Regional Land Cover Research Articles

Holocene Floodplain Transformation Through Catchment‐Scale Human‐Environment Interactions: An Interdisciplinary Case Study of the Gete Catchment (Belgium)

ABSTRACTFloodplains across the European loess region transformed from nature‐ to human‐dominated environments during the Holocene. A general framework of this evolution is well established, but it is less clear how differences in timing—observed within and between catchments—can be explained. Although human impact is an important driver, little attention has thus far been paid to the actual human activities involved, their intensities, and spatiotemporal patterns. By adopting an interdisciplinary approach, this study provides insight into the evolution of both environmental (e.g., local floodplain geoecohydrology and regional land cover) and human dynamics (e.g., demography and land use) in the Belgian Gete catchment. Results show that the observed changes in land cover and floodplain geoecology can indeed be attributed to spatiotemporal variations in human impact. Human‐induced vegetation change, driven by population growth and associated agrarian production, resulted in hydrological changes and localized colluviation. Catchment‐scale improvement of hillslope‐channel connectivity initiated alluviation in the valleys and completed the transformation from forested marsh to open floodplain with overbank sedimentation. In turn, this allowed people to settle in the floodplains in the Early Medieval period, laying the foundations for our present‐day landscape.

Land Cover Mapping in West Africa: A Collaborative Process

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

Earth observation reveals the shifting patterns of China's lake colour driven by climate change and land cover

Water colour has been recognized as one of the most important Essential Climate Variables of the lake ecosystem, as it is directly related to changes in water constituents and almost all of the lake's ecological changes could alter water colour. Given the high retrieval accuracy from existing Earth observation satellite data, water colour, in terms of Forel Ule Index (FUI), can be a realistic indicator to track the long-term changes in the lake ecosystem and further explore the lake response to environmental changes. This paper aims to comprehensively investigate the spatiotemporal variation patterns of FUI in 159 large lakes (≥25 km2) across China during 2000–2022 based on the MODIS data and detect the climatic and anthropogenic driving forces of these changes. The 23 years of MODIS records revealed an overall downward trend of lake FUI across China, indicating the lakes in China shifted to bluer colour during the past two decades. Through driving factor analyses, the complicated interplay among lake colour, lake morphology, regional climate shifts and human interference dynamics was uncovered. In the long term, it was found the pronounced change in lake colour in the western lake zones was primarily attributed to climate warming and humidification, whereas that in the eastern lake zones was mainly related to the alterations in regional land cover during the past two decades. Seasonally, lake basin's air temperature was identified as the main factor impacting the seasonal patterns of lake colour, followed by wind speed and runoff. Spatially, there was high spatial variability in lake colour across China, which was mainly associated with lake elevation and lake basin's precipitation rate, although the factors exhibited considerable divergence across different zones. Based upon the above findings, the implications for lake environment protection and management in different regions of China were further discussed.

Land Cover and Land Use Ontology—Evolution of International Standards, Challenges, and Opportunities

Knowledge of land is of central importance to manage the impact of mankind upon the environment. The understanding and treatment of land vary greatly across different regions and communities, making the description of land highly specific to each locality. To address the larger global issues, such as world food production or climate change mitigation, one needs to have a common standardized understanding of the biosphere cover and use of land. Different governments and institutions established national systems to describe thematic content of land within their jurisdictions. These systems are all valid and tuned to address various national needs. However, their integration at regional or global levels is lacking. Integrating data from widely divergent sources to create world datasets not only requires standards, but also an approach to integrate national and regional land cover classification systems. The ISO 19144 series, developed through the collaboration between the Food and Agriculture Organization of the United Nations (FAO) and the International Organization for Standardization (ISO), offers a meta-language for the integration of disparate land classification systems, enhancing interoperability, data sharing, and national to global data integration and comparison. This paper provides an overview of classification system concepts, different stages for the development of standards in ISO and the status of different standards in the ISO 19144 series. It also explores the critical role of the ISO 19144 series in standardizing land cover and land use classification systems. Drawing on practical case studies, the paper underscores the series’ potential to support global sustainable development goals and lays out a path for its future development and application. Using these standards, we gain not only a tool for harmonizing land classification, but also a critical level for advancing sustainable development and environmental stewardship worldwide.

