Search Results (105)

Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in 2020 by comparing ten land cover datasets of varying resolutions from 500 to 10 m, using the equivalent factor method. Significant disagreements in ESV estimates are identified, revealing spatial heterogeneity and large inconsistencies among estimates from different datasets, even with high spatial resolution (10 m). Across all counties, the typical discrepancy in ESV estimates between any two datasets reaches 3503 CNY/ha, and the ESV estimates for each county show an average coefficient of variation (CV) of 0.186 across the ten datasets, indicating considerable inconsistency attributable to dataset selection. The results highlight that ESV evaluations based on the CLCD, Globeland30, and GLC-FCS30 datasets demonstrate higher consistency and reliability, making them suitable for regional ecosystem service valuation. Both the landscape configurations and the area disparities of different land types have significant impacts on ESV disagreement. This study provides valuable insights into the applicability of different datasets for ESV evaluation, thereby enhancing the reliability of ESV assessments and supporting informed decision making in ecosystem management. Full article

With advancements in cloud computing and machine learning algorithms, an increasing number of cropland datasets have been developed, including the China land-cover dataset (CLCD) and GlobeLand30 (GLC). The unique climatic conditions of the Tibetan Plateau (TP) introduce significant differences and uncertainties to these datasets. Here, we used a quantitative and visual integrated assessment approach to assess the accuracy and spatial consistency of five cropland datasets around 2020 in the TP, namely the CLCD, GLC30, land-use remote sensing monitoring dataset in China (CNLUCC), Global Land Analysis and Discovery (GLAD), and global land-cover product with a fine classification system (GLC_FCS). We analyzed the impact of terrain, climate, population, and vegetation indices on cropland spatial consistency using structural equation modeling (SEM). In this study, the GLAD cropland area had the highest fit with the national land survey (R² = 0.88). County-level analysis revealed that the CLCD and GLC_FCS underestimated cropland areas in high-elevation counties, whereas the GLC and CNLUCC tended to overestimate cropland areas on the TP. Considering overall accuracy, GLC and GLAD performed the best with scores of 0.76 and 0.75, respectively. In contrast, CLCD (0.640), GLC_FCS (0.640), and CNLUCC (0.620) exhibited poor overall accuracy. This study highlights the significantly low spatial consistency of croplands on the TP, with only 10.60% consistency in high and complete agreement. The results showed substantial differences in spatial accuracy among zones, with relatively higher consistency observed in low-altitude zones and notably poorer accuracy in zones with sparse or fragmented cropland. The SEM results indicated that elevation and slope directly influenced cropland consistency, whereas temperature and precipitation indirectly affected cropland consistency by influencing vegetation indices. This study provides a valuable reference for implementing cropland datasets and future cropland mapping studies on the TP region. Full article

Accurate forest cover maps are essential for forest conservation and sustainable development. Numerous global forest cover products have emerged in recent years; however, most tend to neglect sparsely forested arid and semi-arid areas, such as the Three-North Shelter Forest (TNSF) Program Region in China. Despite their sparse distribution, forests in these areas play a vital role in maintaining global ecological balance and biodiversity. Therefore, a comprehensive evaluation of these products is necessary. In this study, the performance of nine global forest cover products was systematically investigated at a 10–30 m resolution (GlobeLand30, GLC_FCS30D, FROM-GLC30, FROM-GLC10, ESA World Cover, ESRI Land Cover, GFC30, GFC 2020, and GFC) in the TNSF region around 2020. Specifically, a novel and comprehensive validation dataset was first generated by integrating all available open-access validation datasets in the TNSF region after visual interpretation. Second, the consistency and accuracy of nine forest cover products were evaluated, and their discrepancies with government statistical data were analyzed. The results indicate that GFC2020 provides the highest overall accuracy (OA) of 90.49%, followed by ESA World Cover, while GlobeLand30 had the lowest accuracy of 84.78%. Meanwhile, compared with statistical data, all nine products underestimated forest areas, especially in these hyper-arid zones (aridity index < 0.03). Notably, 31.04% of the area is identified as forest by only one product, attributable to differences in forest definitions and remote sensing data among the products. Therefore, this study provides a detailed assessment and analysis of nine global forest cover products from multiple perspectives, offering valuable insights for users in selecting appropriate forest cover products and supporting forest management. Full article

