Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	19.2 Days	CHF 2600
Foods foods	4.7	7.4	2012	14.5 Days	CHF 2900
Land land	3.2	4.9	2012	16.9 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700

23 pages, 7775 KiB

Open AccessArticle

High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine

by Zhenwei Hou, Bangqian Chen, Yaqun Liu, Huadong Zang, Kiril Manevski, Fangmiao Chen, Yadong Yang, Junyong Ge and Zhaohai Zeng

Remote Sens. 2025, 17(10), 1707; https://doi.org/10.3390/rs17101707 - 13 May 2025

Abstract

The accurate mapping of crop types and rotation patterns is essential for promoting sustainable agricultural development, particularly in ecologically fragile regions such as the farming–pastoral ecotone of China (FPEC). This study focuses on Zhangjiakou, a representative area of the FPEC, to develop a [...] Read more.

The accurate mapping of crop types and rotation patterns is essential for promoting sustainable agricultural development, particularly in ecologically fragile regions such as the farming–pastoral ecotone of China (FPEC). This study focuses on Zhangjiakou, a representative area of the FPEC, to develop a multi-sensor remote sensing framework for monitoring crop distribution and analyzing rotation dynamics. After cloud removal and Savitzky–Golay filtering were applied to correct noise, we selected vegetation index features with maximum inter-class separability during the optimal classification window (June 15–August 20) and generated quarterly Sentinel-1 SAR composites. A Random Forest classifier was employed to perform crop classification based on these optimized features, enabling 10 m resolution crop mapping from 2019 to 2023. The proposed method achieved high classification accuracy (overall accuracy and Kappa > 0.90), with strong agreement between mapped and statistical crop areas (R²: 0.85–0.88; RMSE: 0.42–0.58 × 10⁴ ha). Spatial analysis revealed distinct distribution patterns: oats, potato, sesame, and vegetables were predominantly cultivated in northern Zhangjiakou, while maize dominated southern regions. We observed significant annual variations in crop area proportions and identified specific altitudinal preferences: maize, potato, and sesame were mainly grown at 480–520 m, while oats and other crops at 520–600 m. Slope analysis showed that most crops were cultivated on gentle slopes of 0–5°, with sesame extending to 4–10° slopes. Temporal analysis from 2019 to 2023 indicated that sesame, oats, and potato predominantly followed rotation patterns, while maize cultivation was primarily monoculture. Key drivers of rotation change included water scarcity, economic incentives, and continuous cropping constraints. These findings provide critical insights for optimizing crop rotation strategies, enhancing agricultural sustainability, and improving land-use efficiency in ecologically fragile regions. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

29 pages, 13906 KiB

Open AccessArticle

The Effects of Multi-Scenario Land Use Change on the Water Conservation in the Agro-Pastoral Ecotone of Northern China: A Case Study of Bashang Region, Zhangjiakou City

by Ruiyang Zhao, Haiming Kan, Hengkang Xu, Chao Chen, Guofang Zhang, Zhuo Pang and Weiwei Zhang

Agriculture 2025, 15(9), 1008; https://doi.org/10.3390/agriculture15091008 - 6 May 2025

Viewed by 300

Abstract

Water resource management is crucial for sustainable agricultural and ecological development, particularly in regions with complex land-use patterns and sensitive eco-systems. The Bashang region of Zhangjiakou city, located in the agro-pastoral ecotone of northern China, is an ecologically fragile area that is currently [...] Read more.

Water resource management is crucial for sustainable agricultural and ecological development, particularly in regions with complex land-use patterns and sensitive eco-systems. The Bashang region of Zhangjiakou city, located in the agro-pastoral ecotone of northern China, is an ecologically fragile area that is currently undergoing significant land use and climate changes. Despite the importance of understanding the interplay between land use, climate change, and water conservation, few studies have comprehensively evaluated their combined effects on regional water resources. This study addresses this gap by investigating the spatiotemporal changes in the water yield (WY) and water conservation capacity (WCC) of the Bashang region under different land use and climate scenarios for the year 2035. This research employs the FLUS model to predict the future land use and the InVEST model to estimate the WY and WCC under a natural development scenario (NDS), an agricultural production scenario (APS), an ecological protection scenario (EPS), and a land planning scenario (LPS). The results reveal that the WCC is primarily influenced by precipitation, land use, and the topography. This study finds that scenarios which focus on ecological protection and land use optimization, such as the EPS and LPS, significantly enhance the water conservation capacity of the study region Notably, the LPS scenario, which limits urban expansion and increases the amount of ecological land, provides the best balance between the water yield and conservation. The findings highlight the need for integrated approaches to land use and water resource management, particularly in agro-pastoral transitional zones. The unique contribution of this research lies in its comprehensive modeling approach, which combines land use, climate data, and water resource analysis, and which provides valuable insights for sustainable land and water management strategies. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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29 pages, 73598 KiB

Open AccessArticle

Spatiotemporal Dynamics and Driving Mechanisms of Ecosystem Services in the Beijing–Tianjin–Hebei Urban Agglomeration: Implications for Sustainable Land Use Planning

by Shuanqging Sheng and Jinchuan Huang

Land 2025, 14(5), 969; https://doi.org/10.3390/land14050969 - 30 Apr 2025

Viewed by 172

Abstract

The accelerating process of global urbanization has substantially reshaped land use patterns, exerting profound influences on the dynamics of ecosystem service provision. Effective and adaptive ecosystem management necessitates the quantitative identification and analysis of spatiotemporal variations in ecosystem services and their underlying driving [...] Read more.

The accelerating process of global urbanization has substantially reshaped land use patterns, exerting profound influences on the dynamics of ecosystem service provision. Effective and adaptive ecosystem management necessitates the quantitative identification and analysis of spatiotemporal variations in ecosystem services and their underlying driving mechanisms. Using the Beijing–Tianjin–Hebei urban agglomeration as a case study, this research examines land use dynamics from 2000 to 2020 and projects land use patterns for 2030 under alternative development scenarios. Building upon this foundation, the study quantifies the spatiotemporal evolution of four key ecosystem services—Carbon Storage (CS), Water Yield (WY), Habitat Quality (HQ), and Soil Retention (SDR)—from 2000 to 2030, while elucidating the differential impacts and underlying mechanisms of the driving factors on these services. The findings indicate that: (1) Between 2000 and 2020, cultivated land remained the dominant land use type (47.71%), followed by forestland (21.44%) and grassland (16.23%), whereas built-up land expanded significantly from 8.12% to 12.74%; (2) the proportion of medium-to-high CS areas reached 47.65%, high-value WY areas increased by 4.9%, low-value HQ areas expanded by 4.28%, and low-value SDR areas accounted for 84.44%; (3) the PLUS model validation yielded a Kappa coefficient of 86.1%, indicating high simulation accuracy. Scenario-based predictions suggest that under an ecological protection scenario, the proportion of medium-to-high CS areas would increase by 0.59%, whereas under an economic development scenario, these areas would decline by 2.57%, with low-value HQ areas expanding by 2.04%; (4) slope (X₂) was identified as the dominant factor influencing CS (q = 0.22), HQ (q = 0.36–0.42), and SDR (q = 0.42), while mean annual precipitation (X₄) played a crucial role in determining WY. Furthermore, socioeconomic activities, particularly increasing population density, exhibited a growing negative impact on HQ and CS, highlighting the intensifying role of anthropogenic interventions in shaping ecosystem service patterns. This study unveils the spatial heterogeneity of ecosystem services and their driving mechanisms in the context of urbanization, offering valuable insights to inform regional ecological conservation and sustainable development policies. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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23 pages, 17458 KiB

Open AccessArticle

Spatial–Temporal Evolution of Ecological Network Structure During 1967–2021 in Yongding River Floodplain

by Junyi Su, Minghao Wu and Zhicheng Liu

Land 2025, 14(5), 930; https://doi.org/10.3390/land14050930 - 24 Apr 2025

Viewed by 266

Abstract

Constructing a rational ecological network is crucial for balancing regional development with environmental protection. However, existing research typically emphasizes the analysis of overall patterns, lacking an in-depth exploration of the dynamic changes in key elements and the interactions between different components. Using the [...] Read more.

Constructing a rational ecological network is crucial for balancing regional development with environmental protection. However, existing research typically emphasizes the analysis of overall patterns, lacking an in-depth exploration of the dynamic changes in key elements and the interactions between different components. Using the Yongding River floodplain as a case study, this study applied morphological spatial pattern analysis, landscape connectivity metrics, and biodiversity assessments to identify core ecological source areas. Circuit theory was used to delineate ecological corridors and analyze network evolution across four key years, while graph theory facilitated an in-depth analysis of network structural characteristics. Furthermore, key areas for ecological restoration were identified within the floodplain. We found that the number of ecological source patches in the study area has remained relatively stable, though their total area has shown a fluctuating decline, accounting for approximately 10% of the floodplain. Additionally, ecological corridors have decreased significantly from 1967 to 2021, with a marked reduction in major corridors, leading to increased resistance to material and energy flow and a corresponding decline in network connectivity and stability. More importantly, current ecological pinch points are primarily distributed in a bead-like pattern along the Yongding River channel, while ecological barriers are concentrated in the northern and eastern floodplain, often at intersections of dense road networks and ecological corridors. These critical areas of fragmentation within the ecological network are prioritized for targeted ecological protection and restoration efforts. Overall, this study advances our understanding of the spatial distribution and composition of key ecological elements within river corridor networks and offers a framework for evaluating these networks through a multidimensional optimization approach for ecological source patches. At the same time, we conducted an in-depth analysis of key fragmentation areas in the Yongding River floodplain, providing valuable guidance for future ecological protection and restoration initiatives in river corridors. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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24 pages, 5381 KiB

Open AccessArticle

The Spatiotemporal Evolution of Land Use Ecological Efficiency in the Huaihai Economic Zone: Insights from a Multi-Dimensional Framework and Geospatial Modeling

by Gan Teng, Longqian Chen, Ting Zhang, Long Li, Jue Xiao and Linyu Ma

Land 2025, 14(4), 883; https://doi.org/10.3390/land14040883 - 16 Apr 2025

Viewed by 192

Abstract

Existing studies on eco-efficiency evaluation often neglect the social and ecological benefits of land use, as well as the spatial heterogeneity in the effects of natural factors. This study aims to establish a systematic eco-efficiency evaluation framework and examine the dynamic impacts of [...] Read more.

Existing studies on eco-efficiency evaluation often neglect the social and ecological benefits of land use, as well as the spatial heterogeneity in the effects of natural factors. This study aims to establish a systematic eco-efficiency evaluation framework and examine the dynamic impacts of various factors on land use eco-efficiency at the county scale. An evaluation framework integrating economic, ecological, and social outputs was constructed using multi-source remote sensing data. The super-efficiency Slack-Based Measure model and exploratory spatial data analysis method were employed to analyze the land use eco-efficiency and its spatiotemporal characteristics in the Huaihai Economic Zone from 2000 to 2022. Furthermore, the enhanced Geographically and Temporally Weighted Regression model was applied to examine the spatiotemporal heterogeneity of industrial structure, ecological background, land use, and other factors influencing land use eco-efficiency. The results indicated the following: (1) The average land use eco-efficiency in the Huaihai Economic Zone followed a pronounced “U-shaped” pattern, initially decreasing before increasing. At the county level, efficiency values generally showed an upward trend, with municipal districts or county-level cities accounting for more than half of the areas. (2) A weak positive spatial agglomeration of land use eco-efficiency was observed at the county level. High-efficiency areas shifted from a dispersed pattern to a “T-shaped” spatial distribution, while low-efficiency areas gradually converged toward the southern regions, evolving from an “H-shaped” cluster. (3) The influence of industrial structure, urbanization, environmental regulation, ecological factors, and land use on ecological efficiency exhibited clear spatial and temporal heterogeneity. The multi-dimensional land use eco-efficiency evaluation offers an effective tool for comprehensively assessing ecological conservation, environmental sustainability, and socio-economic development. It also provides valuable insights for improving land use eco-efficiency at the county level. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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26 pages, 7890 KiB

Open AccessArticle

Spatiotemporal Variability and Drivers of Cropland Non-Agricultural Conversion Across Mountainous County Types: Evidence from the Qian-Gui Karst Region, China

by Qingping Lu, Siji Zhu, Zhaofu Xiao, Guifang Zhu, Jie Li, Jiahao Cui, Wen He and Jun Sun

Agriculture 2025, 15(7), 795; https://doi.org/10.3390/agriculture15070795 - 7 Apr 2025

Cited by 1 | Viewed by 381

Abstract

The accelerating conversion of agricultural land to non-agricultural uses poses critical threats to food security and sustainable land management, particularly in ecologically fragile karst mountainous regions. This study investigated the spatiotemporal patterns and driving mechanisms of cropland non-agricultural conversion (CNAC) in the Qian-Gui [...] Read more.

