Applications of Remote Sensing and GIS in Land and Soil Resources

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5245

Special Issue Editors

Department of Resource and Environment, Qingdao Agricultural University, Qingdao 266109, China
Interests: analysis of soil hyperspectral characteristics; quantitative model; remote sensing inversion; spatial variation of soil properties; spatial and temporal evolution of soil resources; soil survey; digital soil mapping; soil genesis and classification; land-use change and its ecological environment effect; land evaluation; ecosystem service; soil-quality assessment; environmental risk investigation and evaluation
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
Interests: land use; soil quality evaluation; soil remote sensing
Soil and Water Conservation Department, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430010, China
Interests: soil and water conservation; soil quality and health; soil geochemistry and heavy metal pollution; soil genesis and classification; Karst rocky desertification
School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Interests: digital soil mapping based on machine learning and remote senced data; driving forces of soil organic carbon change at the regional scale; hyperspectral modeling of soil organic matter based on wavelength selection; land use change and scenario simulation; spatio-temporal change of soil nutrient loss and soil erosion

Special Issue Information

Dear Colleagues,

Agriculture is the source of food and clothing, which are the foundations of human survival. It is necessary to master the quantity, quality, spatial distribution and spatiotemporal evolution of agricultural resources. Land and soil resources are important natural resources for human survival and development, and the collaborative application of GIS and remote sensing technology has demonstrated significant advantages in their investigation, monitoring and evaluation.

This Special Issue will focus on the latest advances in spatiotemporal evolution and monitoring of land and soil resources with remote sensing technology and GIS. We are seeking original manuscripts on topics including (but not limited to):

  • Land-use/cover change and simulation;
  • Land-use monitoring;
  • Soil remote sensing;
  • Soil spatial variability;
  • Spatiotemporal evolution of soil resources;
  • Land/soil resources assessment;
  • Dynamic monitoring of soil erosion;
  • Remote-sensing monitoring of agricultural crops;
  • Information extraction of crop with remote sensing;
  • Diagnosis of crop nutrition with remote sensing.

Dr. Xiaoguang Zhang
Prof. Dr. Yanbing Qi
Dr. Zhigang Wang
Dr. Mingsong Zhao
Guest Editors

Manuscript Submission Information

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Keywords

  • spatial variability
  • spatio-temporal evolution
  • soil spectroscopy
  • remote sensing
  • geographic information system (GIS)
  • land use/cover change
  • land-use/cover change and simulation
  • soil erosion monitoring and soil nutrient loss

Published Papers (4 papers)

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Research

24 pages, 32854 KiB  
Article
Soil Erosion in a Changing Environment over 40 Years in the Merguellil Catchment Area of Central Tunisia
by Taoufik Hermassi, Mohamed Lassaad Kotti and Fathia Jarray
Appl. Sci. 2023, 13(21), 11641; https://doi.org/10.3390/app132111641 - 24 Oct 2023
Viewed by 663
Abstract
Soil degradation and erosion in semi-arid regions can significantly impact agricultural development, environmental sustainability, and hydrological balance. Understanding the impacts of land use changes and soil and water conservation (SWC) technique implementation on soil erosion and sediment yield is critical to planning effective [...] Read more.
Soil degradation and erosion in semi-arid regions can significantly impact agricultural development, environmental sustainability, and hydrological balance. Understanding the impacts of land use changes and soil and water conservation (SWC) technique implementation on soil erosion and sediment yield is critical to planning effective watershed management. This study aims to evaluate the impacts of environmental changes in the Merguellil watershed (Central Tunisia) over the last forty years. To achieve this, remote sensing techniques and a geographic information system (GIS) will be employed to classify Landsat images from 1980 to 2020. Additionally, the Revised Universal Soil Loss Equation model will be utilized to estimate soil erosion rates, while the sediment delivery distributed model will be employed for sediment yield modeling. Spatiotemporal changes in land use and land cover and in areas treated with SWC techniques were analyzed as the main factors influencing changes in erosion and sediment yield. The combined impact of land use change and SWC techniques resulted in a decrease in the annual soil erosion rate from 18 to 16 t/ha/year between 1980 and 2020 and in sediment yield from 9.65 to 8.95 t/ha/year for the same period. According to the model’s predictions, both soil erosion and sediment yield will experience a slight increase with further degradation of natural vegetation and a reduction in the efficiency of SWC works. This emphasizes the importance of continued efforts in adopting and sustaining SWC techniques, as well as preserving natural vegetation cover, to proactively combat soil degradation and its adverse effects on the environment and communities. Continuous dedication to these measures is crucial to preserving our ecosystem, promoting sustainable practices, and protecting the well-being of both the environment and society. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)
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19 pages, 27036 KiB  
Article
Spatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China
by Yuanyuan Li, Jingyan Sun, Mingzhu Wang, Jinwei Guo, Xin Wei, Manoj K. Shukla and Yanbing Qi
Appl. Sci. 2023, 13(20), 11532; https://doi.org/10.3390/app132011532 - 21 Oct 2023
Cited by 1 | Viewed by 827
Abstract
Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the [...] Read more.
Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. The results indicated that 73.86% of the area in Shaanxi Province exhibited an increasing FVC with a growth rate of 0.0026 year−1 from 2000 to 2018. The FVC in the three subregions varied, as QBM (87.24–91.47%) > GZP (47.45–66.93%) > LOP (36.33–49.74%), which displayed a significant increase, slight increase, and slight decrease, respectively. The variation of FVC was significantly positively correlated with climate factors (precipitation, temperature, sunshine duration) at monthly and seasonal scales. The time-lag duration between FVC and climate factors was 1–3 months except for the conjunctional areas of GZP with the LOP and QBM, which exhibited a time-lag of 5–6 months. Topographically, the landform of hills had the highest FVC increase at an altitude of 500–1500 m and a slope of 2°–6°. The dominant driving factors affecting FVC variation in Shaanxi Province and LOP area were climatic factors. In the QBM area, the dominant factors were related to topography (relief, elevation, slope), whereas in the GZP area, they were relief and sunshine duration. We can conclude that local topography characteristics are important in implementing revegetation projects because they strongly influence water, temperature, and sunshine redistribution. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)
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17 pages, 5841 KiB  
Article
Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images
by Hongya Zhang, Chi Xu, Zhongjie Fan, Wenzhuo Li, Kaimin Sun and Deren Li
Appl. Sci. 2023, 13(19), 10729; https://doi.org/10.3390/app131910729 - 27 Sep 2023
Viewed by 996
Abstract
Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification [...] Read more.
Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)
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16 pages, 6757 KiB  
Article
Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area
by Banglong Pan, Shutong Cai, Minle Zhao, Hongwei Cheng, Hanming Yu, Shuhua Du, Juan Du and Fazhi Xie
Appl. Sci. 2023, 13(16), 9321; https://doi.org/10.3390/app13169321 - 16 Aug 2023
Cited by 1 | Viewed by 938
Abstract
Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to [...] Read more.
Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)
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