Dynamic Estimation of Mangrove Carbon Storage in Hainan Island Based on the InVEST-PLUS Model

Mangrove ecosystems are pivotal to the global carbon budget. However, there is still a dearth of research addressing the impact of regional mangrove land use and land cover change (LUCC) on carbon sequestration and its associated spatial distribution patterns. To investigate the impact of different development scenarios on the carbon storage capacity of mangrove ecosystems, we focused on Hainan Island. We used LUCC data from 2010 to 2020 from mangrove-inhabited regions. The Markov-PLUS model was applied to predict the spatiotemporal dynamics of mangrove coverage under the natural increase scenario (NIS) and the mangrove protection scenario (MPS) over the next 40 years. Carbon storage was estimated using the InVEST model based on field-measured carbon density data. The outcomes show the following: (1) The Markov-PLUS model, with an overall accuracy of 0.88 and a Kappa coefficient of 0.82, is suitable for predicting mangrove distribution patterns on Hainan Island. (2) Environmental factors were the main drivers of historical mangrove changes on Hainan Island, explaining 54% of the variance, with elevation, temperature, and precipitation each contributing over 13%. (3) From 2025 to 2065, the mangrove area on Hainan Island is projected to increase by approximately 12,505.68 ha, mainly through conversions from forest land (12.73% under NIS and 12.37% under MPS) and agricultural land (39.72% under NIS and 34.53% under MPS). (4) The carbon storage increment within Hainan Island’s mangroves is projected at 2.71 TgC over the whole island, with notable increases expected in the eastern, northern, and northwestern regions, and modest gains in other areas. In this study, we comprehensively investigated the spatiotemporal dynamics and future trends of carbon storage in the mangroves of Hainan Island, offering invaluable guidance for the long-term management of mangrove ecosystems and the realization of carbon neutrality goals by 2060.

Geographic drivers more important than landscape composition in predicting bee beta diversity and community structure

AbstractThe importance of microhabitat traits such as floral availability is well known; however, forest bee spatial dynamics have been variably studied across local to broad geographic scales. Past literature suggests that landscape factors from proximate to distal are important in determining forest bee community metrics, including richness, abundance, and taxonomic composition. Leveraging the interest and assistance of citizen science volunteers, we employed standard bee bowl trap transects across Maryland, Delaware, northern Virginia, and the District of Columbia and identified correlations between bee community composition, local and regional land cover, and broader geospatial patterns. We also identified the partial contributions of both specific species and sampling sites to total beta diversity. Various land cover metrics were significantly related to bee community structure, with bee abundance positively and negatively correlated with forest and wetland cover, respectively. In general, land cover metrics within 1000‐m buffer exhibited stronger correlations with bee communities; however, broader geographic variation, using Cartesian coordinates north and east as indices, was most significantly correlated with the bee community. Specifically, bee communities were less rich in the east and south of the study area. We also identified similar correlations with the bee community as categorized by both trophic and nesting behaviors, with both geographic northing and easting proving to be most strongly correlated with the forest bee community. Results of beta diversity and cluster analyses showed that the most species‐depauperate sites exhibited the highest contributions to beta diversity and that species‐poor sites consisted of a reduced subset of the greater community. Our results show that successful bee conservation must consider, beyond local‐scale resource availability, broad geospatial considerations and forest habitat connectivity across political and administrative boundaries.

A meta-analysis reveals increases in soil organic carbon following the restoration and recovery of croplands in Southwest China.