The accelerating conversion of agricultural land to non-agricultural uses poses critical threats to food security and sustainable land management, particularly in ecologically fragile karst mountainous regions. This study investigated the spatiotemporal patterns and driving mechanisms of cropland non-agricultural conversion (CNAC) in the Qian-Gui karst region (Guangxi and Guizhou, China) from 2000 to 2020, employing land use datasets and socioeconomic indicators through geographically weighted regression (GWR) modeling. The results showed that (1) from 2000 to 2020, the CNAC rate in the Qian-Guizhou karst mountainous region reached 2.03%. The area of CNAC increased by 14.60 × 10⁴ hm², increasing 1.74 times in 2010–2020 compared to 2000–2010, showing a trend of rapid growth. Specifically, the growth rate of the CNAC area was the highest in apparent mountainous (110.36%) and quasi-mountainous counties (100.5%), followed by semi-mountainous counties (95.28%), while entirely mountainous (40.89%) and pure hilly counties (37.68%) experienced the lowest growth, revealing distinct regional disparities. (2) Spatially, CNAC exhibited a pattern of “high in the north and south, low in the central region”, and the overall level of CNAC displayed significant regional imbalances, with extreme grades distributed in provincial capitals, high and medium grades concentrated in prefecture-level city districts, and light and low grades mainly located in counties and districts (accounting for more than 55.56% of the total number of research units in the two time periods). (3) There was significant spatial heterogeneity in the driving effect of factors influencing CNAC. Agricultural output and population density showed the strongest positive correlations; effectively irrigated areas exhibited a growing influence over time (except for pure hilly counties); rocky desertification areas exerted a strengthened influence on CNAC in pure hilly counties, while their impact was relatively lower in other regions compared to other indicators. Therefore, when formulating policies to protect farmland, it is essential to take into account the specific conditions of different types of counties in mountainous areas and adopt management measures tailored to these regional characteristics. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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25 pages, 18366 KiB

Open AccessArticle

Assessing the Supply–Demand Matching and Spatial Flow of Urban Cultural Ecosystem Services: Based on Geospatial Data and User Interaction Data

by Linru Li, Yu Bai, Xuefeng Yuan and Feiyan Li

Land 2025, 14(4), 773; https://doi.org/10.3390/land14040773 - 3 Apr 2025

Viewed by 414

Abstract

Cultural ecosystem services (CESs) reflect the interaction between ecosystems and human well-being. Owing to constraints in data availability and existing methodological limitations, deriving information from non-material ecosystem attributes was inadequate. We took Yulin City, located in the northern Shaanxi Loess Plateau, as a [...] Read more.

Cultural ecosystem services (CESs) reflect the interaction between ecosystems and human well-being. Owing to constraints in data availability and existing methodological limitations, deriving information from non-material ecosystem attributes was inadequate. We took Yulin City, located in the northern Shaanxi Loess Plateau, as a case study. Based on open-source geospatial data and user interaction data from social media, a coupled multi-source model was applied to elucidate the spatial distribution of CESs’ supply–demand flow. The Maxent and LDA model were utilized to quantify CES supply–demand, whereas the breakpoint and gravity model were applied to explain the direction and intensity of CES flow. The results indicated the following: (1) aesthetic was the most perceivable CES in Yulin, with 27% high supply areas and four demand topics. And the perception of the educational CES was the least pronounced, with only 2% of high supply areas and two demand topics. (2) Yulin exhibited a notable mismatching in CES supply–demand, with the supply–demand matching area constituting only approximately 10%. In the center of the city, CESs displayed a spatial pattern of a supply–demand deficit, while areas farther from the city center presented a spatial pattern of a supply–demand surplus. (3) The flow of CESs followed a pattern of movement from peripheral counties to central counties and from less developed counties to more developed counties. We proposed the following targeted recommendations: introducing low-perception CESs to promote the enhancement of ecosystem services (ESs); and alleviating CES supply–demand mismatches by enhancing transportation accessibility and protecting the ecological environment. Simultaneously, attention should be directed towards the developmental disparities between counties, providing differentiated guidance for CES spatial flow. Our study provided a theoretical foundation for understanding CES supply–demand flow and offered scientific insights for the spatial development of urban CES. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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25 pages, 2711 KiB

Open AccessArticle

Assessing Classification Accuracy as a Criterion for Evaluating the Performance of Seven Topographic Correction Algorithms in the Trinidad Mountains, Cuba

by Minerva Sánchez-Llull, Laura Castellanos Torres, Alain Muñoz Caravaca, Gustavo Martín Morales, Sabine Sauvage, Yeleny Zulueta-Véliz, Eugenio Jesús Olalde Chang, Julio León Cabrera, Leosveli Vasallo-Rodríguez, Sylvain Ouillon and José-Miguel Sánchez-Pérez

Remote Sens. 2025, 17(6), 1032; https://doi.org/10.3390/rs17061032 - 15 Mar 2025

Viewed by 706

Abstract

To contribute to SDG-15 about the conservation of terrestrial ecosystems, the effective management of land resources is required. In this respect, determining the land use and cover (LUC) based on remote sensing constitutes a strength. For the Arimao watershed in the province Cienfuegos [...] Read more.

To contribute to SDG-15 about the conservation of terrestrial ecosystems, the effective management of land resources is required. In this respect, determining the land use and cover (LUC) based on remote sensing constitutes a strength. For the Arimao watershed in the province Cienfuegos of Cuba, the main difficulty in determining the LUC is related to the topographic correction in the mountains of Trinidad. This study aims to validate the effectiveness of seven topographic correction methods using classification accuracy as a criterion. For this purpose, the mountain area was cut out on the Landsat-8 OLI image of December 2020, based on its physical–geographical and geological characteristics. Seven topographic correction algorithms were applied: Cosine correction, Improved cosine, C-correction, Minnaert, Minnaert with the slope, including Riano and others by Law, and the Normalization correction. To evaluate their performance, three criteria were used: visual interpretation, statistical analysis, and assessing classification accuracy taking into account eight cover classes. The obtained results showed a higher effectiveness of the Minnaert correction with slope and roughness coefficient k = 0.3, with an overall accuracy of 94.08%. The user and producer accuracies increased the performance for almost all forest classes. For the mountains of Trinidad, the non-forest classes were not affected by the topographic correction, so it was possible to apply the topographic correction algorithms to the entire area. The results have demonstrated the necessity of applying the criterion of accuracy assessment to select the best topographic correction. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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25 pages, 7898 KiB

Open AccessArticle

Spatiotemporal Evolution and Obstacle Factor Analysis of Land Ecological Security in the Surrounding Areas of Beijing, China

by Yutong Wang and Jianyu Yang

Land 2025, 14(3), 457; https://doi.org/10.3390/land14030457 - 23 Feb 2025

Viewed by 333

Abstract

Land ecological security is essential for sustainable land resource use and socioeconomic development. This study presents an evaluation index system combining single-indicator quantification, multi-indicator integration, and multi-criteria comprehensive assessment. It evaluates the land ecological security of 13 regions in the surrounding areas of [...] Read more.

Land ecological security is essential for sustainable land resource use and socioeconomic development. This study presents an evaluation index system combining single-indicator quantification, multi-indicator integration, and multi-criteria comprehensive assessment. It evaluates the land ecological security of 13 regions in the surrounding areas of Beijing from 2012 to 2021. Using Natural Breaks classification and hotspot and coldspot analysis, the study explores the temporal trends and spatial patterns of land ecological security. The obstacle degree model identifies constraining factors, and geographically weighted regression analyzes spatial heterogeneity. The results show the following: (1) The land ecological security index increased from 2012 to 2021, with rapid growth in southeastern Beijing and the three northern counties of Langfang, indicating high security levels. (2) The land ecological security of the region exhibits a symmetrical north–south distribution, with high-security areas concentrated in the Langfang Urban Area, Sanhe City, and Dachang County, while low-security areas are primarily found in Chicheng and Zhuolu counties. (3) The key factors hindering land ecological security are land economic density, fixed-asset investment per unit area, and GDP growth rate, with varying impacts across counties, showing significant spatial heterogeneity. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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15 pages, 6524 KiB

Open AccessFeature PaperArticle

Global Pattern of Vegetation Homogeneity and Its Impact on Land Surface Temperature

by Ehsan Rahimi, Pinliang Dong and Chuleui Jung

Land 2025, 14(2), 421; https://doi.org/10.3390/land14020421 - 17 Feb 2025

Viewed by 424

Abstract

Recent advancements in texture-based metrics have improved the representation of landscape heterogeneity, yet global-scale analyses of the relationship between vegetation homogeneity and land surface temperature (LST) remain limited. This study addresses this gap by examining the correlation between Enhanced Vegetation Index (EVI)-derived texture [...] Read more.

Recent advancements in texture-based metrics have improved the representation of landscape heterogeneity, yet global-scale analyses of the relationship between vegetation homogeneity and land surface temperature (LST) remain limited. This study addresses this gap by examining the correlation between Enhanced Vegetation Index (EVI)-derived texture metrics and LST worldwide. We used texture-based metrics from the EVI to assess landscape homogeneity, with LST data from the 2015 MODIS MOD11A1 V6.1 product at a 1 km spatial resolution. Correlation analyses and nonlinear regression models were applied to explore how EVI homogeneity relates to LST across latitudes. Our findings reveal a significant positive correlation between EVI homogeneity and LST, with the strongest association in the Northern Hemisphere (R² = 49.3%), followed by a moderate relationship in the Southern Hemisphere (R² = 21.1%). In tropical regions (−10° to 10° latitudes), the association is weaker but still significant (R² = 15.1%). The distribution of EVI homogeneity follows a Gaussian curve, peaking in mid-latitudes (from −35° to −15° in the Southern Hemisphere and from 15° to 35° in the Northern Hemisphere), while tropical regions exhibit consistently low homogeneity with minimal variation. Our results indicate that regions with higher EVI homogeneity, representing less fragmented vegetation, tend to experience higher LST, whereas areas with more fragmented vegetation (lower homogeneity) exhibit cooler temperatures. Our findings offer valuable insights into the role of vegetation structure in regulating surface temperature across diverse ecosystems. The study highlights the potential for texture-based metrics to enhance environmental monitoring, contributing to improved climate adaptation strategies and sustainable land management practices globally. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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25 pages, 18472 KiB

Open AccessArticle

Multi-Dimensional Analysis of Urban Growth Characteristics Integrating Remote Sensing Data: A Case Study of the Beijing–Tianjin–Hebei Region

by Yuan Zhou and You Zhao

Remote Sens. 2025, 17(3), 548; https://doi.org/10.3390/rs17030548 - 6 Feb 2025

Cited by 1 | Viewed by 810

Abstract

Sustainable urban growth is an important issue in urbanization. Existing studies mainly focus on urban growth from the two-dimensional morphology perspective due to limited data. Therefore, this study aimed to construct a framework for estimating long-term time series of building volume by integrating [...] Read more.

Sustainable urban growth is an important issue in urbanization. Existing studies mainly focus on urban growth from the two-dimensional morphology perspective due to limited data. Therefore, this study aimed to construct a framework for estimating long-term time series of building volume by integrating nighttime light data, land use data, and existing building volume data. Indicators of urban horizontal expansion (UHE), urban vertical expansion (UVE), and comprehensive development intensity (CDI) were constructed to describe the spatiotemporal characteristics of the horizontal growth, vertical growth, and comprehensive intensity of the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2013 to 2023. The UHE and UVE increased from 0.44 and 0.30 to 0.50 and 0.53, respectively, indicating that BTH has simultaneously experienced horizontal growth and vertical growth and the rate of vertical growth was more significant. The UVE in urban areas and suburbs was higher and continuously increasing; in particular, the UVE in the suburbs changed from 0.35 to 0.60, showing the highest rate of increase. The most significant UHE growth was mainly concentrated in rural areas. The spatial pattern of the CDI was stable, showing a declining trend along the urban–suburb–rural gradient, and CDI growth from 2013 to 2023 was mainly concentrated in urban and surrounding areas. In terms of temporal variation, the CDI growth during 2013–2018 was significant, while it slowed after 2018 because economic development had leveled off. Economic scale, UHE, and UVE were the main positive factors. Due to the slowdown of CDI growth and population growth, economic activity intensity, population density, and improvement in the living environment showed a negative impact on CDI change. The results confirm the validity of estimating the multi-dimensional growth of regions using remote sensing data and provide a basis for differentiated spatial growth planning in urban, suburban, and rural areas. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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23 pages, 8347 KiB

Open AccessArticle

Study on the Extraction of Topsoil-Loss Areas of Cultivated Land Based on Multi-Source Remote Sensing Data

by Xinle Zhang, Chuan Qin, Shinai Ma, Jiming Liu, Yiang Wang, Huanjun Liu, Zeyu An and Yihan Ma

Remote Sens. 2025, 17(3), 547; https://doi.org/10.3390/rs17030547 - 6 Feb 2025

Viewed by 934

Abstract

Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, and yield. However, soil degradation affects over one-third of global land, with topsoil loss emerging as a significant form of this degradation, posing a grave threat to agricultural [...] Read more.

Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, and yield. However, soil degradation affects over one-third of global land, with topsoil loss emerging as a significant form of this degradation, posing a grave threat to agricultural sustainability and socio-economic development. Therefore, accurate monitoring of topsoil-loss distribution is essential for formulating effective soil protection and management strategies. Traditional survey methods are limited by time-consuming and labor-intensive processes, high costs, and complex data processing. These limitations make it particularly challenging to meet the demands of large-scale research and efficient information processing. Therefore, it is imperative to develop a more efficient and accurate extraction method. This study focuses on the Heshan Farm in Heilongjiang Province, China, as the research subject and utilizes remote sensing technology and machine learning methods. It introduces multi-source data, including Sentinel-2 satellite imagery and Digital Elevation Model (DEM) data, to design four extraction schemes. (1) spectral feature extraction; (2) spectral feature + topographic feature extraction; (3) spectral feature + index extraction; (4) spectral feature + topographic feature + index extraction. Models for topsoil loss identification based on Random Forest (RF) and Support Vector Machine (SVM) algorithms are developed, and the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the models. The performance of the models is evaluated using overall accuracy and Kappa coefficient indicators. The results show that Scheme 4, which integrates spectral features, topographic features, and various indices, performs the best in extraction effects. The RF model demonstrates higher classification accuracy than the SVM model. The optimized PSO-RF and PSO-SVM models show significant improvements in extraction accuracy, especially the PSO-RF model, with an overall accuracy of 0.97 and a Kappa coefficient of 0.94. The PSO-RF model using Scheme 4 improves OA by 34.72% and Kappa by 38.81% compared to the RF model in Scheme 1. Topsoil loss has a significant negative impact on crop growth, severely restricting the normal growth and development of crops. This study provides an efficient technical means for monitoring soil degradation in black-soil regions and offers a scientific basis for formulating effective agricultural ecological protection strategies, thereby promoting the sustainable management of soil resources. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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18 pages, 27408 KiB

Open AccessArticle

A Multi-Scenario Simulation and Driving Factor Analysis of Production–Living–Ecological Land in China’s Main Grain Producing Areas: A Case Study of the Huaihe River Eco-Economic Belt

by Wenfeng Hu, Junhao Cheng, Mengtian Zheng, Xiaolong Jin, Junqiang Yao and Fei Guo

Agriculture 2025, 15(3), 349; https://doi.org/10.3390/agriculture15030349 - 6 Feb 2025

Cited by 1 | Viewed by 776

Abstract

The Huaihe River Eco-Economic Belt (HREEB) is a pivotal region in China’s national strategic planning. Land use in this region is crucial to improving ecological quality and ensuring food security. Using the PLUS model and Geodetector, we evaluated the contribution and interaction of [...] Read more.

