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

Topic Information

Dear Colleagues,

Land use change (LUC) is a cause and result of global changes in the environment. It provides essential food, feed, fuel, and ecosystem services for human social systems, while increasingly affecting the biogeochemical processes of the Earth, such as material exchange, energy balance, the carbon and water cycles, and climate change. In the current “Anthropocene” era, land use has undergone unprecedented changes and intensification to meet the demands of the growing population for goods and services and has had negative impacts in terms of deforestation, land degradation, habitat reduction, biodiversity loss, non-point source pollution, and greenhouse gas emissions. In order to alleviate these negative impacts and improve the efficiency and sustainability of land use, it is essential to optimize its structure, functions, and patterns. To achieve this goal, remote sensing (RS) and geographic information systems (GIS) can provide large-scale, real-time, accurate, and consistent ground information, as well as the high-performance capability to compute, analyze, and display multi-source data. These technologies have been widely used in monitoring LUC and its ecological effects; therefore, it is imperative to integrate RS and GIS to reveal the spatiotemporal processes, driving mechanisms, multi-functions, and optimization patterns of LUC. This will support the scientific basis and practical implications necessary for sustainable land use planning, environmental quality improvements, and coordinated human–earth system developments.

The aim of this Topic is to advance novel theories and methods that contribute new knowledge on various aspects of LUC. Specifically, this Topic seeks to (1) monitor the spatiotemporal patterns and processes of typical LUCs, including cropland reclamation and abandonment, crop type adjustment, rural construction and restructure, urban sprawl and compactness, and ecological land protection; (2) reveal the coupled natural and anthropogenic driving mechanisms of LUC; (3) quantify the various aspects associated with LUC, such as economic benefits, resident livelihoods, food production, agricultural non-point source pollution, ecosystem services, and ecological risks, and analyze their trade-offs and synergies; (4) assess the vulnerability, resilience, and sustainability of different land use patterns in the human–earth system; and (5) simulate and optimize LUC under different development scenarios to create adaptation strategies for future challenges. The original research articles and reviews presented in this Topic will offer scientific methodologies, systematic insights, and policy recommendations for effective land use management and regional sustainable development. By addressing these critical themes of LUC, this Topic will contribute to the advancement of knowledge and provide practical guidance for stakeholders and policymakers seeking to optimize land use for the benefits of society and the environment.

We look forward to receiving your submissions.

Dr. Yaqun Liu
Dr. Wei Song
Prof. Dr. Jieyong Wang
Dr. Kangwen Zhu
Dr. Xuanchang Zhang
Dr. Cong Ou
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.6	3.6	2011	17.7 Days	CHF 2600
Foods foods	5.2	5.8	2012	13.1 Days	CHF 2900
Land land	3.9	3.7	2012	14.8 Days	CHF 2600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700

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Published Papers (16 papers)

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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 - 03 May 2024

Viewed by 600

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

Viewed by 356

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

Viewed by 556

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

Viewed by 785

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

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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 - 05 Feb 2024

Viewed by 691

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 1 | Viewed by 723

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

Viewed by 823

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 - 08 Dec 2023

Viewed by 993

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 1 | Viewed by 1232

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

Viewed by 907

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 2 | Viewed by 1066

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

Viewed by 751

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 - 09 Aug 2023

Cited by 1 | Viewed by 808

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 - 05 Aug 2023

Cited by 1 | Viewed by 808

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 3 | Viewed by 1244

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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