Mountains support many kinds of ecosystem services (ESs) for human beings, emphasizing the need to understand the characteristics and drivers of ES changes in mountainous regions. In this study, Nanling, the most significant mountains of southern China, was selected as a case study. Utilizing the GlobeLand30 dataset, we employed InVEST, Geodetector and MGWR to identify the spatio-temporal characteristics and drivers of ES changes, investigate trade-offs and synergies between ESs, and examine the relationship between ESs and the landscape ecological risk index (LERI) to provide a new perspective for ecosystem management in vulnerable mountain regions. The results showed that carbon storage (CS) and habitat quality (HQ) slightly decreased, while the water yield (WY) increased slightly. Soil conservation (SC) significantly decreased, but the total ES (TES) slightly increased. All ES bundles demonstrated a synergistic relationship, but most of the synergies exhibited a decreasing trend. The ESs in the study area were mainly affected by climate factors, and anthropogenic factors also had a significant impact on ESs. LERI exhibited a negative correlation with the provision of ESs and demonstrated a high explanatory power for ES changes, especially for CS, HQ and TES, suggesting that areas with more stable landscape patterns are likely to harbor greater levels of ESs. The results provide insights into the analysis of the characteristics of ES change in vulnerable mountainous areas, also providing the practical implications for introducing LERI as a driver for ES change. Full article

Economic corridors play a crucial role in promoting economic growth and facilitating coordinated regional development. However, land use changes associated with the development of emerging economic corridors have become a prominent source of conflict in regional integration in the Global South. This study takes the Laos Economic Corridor as a case study to explore the characteristics and driving mechanisms of land use changes in emerging economic corridor regions. Using global land cover data from 2000 to 2020 (GlobeLand30) and employing spatial statistical analysis, the Random Forest (RFC) algorithm, and the CA-Markov model, the study follows a Pattern–Process–Mechanism–Trend analytical framework to reveal the spatial distribution characteristics and transformation paths of land use within the corridor. The study results indicate that (1) The land use pattern in the Laos Economic Corridor has gradually shifted from a “single-core radial” structure to a “dumbbell-shaped” structure, promoting coordinated regional economic development. (2) A significant unidirectional flow of land use has been established, with forestland being converted into cultivated land and cultivated land being further converted into artificial surfaces. (3) In addition to the natural geographical constraints, the transport infrastructure and the spatial layout of industries are the main drivers for the expansion of ecological land, agricultural land, and built-up land. (4) Spatial planning interventions are essential and urgent: the establishment of land management rules based on the principles of forest conservation and intensive development can effectively control the uncontrolled expansion of artificial areas, significantly reduce the loss of forestland, and ensure the rational allocation of land resources for long-term development. The findings of this study offer valuable insights and reference points for the Global South, enhancing understanding of the spatial development dynamics of economic corridors, informing the optimization of land-use policies, and supporting efforts to promote regional integration and sustainable development. Full article

Land use change is related to a series of core issues of global environmental change, such as environmental quality improvement, sustainable utilization of resources, energy reuse and climate change. In this study, Google Earth Engine (GEE), a remote sensing natural environment monitoring and analysis platform, was used to realize the combination of Landsat TM/OLI data images with spectral features and topographic features, and the random forest machine learning classification method was used to supervise and classify the low-cloud composite image data of Ordos City. The results show that: (1) GEE has a powerful computing function, which can realize efficient and high-precision in-depth analysis of long-term multi-temporal remote sensing images and monitoring of land use change, and the accuracy of acquisition can reach 87%. Compared with other data sets in the same period, the overall and local classification results are more distinct than ESRI (Environmental Systems Research Institute) and GlobeLand 30 data products. Slightly lower than the Institute of Aerospace Information Innovation of the Chinese Academy of Sciences to obtain global 30 m of land cover fine classification products. (2) The overall accuracy of the land cover data of Ordos City from 2003 to 2023 is between 79–87%, and the Kappa coefficient is between 0.79–0.84. (3) Climate, terrain, population and other interactive factors combined with socio-economic population data and national and local policies are the main factors affecting land use change between 2003 and 2023. Full article