The Huaihe River Eco-Economic Belt (HREEB) is a pivotal region in China’s national strategic planning. Land use in this region is crucial to improving ecological quality and ensuring food security. Using the PLUS model and Geodetector, we evaluated the contribution and interaction of 10 drivers to production–living–ecological land (PLEL) and performed multi-scenario simulations of PLEL. The results show that the following: (1) Ecological land is mainly affected by elevation (contribution value > 0.16 for forest, grassland, and water). Production land is influenced by topographic relief, elevation, and GDP per capita (contribution value > 0.13). Living land is driven by topographic relief, GDP per capita, elevation, and population density (contribution value > 0.13). Interaction analysis shows nonlinear or two-factor enhancement among factors. (2) Ecological land in the HREEB has a relatively stable spatial pattern. Simulation results indicate that ecological land will not exceed a 12% change in the next 50 years. (3) By 2075, simulations under various scenarios predict significant changes in land area. Compared with the natural development scenario, production land increased by 14.8% in the farmland protection scenario, and living land increased by 14.3% in the urban development scenario. This research is vital for managing and developing PLEL resources within the HREEB. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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21 pages, 4918 KiB

Open AccessArticle

Identification, Mechanism and Countermeasures of Cropland Abandonment in Northeast Guangdong Province

by Xiaojian Li, Linbing Ma and Xi Liu

Land 2025, 14(2), 246; https://doi.org/10.3390/land14020246 - 24 Jan 2025

Cited by 1 | Viewed by 848

Abstract

Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment [...] Read more.

Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment to safeguard food security. However, due to limitations in data sources and attribution methods, previous studies struggled to comprehensively characterize the driving mechanisms of abandoned land. Using data from Sentinel time series remote-sensing images, we employed the land use change trajectory method to map cropland abandonment in Jiaoling County from 2019 to 2023. Furthermore, we proposed a novel analytical framework to quantify the influence pathways and interaction effects driving cropland abandonment. The results indicated that: (1) The overall accuracy of the abandoned land extraction was 79.6%. During the study period, the abandonment rate in Jiaoling County showed a trend of a “gradual rise followed by a sharp decline”, and the abandoned area reached its maximum in 2021. The abandonment phenomenon in the southeastern rural areas was serious and stubborn. (2) The slope has the greatest explanatory power for abandonment, followed by the total cultivated area, aggregation index of cropland, and distance to road. Each driving factor has a threshold effect. (3) Topography, location, and agriculture driving factors directly or indirectly affect the abandonment rate, with direct influences of 0.247, 0.255, and −0.256, respectively. The research findings offer valuable scientific guidance for managing abandoned land and deepen our understanding of its formation mechanisms. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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19 pages, 30519 KiB

Open AccessArticle

Analyzing Vegetation Heterogeneity Trends in an Urban-Agricultural Landscape in Iran Using Continuous Metrics and NDVI

by Ehsan Rahimi and Chuleui Jung

Land 2025, 14(2), 244; https://doi.org/10.3390/land14020244 - 24 Jan 2025

Cited by 3 | Viewed by 718

Abstract

Understanding vegetation heterogeneity dynamics is crucial for assessing ecosystem resilience, biodiversity patterns, and the impacts of environmental changes on landscape functions. While previous studies primarily focused on NDVI pixel trends, shifts in landscape heterogeneity have often been overlooked. To address this gap, our [...] Read more.

Understanding vegetation heterogeneity dynamics is crucial for assessing ecosystem resilience, biodiversity patterns, and the impacts of environmental changes on landscape functions. While previous studies primarily focused on NDVI pixel trends, shifts in landscape heterogeneity have often been overlooked. To address this gap, our study evaluated the effectiveness of continuous metrics in capturing vegetation dynamics over time, emphasizing their utility in short-term trend analysis. The study area, located in Iran, encompasses a mix of urban and agricultural landscapes dominated by farming-related vegetation. Using 11 Landsat 8 OLI images from 2013 to 2023, we calculated NDVI to analyze vegetation trends and heterogeneity dynamics. We applied three categories of continuous metrics: texture-based metrics (dissimilarity, entropy, and homogeneity), spatial autocorrelation indices (Getis and Moran), and surface metrics (Sa, Sku, and Ssk) to assess vegetation heterogeneity. By generating slope maps through linear regression, we identified significant trends in NDVI and correlated them with the slope maps of the continuous metrics to determine their effectiveness in capturing vegetation dynamics. Our findings revealed that Moran’s Index exhibited the highest positive correlation (0.63) with NDVI trends, followed by Getis (0.49), indicating strong spatial clustering in areas with increasing NDVI. Texture-based metrics, particularly dissimilarity (0.45) and entropy (0.28), also correlated positively with NDVI dynamics, reflecting increased variability and heterogeneity in vegetation composition. In contrast, negative correlations were observed with metrics such as homogeneity (−0.41), Sku (−0.12), and Ssk (−0.24), indicating that increasing NDVI trends were associated with reduced uniformity and surface dominance. Our analysis underscores the complementary roles of these metrics, with spatial autocorrelation metrics excelling in capturing clustering patterns and texture-based metrics highlighting value variability within clusters. By demonstrating the utility of spatial autocorrelation and texture-based metrics in capturing heterogeneity trends, our findings offer valuable tools for land management and conservation planning. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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17 pages, 22853 KiB

Open AccessArticle

Regional Carbon Storage Dynamics Driven by Tea Plantation Expansion: Insights from Meitan County, China

by Renhui Zuo, Yan Ma, Ming Tang, Hao Luo, Junqin Li, Tao Liao, Yuanfang Gong, Shunfu Zhang, Jiyi Gong and Yin Yi

Land 2025, 14(2), 227; https://doi.org/10.3390/land14020227 - 22 Jan 2025

Cited by 1 | Viewed by 773

Abstract

Over the past two decades, the tea plantation area in Meitan County, China, has expanded nearly 30-fold, driving significant land use and cover changes (LUCC) with unclear impacts on regional carbon storage. This study uses the PLUS-InVEST model to analyze LUCC impacts on [...] Read more.

Over the past two decades, the tea plantation area in Meitan County, China, has expanded nearly 30-fold, driving significant land use and cover changes (LUCC) with unclear impacts on regional carbon storage. This study uses the PLUS-InVEST model to analyze LUCC impacts on carbon storage from 2000 to 2020 and predict future changes by 2060. Results show a decline in total carbon storage from 3977.83 × 10⁴ t in 2000 to 3960.85 × 10⁴ t in 2020, primarily due to reductions in cultivated land and grassland. Although carbon storage in forest land and tea plantations increased, the overall trend remained negative. Multi-scenario simulations indicate that the sustainable development scenario (SDS) mitigates carbon loss, with a decrease of 31.53 × 10⁴ t, compared to larger reductions in the natural development (NDS) and economic development (EDS) scenarios. Under the SDS scenario, carbon storage in forest land and grassland increased while construction land expansion was controlled. This study emphasizes optimizing land use and agricultural management to enhance carbon sequestration and protect ecosystems, highlighting the need for a balance between ecological protection and economic development for sustainable carbon management in Meitan County. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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28 pages, 13027 KiB

Open AccessArticle

From Fields to Microclimate: Assessing the Influence of Agricultural Landscape Structure on Vegetation Cover and Local Climate in Central Europe

by Jan Kuntzman and Jakub Brom

Remote Sens. 2025, 17(1), 6; https://doi.org/10.3390/rs17010006 - 24 Dec 2024

Viewed by 806

Abstract

Agricultural intensification through simplification and specialization has homogenized diverse landscapes, reducing their heterogeneity and complexity. While the negative impact of large, simplified fields on biodiversity has been well-documented, the role of landscape structure in mitigating climatic extremes and stabilizing climate is becoming increasingly [...] Read more.

Agricultural intensification through simplification and specialization has homogenized diverse landscapes, reducing their heterogeneity and complexity. While the negative impact of large, simplified fields on biodiversity has been well-documented, the role of landscape structure in mitigating climatic extremes and stabilizing climate is becoming increasingly important. Despite considerable knowledge of landscape cover types, understanding of how landscape structure influences climatic characteristics remains limited. To explore this further, we studied an area along the Czech–Austrian border, where socio-political factors have created stark contrasts in landscape structure, despite a similar topography. Using Land Parcel Information System (LPIS) data, we analyzed the landscape structure on both sides and processed eight Landsat 8 and 9 OLI/TIRS scenes from the 2022 vegetation season to calculate spectral indices (NDVI, NDMI) and microclimatic features (surface temperature, albedo, and energy fluxes). Our findings revealed significant differences between the two regions. Czech fields, with their larger, simpler structure and lower edge density, can amplify local climatic extremes. In contrast, the distribution of values on the Austrian side was more even, likely due to the greater diversity of cultivated crops, a more spatially diverse landscape, and a balanced spread of agricultural activities over time. In light of climate change and biodiversity conservation, these results emphasize the need to protect and restore landscape complexity to enhance resilience and environmental stability. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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24 pages, 18784 KiB

Open AccessArticle

Large Offsets in the Impacts Between Enhanced Atmospheric and Soil Water Constraints and CO₂ Fertilization on Dryland Ecosystems

by Feng Tian, Lei Wang, Ye Yuan and Jin Chen

Remote Sens. 2024, 16(24), 4733; https://doi.org/10.3390/rs16244733 - 18 Dec 2024

Viewed by 795

Abstract

Greening dryland ecosystems greatly benefits from significant CO₂ fertilization. This greening trend across global drylands, however, has also been severely constrained by enhancing atmospheric and soil water (SW) deficits. Thus far, the relative offsets in the contributions between the atmospheric vapor pressure [...] Read more.

Greening dryland ecosystems greatly benefits from significant CO₂ fertilization. This greening trend across global drylands, however, has also been severely constrained by enhancing atmospheric and soil water (SW) deficits. Thus far, the relative offsets in the contributions between the atmospheric vapor pressure deficit (VPD), SW at varying depths, and CO₂ fertilization to vegetation dynamics, as well as the differences in the impacts of decreasing SW at different soil depths on dryland ecosystems over long periods, remain poorly recorded. Here, this study comprehensively explored the relative offsets in the contributions to vegetation dynamics between high VPD, low SW, and rising CO₂ concentration across global drylands during 1982–2018 using process-based models and satellite-observed Leaf Area Index (LAI), Gross Primary Productivity (GPP), and solar-induced chlorophyll fluorescence (SIF). Results revealed that decreasing-SW-induced reductions of LAI in dryland ecosystems were larger than those caused by rising VPD. Furthermore, dryland vegetation was more severely constrained by decreasing SW on the subsurface (7–28 cm) among various soil layers. Notable offsets were found in the contributions between enhanced water constraints and CO₂ fertilization, with the former offsetting approximately 38.49% of the beneficial effects of the latter on vegetation changes in global drylands. Process-based models supported the satellite-observed finding that increasing water constraints failed to overwhelmingly offset significant CO₂ fertilization on dryland ecosystems. This work emphasizes the differences in the impact of SW at different soil depths on vegetation dynamics across global drylands as well as highlights the far-reaching importance of significant CO₂ fertilization to greening dryland ecosystems despite increasing atmospheric and SW constraints. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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15 pages, 18588 KiB

Open AccessArticle

Analysis of Annual Spatiotemporal Variations in Carbon Stock in the Urban Agglomeration of the Middle Yangtze River Basin, China

by Zhenbo Du, Cong Ou, Yue An, Jingbo Chen, Yu Meng and Fen Chen

Land 2024, 13(12), 2089; https://doi.org/10.3390/land13122089 - 4 Dec 2024

Viewed by 788

Abstract

Terrestrial ecosystem carbon stock (TECS) is critical to socioeconomic development and ecosystem services and is jointly affected by land use and cover and climate change. However, the dynamics of long-term annual TECS levels in urban agglomeration remain largely unknown, and research mostly ignores [...] Read more.

