Several hectares of plantation agriculture such as rubber, oil palm, and cocoa are replacing the natural vegetation and habitats, thereby threatening indigenous biodiversity. This study aimed to assess the effects of tree monocrop proliferation on landscape transitions between 1986 and 2020 in the Wassa East District of Ghana. The ISODATA clustering technique was used to produce land cover category maps from Landsat images of 1986, 2002, and 2020. A post-classification change detection technique resulted in transition matrices which were used for the computation of land cover transition intensities over 34 years. The results showed that the landscape was dominated by forests in 1986. Still, the forest continuously declined by 34% (1% annually) in 2020 while all other land cover types increased in both periods with cocoa covering 23% oil palm covering 14%, and rubber covering 2% of the land area. These conversions in the land area are important, especially since the closed forests usually transition to monocrops through the intermediate process: food crop farming. This information is essential for decision making on land development and biodiversity conservation. [ABSTRACT FROM AUTHOR]

Land cover change for agriculture is thought to be a major threat to global biodiversity. However, its ecological impact has rarely been quantified in the Northern Hemisphere, as broad-scale conversion to farmland mainly occurred until the 1400s–1700s in the region, limiting the availability of sufficient data. The Ishikari Lowland in Hokkaido, Japan, offers an excellent opportunity to address this issue, as hunter–gatherer lifestyles dominated this region until the mid-nineteenth century and land cover maps are available for the period of land cover changes (i.e. 1850–2016). Using these maps and a hierarchical community model of relationships between breeding bird abundance and land cover types, we estimated that broad-scale land cover change over a 166-year period was associated with more than 70% decline in both potential species richness and abundance of avian communities. We estimated that the abundance of wetland and forest species declined by greater than 88%, whereas that of bare-ground/farmland species increased by more than 50%. Our results suggest that broad-scale land cover change for agriculture has led to drastic reductions in wetland and forest species and promoted changes in community composition in large parts of the Northern Hemisphere. This study provides potential baseline information that could inform future conservation policies. [ABSTRACT FROM AUTHOR]

Evaluation of Land Use Land Cover (LULC) changes play a vital role in understanding the landscape dynamics that have been influencing climate, biodiversity, hydrology, and ecology of a region. The information of temporal LULC aids decision-makers in framing sustainable land use policies for nature conservation. Anthropogenic pressure, especially unplanned developmental activities, has contributed towards fragmenting contiguous forests, thus affecting their structure and loss of habitat for endemic taxa. LULC changes in the Bellary district, Karnataka have been assessed through temporal remote sensing data. Classification of remote sensing data for estimating the spatial extent of land uses has been done through supervised machine learning algorithms namely random forest (RF), support vector machine (SVM), and parametric maximum likelihood classifier (MLC). The performance of these algorithms was evaluated through accuracy assessments. Results reveal that RF has the highest overall accuracy (88.94%) and Kappa value (0.76) compared to overall Kappa of MLC (85.51%, 0.74) and SVM (85.47%, 0.63). Based on this, RF was considered for temporal data analyses, which highlighted the decline of forest cover from 2.61% (1973) to 0.74% (2022). The built-up has increased from 0.27% (1973) to 2.43% (2022), and agriculture from 68.21% (1973) to 84.95% (2022). Fragmentation of contiguous forests is evident from the decline in the interior or intact forests from 6.73% (1973) to 2.41% (2022) and the increase in the non-forest areas such as built-up, agriculture, etc., amounting now to 89.81%. Results highlight the need for immediate policy interventions for the conservation and protection of the remnant forest patches. [ABSTRACT FROM AUTHOR]

The Accra Plain of Ghana is experiencing rapid urbanization, but there is scant information on its impact on local biodiversity. We assessed the changes in land use/land cover of the Accra Plain since 1991 and evaluated how the observed changes have influenced local small mammals in forest fragments. We applied supervised classification and intensity analysis time-series Landsat imagery data to assess land use/land cover changes between 1991 and 2017. Small mammals were surveyed in two forest fragments, the Pinkwae and Adumanya forests from June 2019 to January 2020, using capture-mark-recapture technique. We compared our data with baseline data gathered in 1991–1992, when large areas of the city remained mostly undeveloped. Our data revealed that the urban area has increased by 832%, while the forest area declined by 85% between 1991 and 2017. The Pinkwae and Adumanya forests, which covered 120 and 1.5 ha, respectively, in 1991 have each been reduced to < 1 ha. We found changes in the small mammal species composition in the forest fragments, but not species richness due to species turnover. Grammomys poensis and Dephomys defua are first records for the Accra Plain. Our data suggested that small forest fragments within the urbanizing landscape are important for maintaining the local small mammal species. For the conservation of local small mammals in urbanizing landscapes, it is important to maintain the greatest possible number of small forest fragments and establish policies that prevent forest remnants from being further depleted. [ABSTRACT FROM AUTHOR]