The accuracy assessment of cropland products is a critical prerequisite for agricultural planning and food security evaluations. Current accuracy assessments of remote sensing-based cropland products focused on the consistency of spatial patterns for specific years, yet the reliability of these cropland products in time-series analysis remains unclear. Using cropland area data from the second and third national land surveys of China (referred to as NLSCD) as a benchmark, we evaluate the area-based and spatial-based consistency of cropland changes in five 30 m time-series land cover products covering 2010 and 2020, including the annual cropland dataset of China (CACD), the annual China Land Cover Dataset (CLCD), China’s Land-use/cover dataset (CLUD), the Global Land-Cover product with Fine Classification System (GLC_FCS30), and GlobeLand30. We also employed the GeoDetector model to explore the relationships between the consistency in cropland change and the environmental factors (e.g., cropland fragmentation, topographic features, frequency of cloud cover, and management practices). The area-based consistency analysis showed that all five cropland products indicate a declining trend in cropland areas in China over the past decade, while the amount of cropland loss ranges from 5.59% to 57.85% of that reported by the NLSCD. At the prefecture-level city scale, the correlation coefficients between the cropland area changes detected by five cropland products and the NLSCD are low, with GlobeLand30 having the highest coefficient at 0.67. The proportion of prefecture-level cities where the change direction of cropland area in each cropland product is inconsistent with the NLSCD ranges from 13.27% to 39.23%, with CLCD showing the highest proportion and CLUD the lowest. At the pixel scale, the spatial-based consistency analysis reveals that 79.51% of cropland expansion pixels and 77.79% of cropland loss pixels are completely inconsistent across five cropland products, with the southern part of China exhibiting greater inconsistency compared to Northwest China. Besides, the frequency of cloud cover and management practices (e.g., irrigation) are the primary environmental factors influencing consistency in cropland expansion and loss, respectively. These results suggest low consistency in cropland change across five cropland products, emphasizing the need to address these inconsistencies when generating time-series cropland datasets via remote sensing. Full article

Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables. Full article

The growing problem of non-grain production of cultivated land (NGPOCL) has increased food security risk, garnering attention from China and other nations worldwide. Current research predominantly focuses on the internal planting structure of cultivated land. To more comprehensively measure the level of NGPOCL, we categorized NGPOCL into two types: visible non-grain production of cultivated land (VNGPOCL) and invisible non-grain production of cultivated land (INGPOCL). VNGPOCL and INGPOCL scopes were extracted utilizing land use and vegetation index data, exploring their spatial–temporal characteristics and driving factors through spatial feature analysis and multiple linear regression methods. The findings are as follows: (1) The degree of VNGPOCL shifted from mild to moderate, with its rate increasing from 5.16% in 2000–2010 to 10.82% in 2010–2020. Furthermore, the spatial variation in VNGPOCL indicated a growing east–west disparity while showing a reduction in north–south differences, reflecting significant spatial agglomeration effects. (2) There was a dramatic increase in areas classified as having moderate to severe INGPOCL, with the rate rising from 14.24% in 2000 to 41.47% by 2020. The east–west and north–south disparities concerning INGPOCL diminished rapidly, also indicating strong spatial agglomeration effects. (3) The driving factors for VNGPOCL and INGPOCL differed significantly depending on developmental stages. The results contribute valuable insights into accurately characterizing the spatial–temporal features associated with NGPOCL in Hebei Province while enhancing risk management strategies related to NGPOCL. Full article

Accurate cropland distribution data are essential for efficiently planning production layouts, optimizing farmland use, and improving crop planting efficiency and yield. Although reliable cropland data are crucial for supporting modern regional agricultural monitoring and management, cropland data extracted directly from existing global land use/cover products present uncertainties in local regions. This study evaluated the area consistency, spatial pattern overlap, and positional accuracy of cropland distribution data from six high-resolution land use/cover products from approximately 2020 in the alpine agricultural regions of the Hehuang Valley and middle basin of the Yarlung Zangbo River (YZR) and its tributaries (Lhasa and Nianchu Rivers) area on the Qinghai-Tibet Plateau. The results indicated that (1) in terms of area consistency analysis, European Space Agency (ESA) WorldCover cropland distribution data exhibited the best performance among the 10 m resolution products, while GlobeLand30 cropland distribution data performed the best among the 30 m resolution products, despite a significant overestimation of the cropland area. (2) In terms of spatial pattern overlap analysis, AI Earth 10-Meter Land Cover Classification Dataset (AIEC) cropland distribution data performed the best among the 10 m resolution products, followed closely by ESA WorldCover, while the China Land Cover Dataset (CLCD) performed the best for the Hehuang Valley and GlobeLand30 performed the best for the YZR area among the 30 m resolution products. (3) In terms of positional accuracy analysis, the ESA WorldCover cropland distribution data performed the best among the 10 m resolution products, while GlobeLand30 data performed the best among the 30 m resolution products. Considering the area consistency, spatial pattern overlap, and positional accuracy, GlobeLand30 and ESA WorldCover cropland distribution data performed best at 30 m and 10 m resolutions, respectively. These findings provide a valuable reference for selecting cropland products and can promote refined cropland mapping of the Hehuang Valley and YZR area. Full article