Terrestrial ecosystem carbon stock (TECS) is critical to socioeconomic development and ecosystem services and is jointly affected by land use and cover and climate change. However, the dynamics of long-term annual TECS levels in urban agglomeration remain largely unknown, and research mostly ignores the spatial heterogeneity of climate factors, compromising sustainable environmental management and land planning strategies. To this end, we integrated field observations of carbon density, land use, and climate factors to map the annual distribution of TECS and analyzed their spatiotemporal variations and policy implications in the urban agglomeration of the middle Yangtze River Basin in China from 1990 to 2020. The results showed that 43,855.47 km² of the land of the urban agglomeration changed from 1990 to 2020, accounting for 12.54% of the study area. The farmland and forest land area fluctuated and reduced, and the construction land area increased significantly. The increase in construction land was mainly from farmland and forest land. The TECS in urban agglomerations underwent a remarkable change, the overall trend fluctuated downward, and the maximum interannual variation was 1560 Tg. The transfer of construction land, farmland, forest land, shrubs, grassland, and other land mainly caused the change in carbon storage. Due to abnormal climate change, the urban agglomeration in some areas illustrated carbon storage with a spatially aggregated distribution. When considering the impact of climate change on carbon density, the TECS changes of land types other than forest land were found to be consistent with the area change but more significant due to climate change. The research results can provide reference data for regional land management policy formulation and realization of “dual carbon” goals. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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16 pages, 2018 KiB

Open AccessArticle

Deep Learning and Remote Sensing for Restoring Abandoned Agricultural Lands in the Middle Volga (Russia)

by Artur Gafurov and Maxim Ivanov

Land 2024, 13(12), 2054; https://doi.org/10.3390/land13122054 - 30 Nov 2024

Viewed by 998

Abstract

Abandoned agricultural lands in the Middle Volga region of Russia, which appeared because of socio-economic transformations after the collapse of the USSR and the liquidation of collective farms, represent a significant potential for increasing agricultural production and economic development of the region. This [...] Read more.

Abandoned agricultural lands in the Middle Volga region of Russia, which appeared because of socio-economic transformations after the collapse of the USSR and the liquidation of collective farms, represent a significant potential for increasing agricultural production and economic development of the region. This study develops a comprehensive approach to assessing the suitability of these lands for return to agricultural turnover using machine learning methods and remote sensing data. Sentinel-2 satellite imagery and a deep neural network based on MAnet architecture with Mix Vision Transformer encoder (MiT-b5), which achieved an accuracy of 93.4% and an IoU coefficient of 0.84, were used for semantic segmentation of modern agricultural land. Land use dynamics since 1985 were analysed using Landsat 4–9 data, revealing significant areas of abandoned arable land. Land suitability was assessed, taking into account natural resource factors such as topography, soils and climatic conditions. The results showed that the total area of land suitable for reclaimed land is 2,014,845 ha, which could lead to an increase in wheat yield by 7.052 million tons. The potential cumulative net profit is estimated at 35.26 billion rubles (about US$352.6 million). The main conclusions indicate the significant economic and social potential of returning abandoned land to agricultural turnover, which requires a comprehensive approach that includes investment in infrastructure and the introduction of modern agro-technologies. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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24 pages, 25821 KiB

Open AccessArticle

Impact of Paddy Field Expansion on Ecosystem Services and Associated Trade-Offs and Synergies in Sanjiang Plain

by Xilong Dai, Linghua Meng, Yong Li, Yunfei Yu, Deqiang Zang, Shengqi Zhang, Jia Zhou, Dan Li, Chong Luo, Yue Wang and Huanjun Liu

Agriculture 2024, 14(11), 2063; https://doi.org/10.3390/agriculture14112063 - 16 Nov 2024

Cited by 1 | Viewed by 1296

Abstract

In recent decades, the integrity and security of the ecosystem in the Sanjiang Plain have faced severe challenges due to land reclamation. Understanding the impact of paddy field expansion on regional ecosystem services (ESs), as well as revealing the trade-offs and synergies (TOS) [...] Read more.

In recent decades, the integrity and security of the ecosystem in the Sanjiang Plain have faced severe challenges due to land reclamation. Understanding the impact of paddy field expansion on regional ecosystem services (ESs), as well as revealing the trade-offs and synergies (TOS) between these services to achieve optimal resource allocation, has become an urgent issue to address. This study employs the InVEST model to map the spatial and temporal dynamics of five key ESs, while the Optimal Parameter Geodetector (OPGD) identifies primary drivers of these changes. Correlation analysis and Geographically Weighted Regression (GWR) reveal intricate TOS among ESs at multiple scales. Additionally, the Partial Least Squares-Structural Equation Model (PLS-SEM) elucidates the direct impacts of paddy field expansion on ESs. The main findings include the following: (1) The paddy field area in the Sanjiang Plain increased from 5775 km² to 18,773.41 km² from 1990 to 2020, an increase of 12,998.41 km² in 40 years. And the area of other land use types has generally decreased. (2) Overall, ESs showed a recovery trend, with carbon storage (CS) and habitat quality (HQ) initially decreasing but later improving, and consistent increases were observed in soil conservation, water yield (WY), and food production (FP). Paddy fields, drylands, forests, and wetlands were the main ES providers, with soil type, topography, and NDVI emerging as the main influencing factors. (3) Distinct correlations among ESs, where CS shows synergies with HQ and SC, while trade-offs are noted between CS and both WY and FP. These TOS demonstrate significant spatial heterogeneity and scale effects across subregions. (4) Paddy field expansion enhances regional SC, WY, and FP, but negatively affects CS and HQ. These insights offer a scientific basis for harmonizing agricultural development with ecological conservation, enriching our understanding of ES interrelationships, and guiding sustainable ecosystem management and policymaking. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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18 pages, 18769 KiB

Open AccessArticle

Analysis on Ecological Network Pattern Changes in the Pearl River Delta Forest Urban Agglomeration from 2000 to 2020

by Shengrong Wei, Tao Yu, Ping Ji, Yundan Xiao, Xiaoyao Li, Naijing Zhang and Zhenwei Liu

Remote Sens. 2024, 16(20), 3800; https://doi.org/10.3390/rs16203800 - 12 Oct 2024

Cited by 1 | Viewed by 1106

Abstract

The advancement of urbanization has led to a decline in the ecological function and environmental quality of cities, seriously reducing the services and sustainable development capacity of urban ecosystems. The construction of the National Forest Urban Agglomeration of China is conducive to alleviating [...] Read more.

The advancement of urbanization has led to a decline in the ecological function and environmental quality of cities, seriously reducing the services and sustainable development capacity of urban ecosystems. The construction of the National Forest Urban Agglomeration of China is conducive to alleviating the ecological and environmental problems brought about by rapid urbanization and promoting sustainable urban development. A time series analysis of ecological network changes can quickly and effectively explore the development and changes of ecological spatial patterns over time. Identifying ecological protection and restoration areas in urban agglomerations is an important way to promote ecosystem restoration and optimize ecological networks. This paper takes the Pearl River Delta forest urban agglomeration as the research area, uses multi-source remote sensing data from 2000 to 2020 (every 5 years), identifies ecological sources based on the morphological spatial pattern analysis (MSPA) method, generates ecological corridors based on the minimum cumulative resistance (MCR) model, constructs a time series ecological network pattern in the Pearl River Delta region, and analyzes the evolution process of the ecological network pattern over time. The results indicate that over time, the core green area in the ecological network pattern of the Pearl River Delta first decreased and then increased, and the complexity of ecological corridors first decreased and then increased. The main reason is that the urbanization process in the early 21st century led to severe ecological fragmentation. Under the promotion of the national forest urban agglomeration construction, the ecological network pattern of the Pearl River Delta was restored in 2015 and 2020. The time series analysis of the ecological network pattern in the Pearl River Delta region of this research confirms the effectiveness of the construction of forest urban agglomerations, providing a scientific reference for the identification of ecological networks and optimization of spatial patterns in forest urban agglomerations. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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21 pages, 14551 KiB

Open AccessArticle

Detection of the Evolution Process of Desertification in Gulang County Based on Long Series and Similar Time Images

by Panpan Liu, Bing Guo and Rui Zhang

Land 2024, 13(10), 1652; https://doi.org/10.3390/land13101652 - 10 Oct 2024

Cited by 1 | Viewed by 876

Abstract

Previous studies are mostly conducted based on sparse time series and often ignore the dramatic changes in desertification during the year. Utilizing the Landsat and MODIS data sets from 2000 to 2020, this study applied the spatio-temporal fusion algorithm to obtain the images [...] Read more.

Previous studies are mostly conducted based on sparse time series and often ignore the dramatic changes in desertification during the year. Utilizing the Landsat and MODIS data sets from 2000 to 2020, this study applied the spatio-temporal fusion algorithm to obtain the images of the study area taken at similar times in August over the past 20 years. The optimal desertification remote sensing monitoring index of Gulang County was constructed based on the feature space model, and then the spatial and temporal evolution patterns and the driving mechanism of desertification in Gulang County were revealed by using a geographic detector. The research results were as follows: (1) The ESTARFM algorithm had better applicability in constructing long time series and similar time images with the correlation coefficient R2 = 0.83 between the results of the ESTARFM fusion model and the original image; (2) the SWCI-MSAVI feature space desertification monitoring index model based on point-to-point mode had the best applicability with an overall accuracy of 95.39% and a Kappa coefficient of 0.94; (3) from 2000 to 2020, the desertification showed an increasing trend, and the degree of desertification gradually intensified from south to north in Gulang County; (4) the dominant factors in various historical periods were different, which were mainly composed of precipitation, temperature and population density. The dominant interactive factors changed from alternating dominance of natural factors and human activity factors to the co-dominance of natural factors and human activity factors. The research results could provide decision-making support for precise prevention and control of desertification in arid–semi-arid regions. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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19 pages, 13402 KiB

Open AccessArticle

The Impact of Land Use and Land Cover Changes on Ecosystem Services Value in Laos between 2000 and 2020

by Jun Ma, Jinliang Wang, Jianpeng Zhang, Suling He, Lanfang Liu and Xuzheng Zhong

Land 2024, 13(10), 1568; https://doi.org/10.3390/land13101568 - 27 Sep 2024

Cited by 1 | Viewed by 1739

Abstract

Land use and land cover changes significantly affect the function and value of ecosystem services (ES). Exploring the spatial correspondence between changes in land cover and ES is conducive to optimizing the land use structure and increasing regional coordinated development. Thus, this study [...] Read more.

Land use and land cover changes significantly affect the function and value of ecosystem services (ES). Exploring the spatial correspondence between changes in land cover and ES is conducive to optimizing the land use structure and increasing regional coordinated development. Thus, this study aimed to examine changes in land use and land cover (30 × 30 m) in Laos between 2000 and 2020 and their effects on ecosystem services value (ESV) using the Global Surface Cover Database land use data for 2000 to 2020, ArcGIS technology, and the table of Costanza’s value coefficients. The study results indicated that forest (79.5%), cultivated land (10.6%), and grassland (8.3%) were the dominant land use types in Laos over the past two decades. The forest area decreased significantly, while there were increases in other land types, and the forest was transformed into cultivated land and grassland. ES in Laos was valued at about USD 140–150 billion, with forest contributing the most, followed by cultivated land and grassland. ESV over the last two decades in Laos has increased by USD 3.94 million. Large values were assigned to regulating services (40%) and supporting services (14%). The ESV of food production, soil formation, and water supply increased, and the ESV of climate regulation, genetic resources, and erosion control decreased. In addition, the elasticity value of artificial surfaces was more prominent, with a more evident impact on ESV. For future development, Laos should rationally plan land resources, develop sustainable industries, maintain the dynamic balance of second-category ESV, and achieve sustainable economic and ecological development. This study provides a scientific basis for revealing changes in ESV in Laos over the past two decades, maintaining the stability and sustainable development of the environment in Laos, and realizing the sustainable use and efficient management of the local environmental resources. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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25 pages, 12201 KiB

Open AccessArticle

Spatial Consistency and Accuracy Analysis of Multi-Source Land Cover Products on the Southeastern Tibetan Plateau, China

by Binghua Zhang, Linshan Liu, Yili Zhang, Bo Wei, Dianqing Gong and Lanhui Li

Remote Sens. 2024, 16(17), 3219; https://doi.org/10.3390/rs16173219 - 30 Aug 2024

Cited by 2 | Viewed by 1324

Abstract

Land cover products provide the key inputs for terrestrial change monitoring and modeling. Numerous land cover products have been generated in the past few decades, but their performance on the southeastern Tibetan Plateau remains unclear. This study analyzed 15 land cover products for [...] Read more.

Land cover products provide the key inputs for terrestrial change monitoring and modeling. Numerous land cover products have been generated in the past few decades, but their performance on the southeastern Tibetan Plateau remains unclear. This study analyzed 15 land cover products for consistency through compositional similarity and overlay analyses. Additionally, 1305 validation samples from four datasets were employed to construct confusion matrices to evaluate their accuracy. The results indicate the following: (1) Land cover products exhibit relatively high consistency in 62.92% of the region. (2) Land cover products are strongly influenced by terrain fluctuations, showing lower consistency at elevation below 200 m and instability in land cover classification with increasing elevation, particularly between 2800–4400 m and 4800–5400 m. (3) The accuracy for forest, water, and snow/ice is relatively high. However, there is a relatively lower accuracy for wetland and shrubland, necessitating more field samples for reference to improve classification. (4) The average values of the four validation datasets show that the overall accuracy of the 15 products ranges from 50.97% to 73.50%. For broad-scale studies with lower resolution requirements, the CGLS-LC100 product can be considered. For studies requiring a finer scale, a combination of multiple land cover products should be utilized. ESRI is recommended as a reference for built-up land, while FROM-GLC30 can be used for cropland, although misclassification issues should be noted. This study provides valuable insights for analyzing land cover types on plateaus to refine classification. It also offers guidance for selecting suitable land cover products for future research in this region. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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16 pages, 2075 KiB

Open AccessArticle

A Highland Barley Crop Extraction Method Based on Optimized Feature Combination of Multiple Phenological Sentinel-2 Images

by Xiaogang Wu, Kaiwen Pan, Lin Zhang, Xiulin He, Longhao Wang and Bing Guo

Agriculture 2024, 14(9), 1466; https://doi.org/10.3390/agriculture14091466 - 28 Aug 2024

Viewed by 919

Abstract

Previous studies have primarily focused on the extraction of highland barley crops using single phenological images, which ignored the selection of the optimal phenological period for classification. Utilizing the multiple phenological images from Sentinel-2 to construct 25 features, including spectral, red edge, vegetation, [...] Read more.