In China, the Grain for Green Program (GGP) is an ambitious project to convert croplands into natural vegetation, but exactly how changes in vegetation translate into changes in soil organic carbon remains less clear. Here we conducted a meta-analysis using 734 observations to explore the effects of land recovery on soil organic carbon and nutrients in four provinces in Southwest China. Following GGP, the soil organic carbon content (SOCc) and soil organic carbon stock (SOCs) increased by 33.73% and 22.39%, respectively, compared with the surrounding croplands. Similarly, soil nitrogen increased, while phosphorus decreased. Outcomes were heterogeneous, but depended on variations in soil and environmental characteristics. Both the regional land use and cover change indicated by the landscape type transfer matrix and net primary production from 2000 to 2020 further confirmed that the GGP promoted the forest area and regional mean net primary production. Our findings suggest that the GGP could enhance soil and vegetation carbon sequestration in Southwest China and help to develop a carbon-neutral strategy.

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series

Earth observation satellites offer vast opportunities for quantifying landscapes and regional land cover composition and changes. The integration of artificial intelligence in remote sensing is essential for monitoring significant land cover types like forests, demanding a substantial volume of labeled data for effective AI model development and validation. The Wald5Dplus project introduces a distinctive open benchmark dataset for mid-European forests, labeling Sentinel-1/2 time series using data from airborne laser scanning and multi-spectral imagery. The freely accessible satellite images are fused in polarimetric, spectral, and temporal domains, resulting in analysis-ready data cubes with 512 channels per year on a 10 m UTM grid. The dataset encompasses labels, including tree count, crown area, tree types (deciduous, coniferous, dead), mean crown volume, base height, tree height, and forested area proportion per pixel. The labels are based on an individual tree characterization from high-resolution airborne LiDAR data using a specialized segmentation algorithm. Covering three test sites (Bavarian Forest National Park, Steigerwald, and Kranzberg Forest) and encompassing around six million trees, it generates over two million labeled samples. Comprehensive validation, including metrics like mean absolute error, median deviation, and standard deviation, in the random forest regression confirms the high quality of this dataset, which is made freely available.

Estimating forest extent across Mexico

Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km2 to 675 239 km2. These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range <10%–80%) and subtropical steppe ecological zones (range <10%–60%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km2 with high forest extent agreement, and 340 661 km2 with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km2 is comparable to the official national estimate of 656 920 km2. We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.

Understanding urban growth modeling in Africa: Dynamics, drivers, and challenges

Africa has been a hotspot of rapid urban growth since 2000, yet no review exists that describes the current state of urban growth modeling. This review aims to fill this gap by synthesizing urban growth modeling approaches, input variables and challenges across Africa. Using Web of Science, Google Scholar, Geobase, and Compendex databases, we selected 49 studies that projected urban land use and cover change or modeled probabilities of future urban change associated with a variety of input variables. We found increasing trends in the numbers of studies employing cellular automata, statistical regression, agent-based, video prediction, and integrated approaches. The modeling approaches used and their corresponding increases differed across Africa. The input variables used to project urban growth also varied across subregions. Studies were biased towards populous countries rather than the most urbanized. 78 % of the studies focused on primary cities, while secondary cities were less frequently studied. Comparison of urban growth modeling across multiple regions and for different types of cities is currently difficult due to inconsistent definitions and limited availability of data and computational resources. We recommend that the scientific community develop consistent definitions of African city types and urban land use and land cover classifications, create regional urban land cover products for Africa, and build repositories of models and their input data.

Distance decay and directional diffusion of ecoclimate teleconnections driven by regional-scale tree die-off