Delivery of ecosystem services is strongly affected by changes in the land use/land cover (LULC) of an area. In this study, we analyze spatiotemporal changes in LULC of the rapidly changing Bagmati River Basin (BRB) of Nepal during 1988–2018 using Landsat satellite images. We also quantify carbon storage in different physiographic regions and LULC classes using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and assess economic valuation of carbon using the benefit transfer method. According to our analysis, there were increases in urban/built-up (247.5%), barren land (109.5%), shrub land (32.4%), and declines in forest cover (− 6.2%), cultivated land (− 4.1%), waterbodies (− 30.3%), sand (− 29.2%), and grass cover (− 10.6%) during the study period. As a result of these changes in LULC, carbon storage declined from 31.4 million tons year−1 in 1988 (worth 157.0 million USD) to 30.8 million tons year−1 (154.1 million USD) in 2018 with the total loss of 2.9 million USD. The largest decline in stored carbon was observed in Tarai and Dun valleys, from 6.8 to 6.5 million tons (− 1.4 million USD) followed by Churia, from 7.8 to 7.6 million tons (- 1.1 million USD). Increases in carbon storage were observed in urban/built-up and shrub land areas and declines in cultivated land, forest, barren land, waterbodies and grass land. The results of LULC change and estimated carbon stock in BRB provides a baseline for planners and policy makers to formulate appropriate plans to sustainably manage the region's land cover and to mitigate carbon loss. [ABSTRACT FROM AUTHOR]

Restoring degraded forest is essential if we are to reduce human pressure on natural ecosystems and their biodiversity. Forests were nationalized in 1957 in Nepal and as a consequence, forest cover declined from 45% in 1964 to just 29% in 1994. However, as its response, sectoral plans and policies, particularly introduction of community-based forest management programs since the 1980s and conservation activities resulted in large scale forest cover restoration. Here, we examined the forest cover change in the Gandaki River Basin (GRB), the catchment with the largest altitudinal variation (ranging from ± 93 to 8167 m) and environmental and ecological significance. To see how forests have changed since then, we analyzed snapshots of spatiotemporal, ecological and physiographic changes in forest cover, and forest type at decadal intervals from 1996 to 2016 using Landsat 5 and 8 satellite images. We observed an overall gain in forest cover of 207 km2, from 7571 km2 (34.4% of the total area) in 1996 to 7778 km2 (35.3%) in 2016. Of the 21 forest cover types identified, the greatest forest coverage during 2016 was of Schima-Castanopsis forest (25.9%) and hill sal forests (16.4%). In terms of physiographic zones, land below 500 m (Tarai) where most people live, witnessed gradual declines in forest cover, in contrast to large increases in forests above 500 m. Historical examination of forest cover at ecological and physiographic scales helps to identify the elevation-wise distribution of forest resources, vegetation composition, ecosystem characteristics, anthropogenic pressure upon vegetation, and hence the overall influence of LULC upon the environment. These outputs will assist planners, policy makers, and researchers in their formulation of effective basin wide plans and policies to ensure the protection of basin level biodiversity and ecosystem function. [ABSTRACT FROM AUTHOR]

Land use is a dynamic phenomenon, changing with time and space. Land use/land cover (LULC) information and its periodic changes has become crucial to carry out the prediction to the dynamical change of land use. This study analyzed the spatial and temporal changes in land use that has taken place in Nagaland over the last 10 years (2005–2016). It has been observed that the dynamic change in land use and land cover has been mainly caused by the traditional practice of jhum cultivation, also known as shifting cultivation in Nagaland, which constitute about 81% of the total agricultural land. As a result, there is a decline in forest area by 593.87 km2 (5.66%) from 2005–2006 to 2015–2016. Concurrently, abandoned jhum land and scrubland has increased by 11.72% and 24.89%, demonstrating the decreased in jhum/ shifting cultivation cycle. The loss of forest in the last decade was attributed to ever increasing population, putting pressure on demand of jhum/ shifting cultivation and other anthropogenic activities. The degradation of forest is ever increasing, which calls for intervention of appropriate technology and holistic approach to address this issue. [ABSTRACT FROM AUTHOR]