Previous studies have primarily focused on the extraction of highland barley crops using single phenological images, which ignored the selection of the optimal phenological period for classification. Utilizing the multiple phenological images from Sentinel-2 to construct 25 features, including spectral, red edge, vegetation, and texture features, the recursive feature elimination algorithm and the random forest algorithm (RF) were employed to optimize feature datasets for different phenological stages, which were then used for the identification and classification of high-land barley by RF. The main results were as follows: (1) Information extraction based on feature optimization combinations yielded good overall classification accuracy, with classification accuracies for highland barley being 92.56% (jointing stage), 90.90% (heading stage), 90.74% (flowering stage), 91.55% (milk ripening stage), and 90.51% (maturity stage), respectively. (2) NDVIre1 had the highest importance score (0.1792) in the feature selection combination, indicating that the red edge index contributed significantly to crop information extraction and classification. (3) The five feature variables—GLCM_Mean, RVI, homogeneity, MAX, and GLCM_Correlation—showed stability and universality in the extraction of highland barley. These results demonstrated that the images that derived from the jointing and milk ripening phenological stages had the best applicability for highland barley extraction, and the optimized feature datasets that composed of NDVIre1 were conductive to detect and monitor of highland barley crops in the mountainous regions of northwest China. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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20 pages, 17564 KiB

Open AccessArticle

Spatiotemporal Dynamics and Evolution of Grain Cropping Patterns in Northeast China: Insights from Remote Sensing and Spatial Overlay Analysis

by Guoming Du, Le Han, Longcheng Yao and Bonoua Faye

Agriculture 2024, 14(9), 1443; https://doi.org/10.3390/agriculture14091443 - 24 Aug 2024

Cited by 3 | Viewed by 1602

Abstract

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract [...] Read more.

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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24 pages, 24165 KiB

Open AccessArticle

Land-Use Conflict Dynamics, Patterns, and Drivers under Rapid Urbanization

by Guojian Wang, Jianguo Wang, Lingzhi Wang, Yi Zhang and Wenxuan Zhang

Land 2024, 13(8), 1317; https://doi.org/10.3390/land13081317 - 20 Aug 2024

Cited by 3 | Viewed by 1995

Abstract

Conflict over land use is an issue that all countries are experiencing in the accelerated process of urbanization and industrialization. Research on the identification and characterization of land-use conflicts is an important basis for promoting the sustainable development of regional land use. Taking [...] Read more.

Conflict over land use is an issue that all countries are experiencing in the accelerated process of urbanization and industrialization. Research on the identification and characterization of land-use conflicts is an important basis for promoting the sustainable development of regional land use. Taking Hebei Province under the background of Beijing–Tianjin–Hebei integration as the research object, this article combines the SCCI model and the LUF model to study the land-use flush in the process of rapid urbanization from the dimensions of land-use landscape conflict and land-use function conflict. The results of this study point out that land-use conflicts in the region have gone through a developmental course of intensification of heavy conflicts, slowing down, and then smoothing out. The exacerbation of land-use conflicts is synchronized with the time pattern of construction and development in the accelerated industrialization and urbanization of Hebei, while the activities of arable land occupation and compensation balance and land ecological management produce lagging land-use conflicts. The spatial pattern is characterized by dispersed and random conflicts in the plains, concentrated conflicts in the mountain stream zones, and stable conflicts in the ecological zones within the mountains in the mountainous areas. The role of externalities and internalities from within the region and in the coordinated development of the region has led to the coexistence of developmental and governance land-use conflicts in Hebei Province, and the geographic environment has a constraining effect on the spatial differentiation of these conflicts. Along with the strong implementation of China’s eco-governance and use-control systems, developmental land-use conflicts from the region will be effectively curbed, but the risk of overlapping developmental conflicts and lagging governance conflicts from coordinated regional development is a key focus for conflict prevention in the future. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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16 pages, 4368 KiB

Open AccessArticle

The Spatio-Temporal Development and Influencing Factors of Urban Residential Land Prices in Hebei Province, China

by Yutong Wang and Jianyu Yang

Land 2024, 13(8), 1234; https://doi.org/10.3390/land13081234 - 8 Aug 2024

Viewed by 1297

Abstract

Against the backdrop of rapid urbanization and coordinated development in the Beijing–Tianjin–Hebei region of China, urban residential land prices in Hebei Province have experienced significant increases, exacerbating housing pressures on residents. This study aims to elucidate the spatio-temporal evolution characteristics of urban residential [...] Read more.

Against the backdrop of rapid urbanization and coordinated development in the Beijing–Tianjin–Hebei region of China, urban residential land prices in Hebei Province have experienced significant increases, exacerbating housing pressures on residents. This study aims to elucidate the spatio-temporal evolution characteristics of urban residential land prices, identify the key influencing factors in Hebei Province of China, and offer insights on macro-control of the land market, optimization of the land supply structure, and guidance on the sustainable development of land and real estate markets in the region. Utilizing land price monitoring data from 11 prefecture-level cities in Hebei Province spanning the past five years, this research employs quantitative methods, such as the Theil index, the standard deviation ellipse, and the geographic detector model, to analyze the spatio-temporal dynamics and factors shaping urban residential land prices. The results show that: (1) Urban residential land prices in Hebei Province exhibited an overall upward trend from 2018 to 2022, characterized by pronounced spatial variations, with higher prices predominantly concentrated in the cities along the Shijiazhuang–Baoding–Langfang–Tangshan corridor; (2) The distribution pattern of urban residential land prices generally mirrors that of GDP, indicating a consistent movement of price centers with urban land price escalation; (3) Urban land prices are influenced by multiple factors in combination, where the interactions among these factors outweigh the impact of any single factor. Specifically, the proportion of GDP attributed to the tertiary industry and location conditions emerge as pivotal factors affecting urban residential land prices in Hebei Province. Given these significant spatial disparities, addressing the industrial structure and optimizing urban land resource allocation are critically imperative. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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21 pages, 4249 KiB

Open AccessArticle

Identification of Potential Land Use Conflicts in Shandong Province: A New Framework

by Guanglong Dong, Zengyu Sun, Wei Li, Keqiang Wang and Chenzhao Yuan

Land 2024, 13(8), 1203; https://doi.org/10.3390/land13081203 - 5 Aug 2024

Cited by 4 | Viewed by 1074

Abstract

Land use conflicts (LUCs) have become a significant global issue. Accurately identifying potential LUCs is crucial for mediating these conflicts, optimizing land use structure, and enhancing land use function. The necessary conditions of LUCs are land use multi-suitability (LUMS), land resource scarcity (LRS), [...] Read more.

Land use conflicts (LUCs) have become a significant global issue. Accurately identifying potential LUCs is crucial for mediating these conflicts, optimizing land use structure, and enhancing land use function. The necessary conditions of LUCs are land use multi-suitability (LUMS), land resource scarcity (LRS), and diversity of demands (DD). However, few studies have approached LUC identification from these three dimensions simultaneously. In addition, when assessing the diversity of demand, only human needs are considered and wildlife needs are ignored. In order to address this gap in the research, this paper constructs a novel framework for LUC identification and proposes an induction-oriented governance path. LUMS was evaluated from three aspects: construction suitability, cultivation suitability, and ecological suitability. LRS is measured from three dimensions: construction land, cultivated land, and ecological land scarcity. The DD is expanded into human and wildlife demand diversity. By analyzing the combination of LUMS, LRS, and DD, LUCs are classified using the potential LUC identification Rubik’s cube model, and corresponding governance paths are suggested. In Shandong Province, potential LUCs are relatively high, with strong, medium, and weak conflicts accounting for 27.39%, 57.10%, and 13.06%, respectively. Potential strong conflicts are mainly distributed in the metropolitan suburbs and in the western plain of Shandong Province. Cultivated land is the main potential land use conflict space. The new framework of LUC identification proposed in this paper can effectively identify potential LUCs. Our research provides scientific reference for sustainable land use. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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21 pages, 30303 KiB

Open AccessArticle

Policy-Driven Vegetation Restoration in Qinghai Province: Spatiotemporal Analysis and Policy Evaluation

by Yuchen Zhang, Jianghong Zhu, Lin Wang, Ke Wang and Jianjun Zhang

Land 2024, 13(7), 1052; https://doi.org/10.3390/land13071052 - 13 Jul 2024

Cited by 2 | Viewed by 1310

Abstract

The Chinese government has implemented numerous ecological policies in Qinghai Province aimed at protecting and restoring the natural ecosystem. Yet, amid global climate change, the precise effects of these policies on ecological improvement remain ambiguous. There is an urgent need for a comprehensive [...] Read more.

The Chinese government has implemented numerous ecological policies in Qinghai Province aimed at protecting and restoring the natural ecosystem. Yet, amid global climate change, the precise effects of these policies on ecological improvement remain ambiguous. There is an urgent need for a comprehensive evaluation of the effects of these policies at a regional scale and an analysis of the changes in policy implementation stages to optimize the strategic direction of regional ecological policies. In this study, using mathematical statistics and spatial analysis, we analysed the spatial and temporal characteristics of the Normalized Difference Vegetation Index (NDVI) in Qinghai Province from 2000 to 2023. Further, by systematically reviewing ten major ecological policies currently or previously implemented in the region, we explored the response of vegetation restoration to these policies through both horizontal and vertical evaluations by reasonably delineating the policy study sub-zones. The study identified distinct stages of policy implementation—regreening, stabilizing, and natural recovery—and correlated these stages with the efficacy of policy impacts. Our findings indicate significant vegetation coverage improvements across Qinghai Province over the past two decades, with all ecological policies positively influencing the environment. The main contribution of this study is that it comprehensively evaluates the impact of multiple ecological policies on vegetation restoration at the regional scale, providing a reference for the formulation and adjustment of subsequent ecological policies. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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19 pages, 35897 KiB

Open AccessArticle

Integrating Future Multi-Scenarios to Evaluate the Effectiveness of Ecological Restoration: A Case Study of the Yellow River Basin

by Xinbei Huang, Chengming Ye, Hongyu Tao, Junjie Zou, Yuzhan Zhou and Shufan Zheng

Land 2024, 13(7), 1032; https://doi.org/10.3390/land13071032 - 10 Jul 2024

Cited by 2 | Viewed by 1829

Abstract

Ecological restoration is an important strategy for mitigating environmental degradation, and the effectiveness evaluation of ecological restoration is of profound significance for the scientific implementation of restoration projects. This study improved the Patch-generating Land Use Simulation (PLUS) model. It was used to simulate [...] Read more.

Ecological restoration is an important strategy for mitigating environmental degradation, and the effectiveness evaluation of ecological restoration is of profound significance for the scientific implementation of restoration projects. This study improved the Patch-generating Land Use Simulation (PLUS) model. It was used to simulate the land use patterns under multi-scenarios such as natural development (ND), economic priority (EP), and ecological restoration (ER) in 2030. An evaluation framework covering ecological “Restoration–Monitoring–Effectiveness” (RME) was proposed. Based on 30 m high-resolution remote-sensing data from 2000 to 2020, the land use distribution, landscape pattern changes, and ecosystem services under different scenarios were evaluated and predicted in the Yellow River Basin of Sichuan to verify the effectiveness of the evaluation framework. The results showed the following: (1) Under the ER scenario, the transfer of land use types in 2020–2030 was mainly characterized by an increase in the area of wetlands and a decrease in the area of built-up land. (2) There were obvious differences in land use and landscape patterns under different scenarios. Compared with the ND and EP scenarios, the growth of the construction rate was suppressed in the ER scenario, and the coverage of grassland and wetlands increased significantly. (3) The mean values of ecosystem services in the ER scenario were higher than those in the ND and EP scenarios. These findings clearly indicate that the RME evaluation system can accurately evaluate the ecological restoration effects under multi-scenarios in the future, providing a new perspective for ecological restoration evaluation in other regions. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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21 pages, 10569 KiB

Open AccessArticle

Interactions and Conflicts between Urbanization and Greenness: A Case Study from Nanjing, China

by Shengjie Yang, Liang Zhong, Yunqiao Zhou, Bin Sun, Rui Wang, Zhengguo Sun and Jianlong Li

Remote Sens. 2024, 16(13), 2505; https://doi.org/10.3390/rs16132505 - 8 Jul 2024

Cited by 2 | Viewed by 1407

Abstract

Urbanization is rapidly occupying green spaces, making it crucial to understand implicit conflicts between urbanization and greenness. This study proposes an ecological greenness index (EGI) and a comprehensive urbanization index (CUI) and selects Nanjing, a megacity in China, as the study area to [...] Read more.