Climate change is triggering regional-scale alterations in vegetation including land cover change such as forest die-off. At sufficient magnitudes, land cover change from forest die-off in one region can change not only local climate but also vegetation including agriculture elsewhere via changes in larger scale climate patterns, termed an ‘ecoclimate teleconnection’. Ecoclimate teleconnections can therefore have impacts on vegetative growth in distant regions, but the degrees to which the impact decays with distance or directionally diffuses relative to the initial perturbation are general properties that have not been evaluated. We used the Community Earth system model to study this, examining the implications of tree die-off in 14 major US forested regions. For each case we evaluated the ecological impact across North America as a function of distance and direction from the location of regional tree die-off. We found that the effects on gross primary productivity (GPP) generally decayed linearly with distance, with notable exceptions. Distance from the region of tree die-off alone explained up to ∼30% of the variance in many regions. We also found that the GPP impact was not uniform across directions and that including an additional term to account for direction to regional land cover change from tree die-off was statistically significant for nearly all regions and explained up to ∼40% of the variance in many regions, comparable in magnitude to the influence of El Nino on GPP in the Western US. Our results provide novel insights into the generality of distance decay and directional diffusion of ecoclimate teleconnections, and suggest that it may be hard to identify expected impacts of tree die-off without case-specific simulations. Such patterns of distance decay, directional diffusion, and their exceptions are relevant for cross-regional policy that links forests and other agriculture (e.g. US Department of Agriculture).

A multi-data approach to evaluate progress towards land degradation neutrality in Central Asia

Central Asia hosts one of the largest continuous grassland areas on our planet, that is of vital importance for food security, biodiversity and carbon sequestration. However, this region is also subjected to some of the most intense land degradation processes, related to large scale land use change and climate change.To combat land degradation and pursue sustainable land management the concept of land degradation neutrality (LDN) has been proposed. LDN assessments are recommended to be based on global data in regions that lack sufficient regional data such as Central Asia. However, it remains unclear how the selection of datasets influences the estimated extent of land degradation. We followed the LDN framework and first calculated changes to three LDN indicators: land productivity, land cover, and Soil Organic Carbon (SOC). We then evaluated the impact of using different regional and global land cover data on the extent of land degradation in the region. Finally, we calculated a regionally specific integrated indicator on soil quality that we used to assess the “like-for-like” principle which enables counterbalancing losses and gains.Our results indicate that particularly the selection of land cover data has a significant impact on the overall assessment of degradation state. The extent of land between 2000 and 2019 varies considerably depending on the data and ranges between 15 and 34 % of the whole Central Asian region. Our findings reveal that the area of degraded land on high quality soils is twice as high as the area of high quality soils where the land condition has improved (12 % versus 6 %). In areas with low soil quality, 13 % was subject to degradation, and 10 % improved.Whilst the degradation extent varies according to the selected land cover datasets, our results demonstrates that Central Asia is undergoing land degradation affecting high quality soils. Sensitivity analysis of multiple datasets can reduce the risk of misjudgement in degradation extent assessments, especially in regions with mosaic ecosystems, such as grasslands.

Accuracy Assessment of Eleven Medium Resolution Global and Regional Land Cover Land Use Products: A Case Study over the Conterminous United States

Land cover land use (LCLU) products provide essential information for numerous environmental and human studies. Here, we assess the accuracy of eleven global and regional products over the conterminous U.S. using 25,000 high-confidence randomly distributed samples. Results show that in general, the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment and Projection (LCMAP) outperform other multi-class products, both in terms of higher individual class accuracy and with accuracy variability across classes. More specifically, F1 accuracy comparisons between the best performing USGS and non-USGS products indicate: (i) similar performance for the water class, (ii) USGS product outperformance in the developed (+1.3%), grass/shrub (+3.2%) and tree cover (+4.2%) classes, and (iii) non-USGS product (WorldCover) gains in the cropland (+5.1%) class. The NLCD and LCMAP also outperformed specialized single-class products, such as the Hansen Global Forest Change, the Cropland Data Layer and the Global Artificial Impervious Areas, while offering comparable results to the Global Surface Water Dynamics product. Spatial visualizations also allowed accuracy comparisons across different geographic areas. In general, the NLCD and LCMAP have disagreements mainly in the middle and southeastern part of conterminous U.S. while Esri, WorldCover and Dynamic World have most errors in the western U.S. Comparisons were also undertaken on a subset of the reference data, called spatial edge samples, that identifies samples surrounded by neighboring samples of different class labels, thus excluding easy-to-classify homogenous areas. There, the WorldCover product offers higher accuracies for the highly dynamic grass/shrub (+4.4%) and cropland (+8.1%) classes when compared to the NLCD and LCMAP products. An important conclusion while looking at these challenging samples is that except for the tree class (78%), the best performing products per class range in accuracy between 55% and 70%, which suggests that there is substantial room for improvement.

