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Special Issue "Applications of Remote Sensing and GIS in Land and Soil Resources"

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

Deadline for manuscript submissions: 31 August 2023 | Viewed by 3270

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Special Issue Editors

Dr. Xiaoguang Zhang

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Guest Editor

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

Prof. Dr. Yanbing Qi

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Guest Editor

College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
Interests: land use; soil quality evaluation; soil remote sensing

Dr. Zhigang Wang

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Guest Editor

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

Dr. Mingsong Zhao

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Guest Editor

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Open AccessArticle

Analyzing Spatial-Temporal Change of Vegetation Ecological Quality and Its Influencing Factors in Anhui Province, Eastern China Using Multiscale Geographically Weighted Regression

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Appl. Sci. 2023, 13(11), 6359; https://doi.org/10.3390/app13116359 - 23 May 2023

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Abstract

Vegetation is a crucial component of terrestrial ecology and plays a significant role in carbon sequestration. Monitoring changes in vegetation ecological quality has important guidance value for sustainable development. In this study, we investigated the spatial and temporal variation characteristics of Ecological Quality Index of Terrestrial Vegetation (EQI) in Anhui Province during the growing season from 2000 to 2020 using trend analysis, partial correlation analysis and bivariate spatial autocorrelation analysis. Based on the Multiscale Geographically Weighted Regression (MGWR), the spatial heterogeneity of the effects of average temperature, precipitation, elevation, slope, and human activity factors on EQI was explored. Our results showed an increasing trend in EQI during the growing season in Anhui Province from 2000 to 2020. The significantly increasing areas accounted for 43.49%, while the significantly decreasing areas accounted for 3.60%. EQI had a mostly positive correlation with precipitation and a negative correlation with average temperature (p < 0.1), showing a higher sensitivity to precipitation than to temperature. Additionally, EQI tended to increase initially and then decrease with increasing elevation and slope. Furthermore, our analysis revealed a significant negative spatial correlation between human activity intensity and EQI (p < 0.01). The bivariate global autocorrelation Moran index between EQI and human activity in 2000, 2005, 2010, 2015, and 2018 were −0.418, −0.427, −0.414, −0.487, and −0.470, respectively. We also found that the influencing factors explain 63–83% of the spatial variation of EQI, and the order of influence of factors on EQI is elevation > human activity > slope > average temperature > precipitation. MGWR results indicated that human activities and topographic factors had a stronger impact on EQI at the local scale, while climate factors tended to influence EQI at the global scale. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)

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Open AccessArticle

Ecological Sensitivity Assessment and Spatial Pattern Analysis of Land Resources in Tumen River Basin, China

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Appl. Sci. 2023, 13(7), 4197; https://doi.org/10.3390/app13074197 - 25 Mar 2023

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Abstract

Ecological sensitivity is one of the important indicators of regional ecological fragility, which can represent the sensitivity of ecosystems to natural environmental conditions and human activity disturbances in the region. In this study, the ecological sensitivity of land resources in the Tumen River Basin of China was quantitatively evaluated by taking 3 ecologically sensitive impact types, including the natural environment, human disturbance, and soil erosion, as evaluation criteria, and 11 ecologically sensitive factors were selected to build an evaluation system using the analytic hierarchy process (AHP) method, to determine the weights of the evaluation factors, combined with geographic information system (GIS) technology. The results show that: (1) Among the three types of ecological sensitivity factors, the influence of human disturbance is the most obvious, and the two factors of land use type and distance from construction land have the highest weights in the comprehensive ecological sensitivity evaluation. (2) There are no extremely sensitive areas or insensitive areas in the Tumen River Basin in China. Highly sensitive areas account for only 0.59% of the total area and are mainly concentrated in the lakes, rivers, and reservoirs in the study area. Moderately sensitive areas account for 54.12%, which are concentrated in the central part of the Tumen River Basin Slightly sensitive areas are mainly located in the mountainous areas in the north and south of the study area. (3) Among the various land resource types, the proportion of slightly sensitive areas and moderately sensitive areas of woodland is close (about 50%), while cultivated land, grassland, construction land, and bare land are mainly moderately sensitive areas (73.95%, 82.07%, 96.59%, and 78.78%), and water bodies are mostly distributed within highly sensitive areas (60.97%), and all wetlands with the smallest area are moderately sensitive. The results of the study can provide data support and a scientific basis for regional ecological protection and development planning. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)

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Open AccessArticle

The Quantified and Major Influencing Factors on Spatial Distribution of Soil Organic Matter in Provincial-Scale Farmland—A Case Study of Shandong Province in Eastern China

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Appl. Sci. 2023, 13(6), 3738; https://doi.org/10.3390/app13063738 - 15 Mar 2023

Viewed by 487

Abstract

Soil organic matter (SOM) is an important component of soil and plays an important role in improving the soil’s physical and chemical properties. Ascertaining the spatial distribution of soil organic matter and its main controlling factors in the context of provincial scale farming is of important guiding significance for soil carbon sequestration, emission reduction and sustainable utilization. Using 257 soil profiles from the second soil survey in Shandong Province, GIS, we applied geostatistical methods to study the spatial distribution characteristics of SOM in topsoil. In addition, correlation and regression analyses were used to explore the main controlling factors over the spatial variation of SOM. The results showed that the mean amount of SOM in Shandong province ranged from 1.20–74.90 g·kg⁻¹, with a coefficient of variation of 73.52%, which is a medium level of variation. The distribution of SOM in the study area was patchy, with higher levels of organic matter in the central, eastern, and northern parts, and lower levels of organic matter in the south-west. The comprehensive explanatory ability of all factors reached 52.30%. Soil type and parent material were the main controlling factors for the spatial variability of SOM in the Shandong Province, followed by soil texture and land use type, with topography and climatic factors having relatively little influence. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)

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Open AccessArticle

Scene Recognition for Construction Projects Based on the Combination Detection of Detailed Ground Objects

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Appl. Sci. 2023, 13(4), 2578; https://doi.org/10.3390/app13042578 - 16 Feb 2023

Viewed by 700

Abstract

The automatic identification of construction projects, which can be considered as complex scenes, is a technical challenge for the supervision of soil and water conservation in urban areas. Construction projects in high-resolution remote sensing images have no unified semantic definition, thereby exhibiting significant differences in image features. This paper proposes an identification method for construction projects based on the detection of detailed ground objects, which construction projects comprise, including movable slab houses, buildings under construction, dust screens, and bare soil (rock). To create the training data set, we select highly informative detailed ground objects from high-resolution remote sensing images. Then, the Faster RCNN (region-based convolutional neural network) algorithm is used to detect construction projects and the highly informative detailed ground objects separately. The merging of detection boxes and the correction of detailed ground object combinations are used to jointly improve the confidence of construction project detection results. The empirical experiments show that the accuracy evaluation indicators of this method on a data set of Wuhan construction projects outperform other comparative methods, and its AP value and F1 score reached 0.773 and 0.417, respectively. The proposed method can achieve satisfactory identification results for construction projects with complex scenes, and can be applied to the comprehensive supervision of soil and water conservation in construction projects. Full article

(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Land and Soil Resources)

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