After the year 2010, a century of rapid decline in Philippine forest cover was reversed due to a deliberate National Greening Program (NGP). Drivers that can sustain or counter this increase can be better investigated through the help of land change models. However, such models are not yet in mainstream use for planning in the Philippines. Hence, this study used two models – FOREST-SAGE and GEOMOD – to simulate forest conditions based on anthropogenic drivers and to evaluate model applicability in the Philippines. The performance of each model was assessed using the root-mean-square error (RMSE), mean absolute error (MAE), Kappa, national average tree cover percentage, and common deforestation hotspots with reference datasets. Validation with Climate Change Initiative Land Cover dataset (CCI-LC) yielded similar results between the models: 2015 tree cover maps with RMSE of 22–25% tree cover, MAE of 10–12% tree cover, and moderate agreement with reference map based on Kappa (0.4–0.6); and 2010–2015 change maps with RMSE of 8–9% tree cover, MAE of 1–2% tree cover, and agreement due to chance based on Kappa (0.01–0.03). Validation with MODerate Resolution Imaging Spectroradiometer-Vegetation Continuous Field (MODIS-VCF) maps yielded similar MAE results (10% tree cover) between the models. Validating FOREST-SAGE end-time and change maps with MODIS-VCF yielded better RMSE results than GEOMOD (RMSE of 13% tree cover for FOREST-SAGE; 22% tree cover for GEOMOD). However, GEOMOD tree cover maps yielded better Kappa than FOREST-SAGE (0.60 for GEOMOD; 0.01 for FOREST-SAGE). Results suggest that FOREST-SAGE is more applicable in the Philippines for provincial extent studies that aim to quantitively track forest cover change, while GEOMOD is more applicable for national extent studies that use categorical data. Results also suggest that input parameter settings must be improved to simulate spatial distribution of forest cover. [ABSTRACT FROM AUTHOR]

The systematic decline of land quality due to the mismatch between land quality and land use is called land degradation. It is the consequences of different physical factors but accelerated by human activities. The processes and factors of land degradation are different in different morpho-climatic regions of the world. Present study is primarily concerned in identifying the factors and status of land degradation in a tropical plateau region. Tropical plateaus are one of the most water erosion related land degraded areas of world. Chotanagpur plateau in tropical eastern India is one of the vulnerable soil degraded areas of the tropical environment. The Precambrian geological formation, undulating topography, immature soil, increasing aridity sets natural settings for land degradation in tropical plateau areas. However, induced degradation results from inappropriate land use, clearance of forest land and heavy land pressure related anthropogenic factors accelerates the processes of land degradation. Kangsabati River basin of eastern Chotanagpur plateau bears all of the above characteristics has been taken for present study as a degraded tropical plateau region. Water erosion and vegetal degradation are the major factors of land degradation in Kangsabati basin. The physical factors responsible for land degradation in the studied area includes physiographic formations, edaphic maturity, hydrologic characteristics, and vegetation cover dynamics etc. This land degradation processes is accelerated by different anthropogenic factors like, land use land cover dynamics, deforestation, agricultural pressure. Multispectral remote sensing data, digital elevation data, thematic maps, recorded climatological data, secondary data and field observation have been utilised to understand the different factors of land degradation in a typical tropical plateau region. The less developed soil profile over Precambrian geology with undulated landforms causes high soil erosion in upper plateau proper regions of the basin. High agricultural pressure, forest and groundwater decline with associated land use land cover dynamics creates decline of land quality in lower basin regions of tropical plateau. The varying intensities of land degraded areas for the basin has been identified through the multiplication of different physical and anthropogenic factor overlaying rasters in GIS environment. Before multiplying, each of these input factor rasters has assigned with weightage after considerable importance of land degradation. The lower basin area with accelerated human interference is the most vulnerable land degraded area for the Kangsabati River basin. [ABSTRACT FROM AUTHOR]