Regional Development, Rural Transformation, and Land Use/Cover Changes in a Fast-Growing Oil Palm Region: The Case of Jambi Province, Indonesia

The development policies implemented in Jambi Province, Indonesia, since the New Order era, have encouraged transformations in demography, regional paradigms, growth base sectors, and growth actors. This study aims to understand how the central government’s demographic policies and plantation development program have impacted regional land cover change, rural development transformation, and economic development in Sumatra Island’s biodiversity-rich rainforest region (Jambi Province). This study explores the shifts in village typology that have occurred. Descriptive analysis and cluster analysis were applied to collect data at the provincial level that included population, poverty rate, levels of education, HDI, GRDP, Gini index, and land cover. Village Potential Data at the village level was then used to build an infrastructure index and a diversity of activities index. The results of this study indicated that macro development indicators are in a positive trend: HDI, educational levels, GRDP per capita, and the proportion of the tertiary sector in GRDP have all increased. The growth of the tertiary sector was largely driven by the trade and motorized-vehicle repair sectors. The provincial landscape has changed significantly due to the expansion of oil palm plantations. At the village level, the infrastructure index and diversity index show an increase in the number of facilities and variations in economic activity in most villages. Some shifts in rural typology are highlighted: the increase in urban villages and in villages that specialize in oil palm plantation or other industrial plantation crops.

Factors Influencing the Spatial Distribution of Regulating Agro-Ecosystem Services in Agriculture Soils: A Case Study of Slovakia

Agro-ecosystem services assessment and mapping are one of the main requirements for implementing the concept of ecosystem services into institutional decision-making within the European Union and worldwide. The aim of this study was to identify the most important regional drivers of the natural potential of regulating ecosystem services with agricultural soil in Slovakia, resulting in an original set of macro-scale spatial stratification criteria for agricultural land, and to estimate typical middle values of its potential in newly defined regions. The non-monetary assessment was based on natural environment parameters and land use factors. For the assessment of individual indicators of agro-ecosystem services, we utilized an expert scoring system. We evaluated ecosystem services on the basis of biophysical indicators that determine the corresponding soil functions and are part of the existing databases available in the Slovak Republic. The new methodological combination enabled us to provide unique mapping and assessment of agro-ecosystem services within Slovakia. Regional climate, land cover, and soil slope were identified as key factors impacting agro-ecosystem services potential within the country, which can be used as data stratification levels for further analyses. Linking the value of individual regulating agro-ecosystem services potential with geographical distribution can help to optimize its potential depending on the needs of the inhabitants living in different regions by introducing appropriate measures and can contribute to effective agricultural policymaking.

Assessment of future environmental carrying capacity in the Mamminasata area, Indonesia, derived from regional census and land cover

ABSTRACT Environmental carrying capacity is central to the sustainable development concept that has been applied in spatial planning programs by the Government of Indonesia. These programs focus on population needs based on existing conditions and projected population growth. This study predicted the future environmental carrying capacity of the region called Mamminasata (Makassar City, Maros, Sungguminasa and Takalar Regencies) to 2031 through statistical calculation. Census data from local government agencies and land cover prediction data from previous studies were used. The respective results for demographic, residential, and protected function carrying capacities by 2031 were 0.104 (good), 6.297 (good), and 0.520 (good but requiring attention to prevent further decline). Projected agricultural carrying capacity in 2031 would leave about 45% of the agricultural land now used for food crops. The results of this research can assist sustainable development in Mamminasata.