Under the influences of climate change and human activities, habitat quality (HQ) in inland river basins continues to decline. Studying the spatiotemporal distributions of land use and HQ can provide support for sustainable development strategies of the ecological environment in arid regions. Therefore, this study utilized the SD-PLUS model, InVEST-HQ model, and Geodetector to assess and simulate the land-use changes and HQ in the Tarim River Basin (TRB) at multiple scales (county and grid scales) and scenarios (SSP126, SSP245, and SSP585). The results indicated that (1) the Figure of Merit (FoM) values for Globeland 30, China’s 30 m annual land-cover product, and the Chinese Academy of Sciences (30 m) product were 0.22, 0.12, and 0.15, respectively. A comparison of land-use datasets with different resolutions revealed that the kappa value tended to decline as the resolution decreased. (2) In 2000, 2010, and 2020, the HQ values were 0.4656, 0.4646, and 0.5143, respectively. Under the SSP126 and SSP245 scenarios, the HQ values showed an increasing trend: for the years 2030, 2040, and 2050, they were 0.4797, 0.4834, and 0.4855 and 0.4805, 0.4861, and 0.4924, respectively. Under SSP585, the HQ values first increased and then decreased, with values of 0.4791, 0.4800, and 0.4766 for 2030, 2040, and 2050, respectively. (3) Under three scenarios, areas with improved HQ were mainly located in the southern and northern high mountain regions and around urban areas, while areas with diminished HQ were primarily in the western part of the basin and central urban areas. (4) At the county scale, the spatial correlation was not significant, with Moran’s I ranging between 0.07 and 0.12, except in 2000 and 2020. At the grid scale, the spatial correlation was significant, with clear high- and low-value clustering (Moran’s I between 0.80 and 0.83). This study will assist land-use planners and policymakers in formulating sustainable development policies to promote ecological civilization in the basin. Full article

Analyzing the change trend of urban green space (UGS) and exploring related driving forces can provide scientific reference for sustainable development in rapidly urbanizing areas. However, the spatial and temporal driving mechanisms of the drivers on UGS patterns at different scales are still not deeply understood. Based on the GlobeLand30 land cover data, nighttime lighting data and spatial statistics from 2000 to 2020, this study analyzed the size, shape and diversity of UGS in Guangzhou at the urban level, gradient level and township level with multiple landscape indices. Diversity means the richness of UGS patch types. The selected indices include percent of landscape (PLAND), largest path index (LPI), landscape shape index (LSI), aggregation index (AI) and Shannon’s diversity index (SHDI). The spatiotemporal heterogeneity of the drivers was then explored using the spatiotemporal weighted regression (GTWR) method. Results showed the following: (1) During 2000−2020, the total amount of UGS in Guangzhou increased slightly and then decreased gradually. UGS was mainly transferred into artificial surfaces (lands modified by human activities). (2) The UGS landscape showed a non-linear trend along the urban–rural gradient and fluctuated more in the interval of 20–60% urbanization level. PLAND, LPI and AI decreased significantly in areas with higher levels of urbanization. LSI increased and SHDI decreased significantly in areas with lower levels of urbanization. At township level, the landscape indices showed significant spatial autocorrelation. They transformed from discrete changes at the edge and at the junction of the administrative district to large-scale aggregated change, especially in northern areas. (3) The size of UGSs was mainly influenced by natural factors and population density, but their shape and diversity were mainly influenced by socio-economic factors. More regular shapes of green patches were expected in higher urbanization areas. Population agglomeration positively influenced green space patterns in the northeastern and southern regions (Zengcheng, Conghua and Nansha). Meanwhile the negative influence of urban expansion on the green space pattern in the central and southern regions decreased over time. This study contributes to an in-depth understanding of how the key factors affect the different changes of UGS with time and space and provides methodological support for the long-term zoning planning and management of UGS. Full article