Urbanization is rapidly occupying green spaces, making it crucial to understand implicit conflicts between urbanization and greenness. This study proposes an ecological greenness index (EGI) and a comprehensive urbanization index (CUI) and selects Nanjing, a megacity in China, as the study area to research the spatial and temporal evolutionary trends of the EGI and CUI in the context of land use/land cover (LULC) changes from 2000 to 2020. Meanwhile, the conflicts and complex interaction characteristics of the EGI and CUI are discussed from both static and dynamic perspectives, and their driving mechanisms are investigated by combining specific indicators. The results demonstrate that over the past 20 years, LULC in Nanjing was dominated by cultivated land, forest land, and artificial surfaces. The encroachment of artificial surfaces on green space was strengthened, resulting in a decrease in the proportion of cultivated land from 70.09% in 2000 to 58.00% in 2020. The CUI increased at a change rate of 0.6%/year, while the EGI showed significant browning (change rate: −0.23%/year), mainly concentrated within the main urban boundaries. The relationship between the CUI and EGI made the leap from “primary coordination” to “moderate coordination”, but there remains a risk of further deterioration of the decoupling relationship between the CUI and ecological pressures. The multi-year average contribution of the CUI to the EGI was 49.45%. Urbanization activities that dominate changes in greenness have changed over time, reflecting the timing of urban conflict management. The results provide important insights for urban ecological health monitoring and management. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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17 pages, 2326 KiB

Open AccessArticle

Evaluation of Rice–Crayfish Field Fragmentation Based on Landscape Indices: A Case Study of Qianjiang City, China

by Lei Shi, Xu He, Bo Hu, Jiuwei Li and Lei Yu

Land 2024, 13(7), 1001; https://doi.org/10.3390/land13071001 - 6 Jul 2024

Cited by 2 | Viewed by 1263

Abstract

Since the 21st century, rice–crayfish fields have been widely distributed in the Yangtze River Basin in China. However, the spontaneous construction of these fields by farmers has given rise to the issue of rice–crayfish field fragmentation (RCFF) in certain areas. This study introduced [...] Read more.

Since the 21st century, rice–crayfish fields have been widely distributed in the Yangtze River Basin in China. However, the spontaneous construction of these fields by farmers has given rise to the issue of rice–crayfish field fragmentation (RCFF) in certain areas. This study introduced a novel method for evaluating RCFF using township-level administrative regions as the evaluation units. Three key evaluation elements, including five landscape indices, were employed: area and edge metrics (rice–crayfish area ratio), shape metrics (perimeter–area ratio distribution), and aggregation metrics (rice–crayfish patch density, percentage of like adjacencies, and rice–crayfish contagion index). The RCFF was quantified and its spatial distribution pattern was analyzed through the entropy method and GIS spatial analysis. Empirical studies conducted in Qianjiang city yielded insightful results: (1) The contribution of evaluation elements to the RCFF was ranked in descending order as follows: aggregation metrics > shape metrics > area and edge metrics. (2) The RCFF of Yunlianghu farm was the lowest at 0.06, while the RCFF of Yangshi subdistrict 2 was the highest at 0.94. The spatial distribution of the RCFF exhibited a distinct trend, showing a gradual decrease from the northeast to the southwest in Qianjiang, and a low-RCFF area in the southwest. This evaluation system enables local government decisionmakers to comprehend the current status of rice–crayfish field management and construction. It facilitates the scientific planning of rice–crayfish field layouts and provides guidance for farmers in their expansion strategies. This method can be promoted in counties (cities) where rice–crayfish fields are primarily distributed in the Yangtze River Basin, promoting the transition of traditional agriculture to environmentally friendly agriculture in China. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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22 pages, 9738 KiB

Open AccessArticle

Construction and Optimization of Ecological Security Pattern Network Based on the Supply–Demand Ratio of Ecosystem Services: A Study from Chengdu–Chongqing Economic Circle, China

by Dongjie Guan, Qiongyao Chang, Lilei Zhou, Kangwen Zhu and Guochuan Peng

Land 2024, 13(6), 844; https://doi.org/10.3390/land13060844 - 13 Jun 2024

Cited by 1 | Viewed by 1580

Abstract

The exploration of ecological security patterns (ESPs) can help people find those areas that are in urgent need of restoration, which is an effective way to realize ecological protection. It is of utmost significance for promoting regional sustainable development to construct ESP and [...] Read more.

The exploration of ecological security patterns (ESPs) can help people find those areas that are in urgent need of restoration, which is an effective way to realize ecological protection. It is of utmost significance for promoting regional sustainable development to construct ESP and put forward sub-regional optimization suggestions based on the supply and demand ratio of ecosystem services (ESs). In this paper, we assessed the level of supply and demand for five ESs based on multi-source data in 2020 with the help of InVEST, ArcGIS, and IUEMS. Based on the results of supply and demand, we calculated the supply and demand ratio of ESs and extracted the ecological source areas (ESAs) on this basis. Then, we used the Linkage Mapper tool to construct the ESP based on the principle of the minimum cumulative resistance (MCR) model and circuit theory in the Chengdu–Chongqing economic circle (CCEC). Our results indicated that there were apparent spatial differences in the supply and demand of five ESs. There were 35 ESAs in the ESP network, covering an area of about 7914 km², and most of their land use types were woodland. The CCEC was interconnected by a network of 91 ecological corridors (ECs), spanning a total length of approximately 10,701 km. From the ECs, we extracted 29 ecological pinch points (EPPs) and 16 ecological barrier points (EBPs), which each accounted for about 0.3% of the planned area of the CCEC. Finally, we divided the ecological spaces into four types and put forward the corresponding optimization suggestions. Among them, the proportion of ecological restoration area was 7.7%, which was located in Chengdu City, northwest of the study area. The findings of this paper can give some theoretical guidance and serve as a reference for making decisions in the pursuit of ecological civilization in this region. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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20 pages, 29625 KiB

Open AccessArticle

Spatial and Temporal Heterogeneity of Eco-Environmental Quality in Yanhe Watershed (China) Using the Remote-Sensing-Based Ecological Index (RSEI)

by Lingda Zhang, Quanhua Hou, Yaqiong Duan and Sanbao Ma

Land 2024, 13(6), 780; https://doi.org/10.3390/land13060780 - 31 May 2024

Cited by 6 | Viewed by 1290

Abstract

The long-term impacts of climate change and human activities have resulted in the Yanhe watershed, a typical watershed in the Loess Plateau region, exhibiting a high degree of vulnerability and significant heterogeneity in ecological environmental quality. This has led to environmental degradation and [...] Read more.

The long-term impacts of climate change and human activities have resulted in the Yanhe watershed, a typical watershed in the Loess Plateau region, exhibiting a high degree of vulnerability and significant heterogeneity in ecological environmental quality. This has led to environmental degradation and complex socio-ecological challenges. Consequently, there is an urgent need to carry out research on the spatial and temporal differentiation patterns of ecological environment quality. By utilizing remote sensing data spanning 21 years, this study evaluated the evolutionary trends and consistency of ecological environment quality (EEQ) within the Yanhe watershed based on the remote-sensing-based ecological index (RSEI). Furthermore, it examined global and local spatial autocorrelation of the RSEI by constructing a hexagonal grid, thereby revealing the spatiotemporal characteristics of EEQ at different scales within the Yanhe watershed. The results were as follows: (1) The EEQ has exhibited an overall upward trend in the past two decades, while it has displayed significant fluctuations; (2) the Global Moran’s I values for the years 2000, 2010, and 2020 were 0.18, 0.32, and 0.21, respectively, indicating a presence of spatial autocorrelation within the RSEI; (3) the overall EEQ of the Yanhe watershed will continue to improve, although the ecological quality in certain areas remains unstable due to local natural conditions and human activities. This research not only contributes to the technical framework for analyzing the spatiotemporal heterogeneity of EEQ but also provides actionable insights for ecosystem restoration and sustainability within the Loess Plateau watershed. Our work advances the understanding of ecological dynamics in semi-arid regions and offers a model for assessing ecological quality in similar environmental contexts. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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24 pages, 19755 KiB

Open AccessArticle

Vertical Accuracy Assessment and Improvement of Five High-Resolution Open-Source Digital Elevation Models Using ICESat-2 Data and Random Forest: Case Study on Chongqing, China

by Weifeng Xu, Jun Li, Dailiang Peng, Hongyue Yin, Jinge Jiang, Hongxuan Xia and Di Wen

Remote Sens. 2024, 16(11), 1903; https://doi.org/10.3390/rs16111903 - 25 May 2024

Cited by 5 | Viewed by 2627

Abstract

Digital elevation models (DEMs) are widely used in digital terrain analysis, global change research, digital Earth applications, and studies concerning natural disasters. In this investigation, a thorough examination and comparison of five open-source DEMs (ALOS PALSAR, SRTM1 DEM, SRTM3 DEM, NASADEM, and ASTER [...] Read more.

Digital elevation models (DEMs) are widely used in digital terrain analysis, global change research, digital Earth applications, and studies concerning natural disasters. In this investigation, a thorough examination and comparison of five open-source DEMs (ALOS PALSAR, SRTM1 DEM, SRTM3 DEM, NASADEM, and ASTER GDEM V3) was carried out, with a focus on the Chongqing region as a specific case study. By utilizing ICESat-2 ATL08 data for validation and employing a random forest model to refine terrain variables such as slope, aspect, land cover, and landform type, a study was undertaken to assess the precision of DEM data. Research indicates that spatial resolution significantly impacts the accuracy of DEMs. ALOS PALSAR demonstrated satisfactory performance, reducing the corrected root mean square error (RMSE) from 13.29 m to 9.15 m. The implementation of the random forest model resulted in a significant improvement in the accuracy of the 30 m resolution NASADEM product. This improvement was supported by a decrease in the RMSE from 38.24 m to 9.77 m, demonstrating a significant 74.45% enhancement in accuracy. Consequently, the ALOS PALSAR and NASADEM datasets are considered the preferred data sources for mountainous urban areas. Furthermore, the study established a clear relationship between the precision of DEMs and slope, demonstrating a consistent decline in precision as slope steepness increases. The influence of aspect on accuracy was considered to be relatively minor, while vegetated areas and medium-to-high-relief mountainous terrains were identified as the main challenges in attaining accuracy in the DEMs. This study offers valuable insights into selecting DEM datasets for complex terrains in mountainous urban areas, highlighting the critical importance of choosing the appropriate DEM data for scientific research. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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20 pages, 31427 KiB

Open AccessArticle

Assessment and Simulation of Urban Ecological Environment Quality Based on Geographic Information System Ecological Index

by Lusheng Che, Shuyan Yin, Junfang Jin and Weijian Wu

Land 2024, 13(5), 687; https://doi.org/10.3390/land13050687 - 14 May 2024

Cited by 3 | Viewed by 1419

Abstract

The urban ecological environment is crucial to the quality of life of residents and the sustainable development of the region, and the assessment and prediction of the ecological environment quality can provide a scientific guidance for ecological environment management and improvement. We proposed [...] Read more.

The urban ecological environment is crucial to the quality of life of residents and the sustainable development of the region, and the assessment and prediction of the ecological environment quality can provide a scientific guidance for ecological environment management and improvement. We proposed a novel approach to assess and simulate the urban ecological environment quality using the Geographic Information System Ecological Index (GISEI). First, we calculated the remote sensing ecological index (RSEI) for Xi’an in 2020. Second, we selected land use data, mean annual temperature, and mean annual relative humidity as ecological indicators. We regressed these indicators on the RSEI to obtain the GISEI of Xi’an in 2020. Finally, we simulated the GISEI of Xi’an in 2030 by predicting the ecological indicators and analyzed the changes in the ecological environment quality. The results of the study show that the ecological environment quality in Xi’an in 2020 is better overall. By 2030, most of the ecological environment quality in Xi’an will be worse, and the proportion of the excellent area will decrease from 42.8% to 3.8%. The more serious ecological degradation is mainly located in the regions bordering the Qinling Mountains and the Guanzhong Plain, and the ecological environment quality in most areas of the Qinling Mountains will deteriorate from excellent to good. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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27 pages, 9009 KiB

Open AccessArticle

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

by Gareth Rees, Liliia Hebryn-Baidy and Vadym Belenok

Remote Sens. 2024, 16(9), 1637; https://doi.org/10.3390/rs16091637 - 3 May 2024

Cited by 12 | Viewed by 3753

Abstract

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat [...] Read more.

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (T_air), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside T_air climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in T_air and LST during the summer months from 1984 to 2023, with a notable positive correlation between T_air and LST across both datasets. MODIS data exhibit a stronger correlation (R² = 0.879) compared to Landsat (R² = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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21 pages, 20346 KiB

Open AccessArticle

Multi-Scenario Simulating the Impacts of Land Use Changes on Ecosystem Health in Urban Agglomerations on the Northern Slope of the Tianshan Mountain, China

by Ziyi Hua, Jing Ma, Yan Sun, Yongjun Yang, Xinhua Zhu and Fu Chen

Land 2024, 13(5), 571; https://doi.org/10.3390/land13050571 - 25 Apr 2024

Cited by 5 | Viewed by 1342

Abstract

It is of great significance for scientific land use planning and ecological security protection to clarify the impacts of land use changes on an ecosystem’s health. Based on the dynamic evolution of land use and ecosystem health on the Northern Slope of Tianshan [...] Read more.

It is of great significance for scientific land use planning and ecological security protection to clarify the impacts of land use changes on an ecosystem’s health. Based on the dynamic evolution of land use and ecosystem health on the Northern Slope of Tianshan Mountain (NSTM) from 2000 to 2020, this study utilized the patch-generating land use simulation (PLUS) model, the Vitality–Organization–Resilience–Services (VORS) model, and the elasticity approach to assess the impacts of land use changes on ecosystem health under four different scenarios: Natural Development Scenario (ND), Farmland Conservation Priority Scenario (FP), Ecological Conservation Priority Scenario (EP), and Urban Development Priority Scenario (UD). The results indicate that (1) land use on the NSTM from 2000 to 2020 was predominantly characterized by barren land and grassland. (2) The overall level of ecosystem health on the NSTM was poor from 2000 to 2020 but showed a gradual improvement trend. (3) Ecosystem health levels vary greatly across scenarios. In general, ecosystem health improves under FP and EP scenarios but deteriorates significantly under ND and UD scenarios. The resilience of ecosystem health varies significantly across different land categories. In the future, optimizing the current land use pattern and refining the ecological protection policy are essential to enhance ecosystem health and services in the NSTM. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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19 pages, 6820 KiB

Open AccessArticle

Spatio-Temporal Variation and Future Sustainability of Net Primary Productivity from 2001 to 2021 in Hetao Irrigation District, Inner Mongolia

by Manman Peng, Chaoqun Li, Peng Wang and Xincong Dai

Agriculture 2024, 14(4), 613; https://doi.org/10.3390/agriculture14040613 - 15 Apr 2024

Cited by 4 | Viewed by 1925

Abstract

The Hetao Irrigation District in Inner Mongolia, a vital grain-producing region in northern China, faces growing environmental challenges. Studying net primary productivity (NPP) is essential for understanding spatiotemporal vegetation shifts and guiding locally adapted restoration and management efforts. Utilizing MOD17A3/NPP data, this study [...] Read more.