Assessment of urban sprawls, amenities, and indifferences of LST and AOD in sub-urban area: a case study of Jammu

Urbanization, particularly in peri-urban areas, often results in critically transforming the regional land use and land cover (LULC). The increased built-up in peri-urban areas affects the regional accessibility of residents of urban clusters to requisite amenities and severely affects the regional environment, as observed in the case of Jammu district situated in the foothills of the Indian Himalayas. The present study is aimed at assessing the rise of urban sprawls in Jammu district over the past two decades and how the urbanization has affected the lag in the number of amenities corresponding to the urban growth based on qualitative parameters. Further, a parameterization scheme is developed to assess the amenities quality. A comparison is made with Indore, a planned smart city, to assess the status of urbanization and residential quality based on an amenity index. The study also investigates the indifferences observed in some of the climate variables in the urban and sub-urban settings of the Jammu district. The investigation is conducted through a multi-ring buffer analysis approach utilizing the land use land cover (LULC) products based on Landsat 8/7 satellite imagery of 2002, 2013, and 2021. The indifferences in the settings are analyzed using MODIS aerosol optical depth (AOD) and land surface temperature (LST) products. The analysis leads to determination of critical urban parameters including the urban area, density, and growth rate, revealing significant urbanization at 25-27km from the city center. Significant indifferences are observed in urban and sub-urban areas indicating higher rise in LST and AOD, particularly in the recent decade. These investigations provide critical information to urban and climate solution authorities for planning and management, particularly in critically endangered areas.

Land Cover Changes of the Qilian Mountain National Park in Northwest China Based on Phenological Features and Sample Migration from 1990 to 2020

The spatial and temporal variation analysis of land cover classification is important for studying the distribution and transformation of regional land cover changes. The Qilian Mountain National Park (QMNP), an important ecological barrier in northwestern China, has lacked land cover products for long time series. The Landsat images available on the Google Earth Engine (GEE) make it possible to analyze the land cover changes over the past three decades. The purpose of this study was to generate a long time series of datasets of land cover classification based on the method of sample migration in the QMNP in Northwest China. The Landsat 5, 7, and 8 images and field sample data were combined with multiple image features and the random forest algorithm to complete the land cover classification of the QMNP from 1990 to 2020. The results indicate that (1) the method of Jeffries–Matusita (J-M) distance can reduce image feature redundancy and show that elevation and phenological features have good differentiability among land cover types that were easy to mix with feature classes; (2) the spatial distribution of land cover every 10 years between 1990 and 2020 was consistent in the QMNP, and there were obvious differences in land cover from the east to the west part of the QMNP, with a large area of vegetation distribution in Sunan county in the central part and Tianzhu county in the east part of the QMNP; (3) over the past 30 years, forests and grasslands decreased by 62.2 km2 and 794.7 km2, respectively, while shrubs increased by 442.9 km2 in the QMNP. The conversion of bare land to grassland and the interconversion between different vegetation types were the main patterns of land cover changes, and the land cover changes were mainly concentrated in pastoral areas, meaning that human activity was the main factor of land cover changes; and (4) when the samples of 2020 were migrated to 2010, 2000, and 1990, the overall classification accuracies were 89.7%, 88.0%, 86.0%, and 83.9%, respectively. The results show that the vegetation conservation process in the QMNP was closely related to human activities.

Relationship between climate and land cover change in Aral Sea Basin

In the past several decades, substantial changes were observed in Central Asia’s land cover. Water-sensitive Central Asia has experienced an increase in farming and expansion of urban areas. These are considered the main reasons for water level reduction in the Aral Sea. The disappearance of the Aral Sea has not only affected the climate of the region but also caused regional land cover changes. In this article, we analyse the temporal variation of the Normalized Difference Vegetation Index (NDVI) and its correlation with climatic variables in the territory of the Aral Sea Basin from 1982 to 2015 using Global Inventory Modelling and Mapping Studies (GIMMS) and Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the mean annual NDVI value recorded a weak positive trend of 0.0023/10a over the last 34 years. The Hurst index is used to test whether the tendency observed in past can be extrapolated in the future or not. Our results showed, the Hurst exponent indicates that the vegetation dynamic trend was consistent, which means that NDVI values will continue to rise in the future. During the study period, precipitation and surface soil moisture increased in the growing season, which affected the temperature during the non-growing season.