The Tibetan Plateau (TP) region contains maximal alpine grassland ecology at the mid-latitudes. This region is also recognized as an ecologically fragile and sensitive area under the effects of global warming. Regional climate modeling and ecosystem research depend on accurate land cover (LC) information. In order to obtain accurate LC information over the TP, the reliability and precision of five moderate/high-resolution LC products (MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 in 2020) were analyzed and evaluated in this study. The different LC products were compared with each other in terms of areal/spatial consistency and assessed with four reference sample datasets (Geo-Wiki, GLCVSS, GOFC-GOLD, and USGS) using the confusion matrix method for accuracy evaluation over the TP. Based on the paired comparison of these five LC datasets, all five LC products show that grass is the major land cover type on the TP, but the range of grass coverage identified by the different products varies noticeably, from 43.35% to 65.49%. The fully consistent spatial regions account for 43.72% of the entire region of the TP, while, in the transition area between grass and bare soil, there is still a large area of medium-to-low consistency. In addition, a comparison of LC datasets using integrated reference datasets shows that the overall accuracies of MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 are 54.29%, 49.32%, 53.03%, 53.73%, and 60.11%, respectively. The producer accuracy of the five products is highest for grass, while glaciers have the most reliable and accurate characteristics among all LC products for users. These findings provide valuable insights for the selection of rational and appropriate LC datasets for studying land-atmosphere interactions and promoting ecological preservation in the TP. Full article

The rapid advancement of remote sensing technology has given rise to numerous global- and regional-scale medium- to high-resolution land cover (LC) datasets, making significant contributions to the exploration of worldwide environmental shifts and the sustainable governance of natural resources. Nonetheless, owing to the inherent uncertainties embedded within remote sensing imagery, LC datasets inevitably exhibit inaccuracies. In this study, a local accuracy assessment of LC datasets in Southwest China was conducted. The datasets utilized in our analysis include ESA WorldCover, CLCD, Esri Land Cover, CRLC, FROM-GLC10, GLC_FCS30, GlobeLand30, and SinoLC-1. This study employed a sampling approach that combines proportional allocation and stratified random sampling (SRS) to gather sample points and compute confusion matrices to validate eight LC products. The local accuracy of the eight LC maps differs significantly from the overall accuracy provided by the original authors in Southwest China. ESA WorldCover and CLCD demonstrate higher local accuracy than other products in Southwest China, with their overall accuracy (OA) values being 87.1% and 85.48%, respectively. Simultaneously, we computed the area for each LC map based on categories, quantifying uncertainty through the reporting of confidence intervals for both accuracy and area parameters. This study aims to validate and compare eight LC datasets and assess precision and area of diverse spatial resolution datasets for mapping and monitoring across Southwest China. Full article

Exploring the mechanisms that drive land use and cover change (LUCC) is essential for informing the formulation and implementation of effective policies aimed at optimizing land use patterns. In this study, we examined the spatial and temporal patterns of LUCC within the Lancang–Mekong River Basin (LMRB) using Globeland30 data for the years 2000, 2010, and 2020. Firstly, we analyzed the quantitative characteristics of LUCC within the LMRB in terms of the value of change and rate of change. Additionally, we investigated the converting characteristics of LUCC within the LMRB by employing land use transition matrices and land use transition probability matrices. Furthermore, we depicted the spatial distribution of LUCC within the LMRB through land use mapping and statistical analysis. The results indicate a substantial decline in forests, coupled with a notable expansion in cultivated land. Given the vital role of forests as carbon sinks, reforestation can enhance ecological services and address challenges related to climate change. Converting cultivated land to forests is an effective human intervention promoting forest transition. This study applies binary logistic models to explore the mechanisms that influence the conversion from cultivated land to forests. The results reveal that slopes ranging from 5° to 15° have the lowest probability of conversion, whereas distances between the cultivated land and the nearest tourist attraction ranging from 9 km to 18 km have the highest probability. Moreover, the conversion process is positively associated with traffic conditions and significantly influenced by human interventions. Within the study area, China, Laos, and Myanmar show a tendency to convert cultivated land into natural LULC types, while Cambodia, Thailand, and Vietnam tend to encroach on cultivated land and expand artificial surfaces. Promoting ecological restoration in the LMRB requires cooperation among these countries. Full article