The Hetao Irrigation District in Inner Mongolia, a vital grain-producing region in northern China, faces growing environmental challenges. Studying net primary productivity (NPP) is essential for understanding spatiotemporal vegetation shifts and guiding locally adapted restoration and management efforts. Utilizing MOD17A3/NPP data, this study applies the Theil–Sen median trend, Mann–Kendall significance, and the Hurst index to scrutinize the spatiotemporal distribution patterns of NPP from 2001 to 2021 and forecast future changes in the area. The findings reveal cyclic temporal trends, forming a “∧” shape with initial increases followed by decreases, notably during the July to August period each year. The multi-year average NPP exhibits a slight upward fluctuation trend, averaging 172.40 gCm⁻²a⁻¹. Peaks occur approximately every three years, reaching the highest average in 2012 at 218.96 gCm⁻²a⁻¹. Spatially, NPP distribution stays consistent over the years, influenced by various land cover types, especially cropland, shaping the spatial patterns. Monthly and yearly NPP trends over the 21 years indicate a significant decrease in May and June, with other months mostly showing a non-significant increase. The Hurst index for monthly and yearly NPP changes over 21 years shows relatively high weak anti-persistence. In summary, over the past 21 years, the NPP trend in the study area has not significantly improved and is expected to decline in the future. This study offers data support and a scientific foundation for refining the carbon cycle model, quantifying vegetation carbon sequestration capacity, addressing climate change policies, and striving for carbon peak and neutrality in the Hetao Irrigation District. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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22 pages, 16634 KiB

Open AccessArticle

Impacts of Crop Type and Climate Changes on Agricultural Water Dynamics in Northeast China from 2000 to 2020

by Xingyuan Xiao, Jing Zhang and Yaqun Liu

Remote Sens. 2024, 16(6), 1007; https://doi.org/10.3390/rs16061007 - 13 Mar 2024

Cited by 6 | Viewed by 2362

Abstract

Northeast China (NEC) is one of the most important national agricultural production bases, and its agricultural water dynamics are essential for food security and sustainable agricultural development. However, the dynamics of long-term annual crop-specific agricultural water and its crop type and climate impacts [...] Read more.

Northeast China (NEC) is one of the most important national agricultural production bases, and its agricultural water dynamics are essential for food security and sustainable agricultural development. However, the dynamics of long-term annual crop-specific agricultural water and its crop type and climate impacts remain largely unknown, compromising water-saving practices and water-efficiency agricultural management in this vital area. Thus, this study used multi-source data of the crop type, climate factors, and the digital elevation model (DEM), and multiple digital agriculture technologies of remote sensing (RS), the geographic information system (GIS), the Soil Conservation Service of the United States Department of Agriculture (USDA-SCS) model, the Food and Agriculture Organization of the United Nations Penman–Monteith (FAO P-M) model, and the water supply–demand index (M) to map the annual spatiotemporal distribution of effective precipitation (Pe), crop water requirement (ET_c), irrigation water requirement (IWR), and the supply–demand situation in the NEC from 2000 to 2020. The study further analyzed the impacts of the crop type and climate changes on agricultural water dynamics and revealed the reasons and policy implications for their spatiotemporal heterogeneity. The results indicated that the annual average Pe, ET_c, IWR, and M increased by 1.56%/a, 0.74%/a, 0.42%/a, and 0.83%/a in the NEC, respectively. Crop-specifically, the annual average Pe increased by 1.15%/a, 2.04%/a, and 2.09%/a, ET_c decreased by 0.46%/a, 0.79%/a, and 0.89%/a, IWR decreased by 1.03%/a, 1.32%/a, and 3.42%/a, and M increased by 1.48%/a, 2.67%/a, and 2.87%/a for maize, rice, and soybean, respectively. Although the ET_c and IWR for all crops decreased, regional averages still increased due to the expansion of water-intensive maize and rice. The crop type and climate changes jointly influenced agricultural water dynamics. Crop type transfer contributed 39.28% and 41.25% of the total IWR increase, and the remaining 60.72% and 58.75% were caused by cropland expansion in the NEC from 2000 to 2010 and 2010 to 2020, respectively. ET_c and IWR increased with increasing temperature and solar radiation, and increasing precipitation led to decreasing IWR in the NEC. The adjustment of crop planting structure and the implementation of water-saving practices need to comprehensively consider the spatiotemporally heterogeneous impacts of crop and climate changes on agricultural water dynamics. The findings of this study can aid RS-GIS-based agricultural water simulations and applications and support the scientific basis for agricultural water management and sustainable agricultural development. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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18 pages, 7905 KiB

Open AccessArticle

Urban Green Connectivity Assessment: A Comparative Study of Datasets in European Cities

by Cristiana Aleixo, Cristina Branquinho, Lauri Laanisto, Piotr Tryjanowski, Ülo Niinemets, Marco Moretti, Roeland Samson and Pedro Pinho

Remote Sens. 2024, 16(5), 771; https://doi.org/10.3390/rs16050771 - 22 Feb 2024

Cited by 2 | Viewed by 3303

Abstract

Urban biodiversity and ecosystem services depend on the quality, quantity, and connectivity of urban green areas (UGAs), which are crucial for enhancing urban livability and resilience. However, assessing these connectivity metrics in urban landscapes often suffers from outdated land cover classifications and insufficient [...] Read more.

Urban biodiversity and ecosystem services depend on the quality, quantity, and connectivity of urban green areas (UGAs), which are crucial for enhancing urban livability and resilience. However, assessing these connectivity metrics in urban landscapes often suffers from outdated land cover classifications and insufficient spatial resolution. Spectral data from Earth Observation, though promising, remains underutilized in analyzing UGAs’ connectivity. This study tests the impact of dataset choices on UGAs’ connectivity assessment, comparing land cover classification (Urban Atlas) and spectral data (Normalized Difference Vegetation Index, NDVI). Conducted in seven European cities, the analysis included 219 UGAs of varying sizes and connectivity levels, using three connectivity metrics (size, proximity index, and surrounding green area) at different spatial scales. The results showed substantial disparities in connectivity metrics, especially at finer scales and shorter distances. These differences are more pronounced in cities with contiguous UGAs, where Urban Atlas faces challenges related to typology issues and minimum mapping units. Overall, spectral data provides a more comprehensive and standardized evaluation of UGAs’ connectivity, reducing reliance on local typology classifications. Consequently, we advocate for integrating spectral data into UGAs’ connectivity analysis to advance urban biodiversity and ecosystem services research. This integration offers a comprehensive and standardized framework for guiding urban planning and management practices. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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18 pages, 4693 KiB

Open AccessArticle

Evaluation of Cropland Utilization Eco-Efficiency and Influencing Factors in Primary Grain-Producing Regions of China

by Jie Li, Zhengchuan Sun, Qin Gao and Yanbin Qi

Agriculture 2024, 14(2), 255; https://doi.org/10.3390/agriculture14020255 - 5 Feb 2024

Cited by 3 | Viewed by 1390

Abstract

Under the backdrop of the “double-carbon” target, the primary grain-producing regions in China are confronted with the tasks of mitigating pollution and carbon emissions and ensuring food security. This paper explores the eco-efficiency of cropland utilization and the factors influencing the primary grain-producing [...] Read more.

Under the backdrop of the “double-carbon” target, the primary grain-producing regions in China are confronted with the tasks of mitigating pollution and carbon emissions and ensuring food security. This paper explores the eco-efficiency of cropland utilization and the factors influencing the primary grain-producing regions in China, utilizing panel data from 13 provinces spanning the period from 2000 to 2019. The analysis employs three models: the super-efficiency SBM model, the Malmquist index model, and the random-effect panel Tobit model. The findings suggest the following: (1) Although the eco-efficiency of cropland utilization in China’s primary grain-producing regions did not reach the production frontier during the period of 2000–2019, it exhibited a high level with an overall upward trend. The limiting factor inhibiting the growth of total factor productivity is lower technical efficiency. (2) There is evident spatial variation in the eco-efficiency of cropland utilization across China, displaying a dynamic evolution from northeast > western > central > eastern to northeast > western > eastern > central. Total factor productivity in each province demonstrates an upward trend, with the east > northeast > west > central ranking. (3) Regarding the influencing factors, the utilization of agricultural production chemicals exerts a negative influence, while the proportion of government financial input, labor input, and irrigation index have a positive impact. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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19 pages, 4540 KiB

Open AccessArticle

Decline in Planting Areas of Double-Season Rice by Half in Southern China over the Last Two Decades

by Wenchao Zhu, Xinqin Peng, Mingjun Ding, Lanhui Li, Yaqun Liu, Wei Liu, Mengdie Yang, Xinxin Chen, Jiale Cai, Hanbing Huang, Yinghan Dong and Jiaye Lu

Remote Sens. 2024, 16(3), 440; https://doi.org/10.3390/rs16030440 - 23 Jan 2024

Cited by 5 | Viewed by 1968

Abstract

Accurately tracking the changes in rice cropping intensity is a critical requirement for policymakers to formulate reasonable land-use policies. Southern China is a traditional region for rice multi-cropping, yet less is known about its spatial–temporal changes under the background of rapid urbanization in [...] Read more.

Accurately tracking the changes in rice cropping intensity is a critical requirement for policymakers to formulate reasonable land-use policies. Southern China is a traditional region for rice multi-cropping, yet less is known about its spatial–temporal changes under the background of rapid urbanization in recent decades. Based on images from Landsat and MODIS and multiple land cover products, the gap-filling and Savitzky–Golay filter method (GF-SG), the enhanced pixel-based phenological features composite approach (Eppf-CM), random forest (RF), and the difference in NDVI approach (DNDVI) were combined to map the rice cropping pattern with a spatial resolution of 30 × 30 m over Southern China in 2000 and 2020 through Google Earth Engine (GEE). Subsequently, the spatial–temporal changes in rice cropping intensity and their driving factors were examined by Getis-Ord Gi* and geographical detector. The results showed that the produced rice cropping pattern maps exhibited high accuracy, with kappa coefficients and overall accuracies exceeding 0.81 and 90%, respectively. Over the past two decades, the planting areas of double-season rice in Southern China decreased by 54.49%, and a reduction was observed across eight provinces, while only half of the provinces exhibited an increase in the planting areas of single-season rice. Compared to the year 2000, the planting area of the conversion from double- to single-season rice cropping systems in 2020 was 2.71 times larger than that of the conversion from single- to double-season rice cropping systems. The hotspots of the change in rice cropping intensity were mainly located in the central part of Southern China (excluding the Poyang Lake Plain). The decline in the rural labor force, coupled with ≥10 °C accumulated temperature and topographical factors, plays a crucial role in the decreased intensity of rice cropping. Our findings can be beneficial for realizing regional agricultural sustainability and food security. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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30 pages, 16101 KiB

Open AccessArticle

Urban Functional Zone Classification Using Light-Detection-and-Ranging Point Clouds, Aerial Images, and Point-of-Interest Data

by You Mo, Zhaocheng Guo, Ruofei Zhong, Wen Song and Shisong Cao

Remote Sens. 2024, 16(2), 386; https://doi.org/10.3390/rs16020386 - 18 Jan 2024

Cited by 8 | Viewed by 2187

Abstract

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it [...] Read more.

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) > 3D UMPs (32.51%) > POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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23 pages, 20362 KiB

Open AccessArticle

Land-Use Optimization Based on Ecological Security Pattern—A Case Study of Baicheng, Northeast China

by Bin Peng, Jiuchun Yang, Yixue Li and Shuwen Zhang

Remote Sens. 2023, 15(24), 5671; https://doi.org/10.3390/rs15245671 - 8 Dec 2023

Cited by 10 | Viewed by 2268

Abstract

In the current context of global urbanization and climate change, balancing ecological protection and economic development is a particular challenge in the optimal allocation of regional land use. Here, we propose a research framework for the optimal allocation of land use that considers [...] Read more.