This study presents an integrated model based on cellular automata for assessing and simulating land use/cover changes and their impact on the environment. Satellite images from Landsat TM and ETM + sensors from the time period of 1985 to 2014 were applied. Seven static and five dynamic variables were applied. These included elevation, slope, aspect, soil salinity, soil texture, distance from rivers, and roads, and distance from the five classes of land use. The model was validated by a fuzzy reciprocal similarity method. The results showed that this model is suitable for simulating changes in periods of less than 15 years and patches with areas greater than 25 hectares. The model was run for 15 years, beginning with the year 2014. The results for the study area predict that settlement areas will expand; agricultural land, rangeland and barren areas will decline; and forests will remain unchanged until 2029. [ABSTRACT FROM AUTHOR]

China's forests were severely degraded by human activities during the latter half of the 20th century. Therefore, China enacted ambitious programs of natural forest protection and afforestation to protect and expand forests. Yet it is unclear how the programs, especially the designation of forest reserves, have affected forest cover and fragmentation. We evaluated the effectiveness of China's national forest reserves designated since 1990 in conserving forests, by analyzing four forest metrics (i.e., percentage forest cover, mean forest patch size, mean forest patch radius of gyration, and forest patch cohesion index) derived from a newly produced 30 m annual China land cover dataset from 1990 to 2019. We found that overall forest cover increased and fragmentation decreased from baseline years, when reserves were designated, to 2019 in both reserves and their surrounding areas, and only the increase in forest cover relative to baseline was significantly greater in reserves than in surrounding areas. The designation time of reserves under national protection had no considerable effect on changes in the four metrics, but for zonation, the core zone showed a significantly higher increase in forest patch cohesion index relative to baseline than the buffer and transition zones. Nevertheless, forest cover declined and fragmentation increased in highly forested reserves, suggesting destructive human activities and ineffective management. Thus, forest protection and regeneration programs were moderately successful. We recommend that there is significant improvement needed to ensure greater protection of existing forests and reduction of threats to promote effective management. • The nature reserve program showed effectiveness in conserving forests in China. • There was better protection and recovery of forest cover than reducing fragmentation. • Destructive human activities may cause forest declines in highly forested reserves. [ABSTRACT FROM AUTHOR]

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF. [ABSTRACT FROM AUTHOR]

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests. [ABSTRACT FROM AUTHOR]

• Forest will decline more than any other land use/cover category by 2030. • Urban area will increase more than any other land cover category by 2030. • Decreasing streamflow is expected under moderate- and high-emissions warming scenarios. • Precipitation contributes more significantly to change in streamflow under future scenarios. • Streamflow rate of change is an effective indicator of future hydroclimatic relationships. Previous studies have identified the importance of simulating and quantifying the relationship between hydrologic variation and climate change under historical scenarios at regional and continental scales. However, robust demonstration of the potential of combining consistent land use/cover change (LUCC) and climate change to simulate future hydrologic processes is still lacking. Furthermore, investigating the future connections between hydrologic characteristics and climate variables demands exploration of these phenomena at small (basin) scale. To fill this gap, this research simulates land use/cover patterns in 2030 based on the logistic Cellular Automata-Markov model. Then the Soil Water Assessment Tool (SWAT) simulates change in streamflow within the Ashuelot River basin in New England between 2002–2009 and 2032–2039. Projected climate data are obtained from two general circulation models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. We also quantify relationships between the rates of change (RC) of streamflow, precipitation and potential evapotranspiration (PET) among 29 subbasins at a monthly scale between the two time periods under different climate scenarios by implementing a panel data approach. Results show greatest changes in forestland (−21.07 km2) and intensive urban land (+5.4 km2) by 2030. Comparisons between the two periods show a negative overall trend in runoff under RCPs 4.5 and 8.5 for both selected GCMs. Panel data analysis indicates that precipitation may contribute more to the RC of streamflow when change in streamflow is significantly influenced by both PET and precipitation over the study period. Therefore, this study provides an important insight into quantifying and comparing the relationship of basin-scale change between streamflow and future climate. [ABSTRACT FROM AUTHOR]

Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an 'anytime' forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability. [ABSTRACT FROM AUTHOR]