In the current context of global urbanization and climate change, balancing ecological protection and economic development is a particular challenge in the optimal allocation of regional land use. Here, we propose a research framework for the optimal allocation of land use that considers the regional ecological security pattern (ESP) and allocates space for land-use activities to areas with low ecological risk. Taking Baicheng, China as our study area, ecological sources were first identified by integrating their ecological importance and landscape connectivity, and ecological corridors and functional zones were extracted using the minimum cumulative resistance difference and circuit theory. The ecological source areas were then taken as limiting factors, and four future scenarios were established for 2030 using the parcel-level land-use simulator (PLUS) model. The ecological corridors and functional zones served as areas having restricted ecological conditions, and the four future scenarios were coupled into the corresponding functional zones to optimize the land-use structure in 2030. The results indicate that under the coupled ESP–PLUS scenario, the spatial distribution and structure of land use in Baicheng balance the needs of ecological source area protection and economic development, resulting in greater sustainability. By 2030, the cultivated land area will steadily increase, but attention will also be given to the protection of ecological land (e.g., woodland and marshland), aligning with current policy planning demands. An analysis of the landscape indices for each future scenario found all scenarios to be effective in reducing negative changes in landscape patterns. These findings provide a novel perspective for the rational allocation of future land resources and the optimization of land-use structures. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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21 pages, 3267 KiB

Open AccessArticle

How to Simulate Carbon Sequestration Potential of Forest Vegetation? A Forest Carbon Sequestration Model across a Typical Mountain City in China

by Dongjie Guan, Jialong Nie, Lilei Zhou, Qiongyao Chang and Jiameng Cao

Remote Sens. 2023, 15(21), 5096; https://doi.org/10.3390/rs15215096 - 24 Oct 2023

Cited by 8 | Viewed by 2735

Abstract

Due to a series of human activities like deforestation and land degradation, the concentration of greenhouse gases has risen significantly. Forest vegetation is an important part of forest ecosystems with high carbon sequestration potential. Estimates of the carbon sequestration rate of forest vegetation [...] Read more.

Due to a series of human activities like deforestation and land degradation, the concentration of greenhouse gases has risen significantly. Forest vegetation is an important part of forest ecosystems with high carbon sequestration potential. Estimates of the carbon sequestration rate of forest vegetation in various provinces and districts are helpful to the regional and global Carbon cycle. How to build an effective carbon sequestration potential model and reveal the spatiotemporal evolution trend and driving factors of carbon sequestration potential is an urgent challenge to be solved in carbon cycle simulation and prediction research. This study characterized the carbon sequestration status of forest vegetation using the modified CASA (Carnegie-Ames Stanford Approach) model and estimated the carbon sequestration potential from 2010 to 2060 using the FCS (Forest Carbon Sequestration) model combined with forest age and biomass under the four future Shared Socioeconomic Pathways (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, then proposes natural, social, and economic perspectives. This study found that the average NPP of the forest vegetation in Chongqing from 2000 to 2020 was 797.95 g C/m², and the carbon storage by 2060 was 269.94 Tg C. The carbon sequestration rate varied between <0.01 Tg C/a and 0.20 Tg C/a in various districts and counties. Over time, forest growth gradually slowed, and carbon sequestration rates also decreased. Under the four future climate scenarios, the SSP5-8.5 pathway had the highest carbon sequestration rate. Natural factors had the greatest influence on changes in carbon sequestration rate. This result provides data support and scientific reference for the planning and control of forests and the enhancement of carbon sequestration capacity in Chongqing. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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19 pages, 8865 KiB

Open AccessArticle

Analysis of Spatial Relationship Based on Ecosystem Services and Ecological Risk Index in the Counties of Chongqing

by Zihui Li, Kangwen Zhu, Dan Song, Dongjie Guan, Jiameng Cao, Xiangyuan Su, Yanjun Zhang, Ya Zhang, Yong Ba and Haoyu Wang

Land 2023, 12(10), 1830; https://doi.org/10.3390/land12101830 - 25 Sep 2023

Cited by 2 | Viewed by 1709

Abstract

Due to the insufficient research on the spatial relationship and driving mechanism of ecosystem services and ecological risks and the current background of rising ecological risks and dysfunctional ecosystem services in local areas, analyzing the relationship and driving mechanism is an urgent task [...] Read more.

Due to the insufficient research on the spatial relationship and driving mechanism of ecosystem services and ecological risks and the current background of rising ecological risks and dysfunctional ecosystem services in local areas, analyzing the relationship and driving mechanism is an urgent task in order to safeguard regional ecological security and improve ecosystem services at present. Taking Chongqing as an example, the study scientifically identifies the spatial relationship between ecosystem services and ecological risks and their driving factors at district and county scales based on the constructed Ecosystem Service—Driver–Pressures–Status–Impacts–Responses (ES-DPSIR) model. The main findings include (1) significant variation in the spatial distribution of the comprehensive ecosystem service index, where the lowest ecosystem service index (0.013) was found in the main urban area of Chongqing and the scores gradually increased outward from this center, reaching 0.689 in the outermost areas; (2) an increase in the comprehensive ecological risk index from east to west, ranging from −0.134 to 0.333; (3) a prominent spatial relationship between ecosystem services and ecological risks, with 52.63% of the districts and counties being imbalanced or mildly imbalanced; and (4) significant differences between development trends of ecosystem services-–ecological risks, including 60.53% imbalanced and 30.47% mildly balanced districts. This study identified and analyzed the spatial change characteristics of ecosystem services and ecological risks based on the ES-DPSIR model, explored the driving factors, and provided new ideas for the relationship and driving research. The results of the study could provide effective ways and references for improving regional ecological security and enhancing the capacity of ecosystem services. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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20 pages, 7324 KiB

Open AccessArticle

Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images

by Yangpeng Zhu, Qianyu Li, Zhiyong Lv and Nicola Falco

Remote Sens. 2023, 15(18), 4609; https://doi.org/10.3390/rs15184609 - 19 Sep 2023

Cited by 8 | Viewed by 2098

Abstract

Change detection with heterogeneous remote sensing images (Hete-CD) plays a significant role in practical applications, particularly in cases where homogenous remote sensing images are unavailable. However, directly comparing bitemporal heterogeneous remote sensing images (HRSIs) to measure the change magnitude is unfeasible. Numerous deep [...] Read more.

Change detection with heterogeneous remote sensing images (Hete-CD) plays a significant role in practical applications, particularly in cases where homogenous remote sensing images are unavailable. However, directly comparing bitemporal heterogeneous remote sensing images (HRSIs) to measure the change magnitude is unfeasible. Numerous deep learning methods require substantial samples to train the module adequately. Moreover, the process of labeling a large number of samples for land cover change detection using HRSIs is time-consuming and labor-intensive. Consequently, deep learning networks face challenges in achieving satisfactory performance in Hete-CD due to the limited number of training samples. This study proposes a novel deep-learning framework for Hete-CD to achieve satisfactory performance even with a limited number of initial samples. We developed a multiscale network with a selected kernel-attention module. This design allows us to effectively capture different change targets characterized by diverse sizes and shapes. In addition, a simple yet effective non-parameter sample-enhanced algorithm that utilizes the Pearson correlation coefficient is proposed to explore the potential samples surrounding every initial sample. The proposed network and sample-enhanced algorithm are integrated into an iterative framework to improve change detection performance with a limited number of small samples. The experimental results were achieved based on four pairs of real HRSIs, which were acquired with Landsat-5, Radarsat-2, and Sentinel-2 satellites with optical and SAR sensors. Results indicated that the proposed framework could achieve competitive accuracy with a small number of samples compared with some state-of-the-art methods, including three traditional methods and nine state-of-the-art deep learning methods. For example, the improvement rates are approximately 3.38% and 1.99% compared with the selected traditional methods and deep learning methods, respectively. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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19 pages, 6381 KiB

Open AccessArticle

Driving Mechanisms of Spatiotemporal Heterogeneity of Land Use Conflicts and Simulation under Multiple Scenarios in Dongting Lake Area

by Xuexian An, Meng Zhang and Zhuo Zang

Remote Sens. 2023, 15(18), 4524; https://doi.org/10.3390/rs15184524 - 14 Sep 2023

Cited by 5 | Viewed by 1535

Abstract

As an important ecological hinterland in Hunan Province, the Dongting Lake area has an irreplaceable role in regional socioeconomic development. However, owing to rapid environmental changes and complex land use relationships, land use/land cover (LULC) changes are actively occurring in the region. Therefore, [...] Read more.

As an important ecological hinterland in Hunan Province, the Dongting Lake area has an irreplaceable role in regional socioeconomic development. However, owing to rapid environmental changes and complex land use relationships, land use/land cover (LULC) changes are actively occurring in the region. Therefore, assessment of the current LULC status and the future development trend for sustainable economic development is of considerable importance. In this study, the driving mechanisms of spatiotemporal evolution for land use conflicts (LUCF) in Dongting Lake from 2000 to 2020 were analyzed by constructing a LUCF model. Additionally, a new model, EnKF-PLUS, which couples ensemble Kalman filtering (EnKF) with patch-generating land use simulation (PLUS), was developed to predict the LULC changes and LUCF in 2030 under different scenarios. The results provide three insights. First, during the period of 2000–2020, high LUCF values were concentrated in highly urbanized and densely populated areas, whereas low LUCF values were centered in hilly regions. Secondly, the impacts of static factors (topographical factors) and dynamic factors (population, GDP, and climate factors) on changes in LUCF were regionally differentiated. Thirdly, our results indicate that the implementation of land use strategies of cropland conservation and ecological conservation can effectively mitigate the degree of LUCF changes in the region and contribute to the promotion of the rational allocation of land resources. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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17 pages, 2813 KiB

Open AccessArticle

What Should Be Learned from the Dynamic Evolution of Cropping Patterns in the Black Soil Region of Northeast China? A Case Study of Wangkui County, Heilongjiang Province

by Guoming Du, Longcheng Yao, Le Han and Faye Bonoua

Land 2023, 12(8), 1574; https://doi.org/10.3390/land12081574 - 9 Aug 2023

Cited by 5 | Viewed by 1757

Abstract

Conventional and scientific cropping patterns are important in realizing the sustainable utilization of Black soil and promoting the high-quality development of agriculture. It also has far-reaching significance for protecting Black soil and constructing the crop rotation system to identify the cropping patterns in [...] Read more.

Conventional and scientific cropping patterns are important in realizing the sustainable utilization of Black soil and promoting the high-quality development of agriculture. It also has far-reaching significance for protecting Black soil and constructing the crop rotation system to identify the cropping patterns in Northeast China and analyze their spatio-temporal dynamic change. Using the geo-information Tupu methods and transfer land matrix, this study identified the cropping patterns and their spatio-temporal change based on remote sensing data for three periods, namely 2002–2005, 2010–2013, and 2018–2021. The main results revealed that the maize continuous, mixed cropping, maize-soybean rotation, and soybean continuous cropping patterns were the main cropping patterns in Wangkui County, with the total area of the four patterns accounting for 95.28%, 94.66%, and 81.69%, respectively, in the three periods. Against the backdrop of global climate warming, the cropping patterns of continuous maize and soybean and the mixed cropping pattern in Wangkui County exhibited a trend towards evolving into a maize-soybean rotation in the northern region. Moreover, the maize-soybean rotation further evolved into a mixed cropping system of maize and soybean in the north. Furthermore, the spatio-temporal evolution of cropping patterns was significantly driven by natural and social factors. Specifically, natural factors influenced the spatio-temporal patterns of variation in cropping patterns, while social factors contributed to the transformation of farmers’ cropping decision-making behavior. Accordingly, new insights, institutional policies, and solid solutions, such as exploring and understanding farmers’ behavior regarding crop rotation practices and mitigating the natural and climatic factors for improving food security, are urgent in the black soil region of China. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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13 pages, 5027 KiB

Open AccessArticle

Spatial Distributions of Yield Gaps and Production Increase Potentials of Spring Wheat and Highland Barley in the Qinghai-Tibet Plateau

by Zemin Zhang, Changhe Lu and Xiao Guan

Land 2023, 12(8), 1555; https://doi.org/10.3390/land12081555 - 5 Aug 2023

Cited by 1 | Viewed by 1627

Abstract

Low grain yield caused by high altitude; cold climate; small, cultivated land area, and poor soil fertility is the critical factor posing a potential risk to local food security in the Qinghai-Tibet Plateau (QTP). Analyzing spatial distribution of the increase potential of grain [...] Read more.

Low grain yield caused by high altitude; cold climate; small, cultivated land area, and poor soil fertility is the critical factor posing a potential risk to local food security in the Qinghai-Tibet Plateau (QTP). Analyzing spatial distribution of the increase potential of grain production in the QTP could be contributable to developing a regional increase in the space of grains to ensure food security. Taking spring wheat and highland barley as objectives, this study simulated the annual potential yields of spring wheat and highland barley at the site level. They estimated their yield gaps and production increase potentials at the regional and county level and mapped their spatial distribution in 2020, based on the methodologies of the literature data collection, using the WOFOST model and GIS analysis. The yield gaps of spring wheat and highland barley were 3.7 and 2.4 t ha⁻¹ for the whole QTP, accounting for 51.4% and 39.5% of their potential yields, respectively. At the county level, the yield gap ranges of spring wheat and highland barley were 1.5–7.0 t ha⁻¹ and 0.3–5.9 t ha⁻¹ across the QTP, respectively. When the yield gap was fully developed, spring wheat and highland barley productions had the potentials of 497.4 and 717.4 Kt for the whole QTP, equal to 118.2% and 75.2% of their current total production, respectively. Spatially, the counties with a large increase potential of spring wheat were mainly distributed in Haidong, Hainan, Xining, Shannan, Nyingchi, and Lhasa, while those with low potential were located in Xigaze and Shannan. Regarding highland barley, Lhasa, Shannan, Xigaze, Yushu, and Hainan had a larger potential to increase. To increase grain production in the QTP, the priority should be given to the shrinkage of the yield gap in the counties with larger potentials to increase, such as Hainan, Shannan, Lhasa, etc., through improving the irrigation rate and fertilizer usage in the farmland. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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23 pages, 18842 KiB

Open AccessArticle

Local Climate Zone Classification Using Daytime Zhuhai-1 Hyperspectral Imagery and Nighttime Light Data

by Ying Liang, Wen Song, Shisong Cao and Mingyi Du

Remote Sens. 2023, 15(13), 3351; https://doi.org/10.3390/rs15133351 - 30 Jun 2023

Cited by 9 | Viewed by 2450

Abstract

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for [...] Read more.

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city’s physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification’s highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

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