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Remote Sensing for Land Degradation and Drought Monitoring II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3112

Special Issue Editors


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Guest Editor
Department of Geography, University of Bergen, 5020 Bergen, Norway
Interests: remote sensing of land surface dynamics; remote sensing for land degradation and drought monitoring & assessment; remote sensing for agricultural applications; earth observation and geo-information for policy support and international cooperation support (SDGs, Sendai indicators etc.)
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geography, University of Bergen, 5020 Bergen, Norway
Interests: arid landscapes; cartography; cultural landscapes; environmental changes; GIS; plastic pollution; remote sensing; vegetation ecology

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Guest Editor
International Centre of Insect Physiology and Ecology, P.O. Box 30772, Nairobi 00100, Kenya
Interests: land surface/dynamics monitoring; understanding the socio-ecological system; diseases and pest modelling; drought monitoring; land degradation; fire effects; agriculutural applications; VCS/REDD+
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second Special Issue concerning the contributions of Remote Sensing for Land Degradation and Drought Monitoring. (https://www.mdpi.com/journal/remotesensing/special_issues/land_degradation_drought_monitoring).

Land degradation (LD) and droughts are among the most crucial challenges worldwide, affecting people’s livelihoods and the health of socioecological systems. Earth observation has become paramount for monitoring and assessing both phenomena. However, some methodological and conceptual gaps still need to be urgently addressed to advance progress and derive spatially explicit and reliable information that can serve as indicators of LD and droughts.

This upcoming Special Issue on “Remote Sensing for Land Degradation and Drought Monitoring” seeks original research papers focused on monitoring land degradation and droughts in different ecosystems and spatial and temporal scales. Submissions that address the synergistic use of multiple EO-based data streams, multiple indicators, and validation techniques are strongly encouraged. Innovative time series analysis techniques and new machine learning approaches are also of interest, alongside integrative spatial modeling approaches for the monitoring and early warning of both phenomena.

Prof. Dr. Olena Dubovyk
Dr. Gidske Leknæs Andersen
Dr. Tobias Landmann
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. Remote Sensing 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 2700 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

  • land degradation
  • desertification
  • disaster risk reduction and preparedness
  • drought hazard
  • early warning
  • multitemporal analysis
  • time–series analysis

Published Papers (3 papers)

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15 pages, 5099 KiB  
Article
The Latest Desertification Process and Its Driving Force in Alxa League from 2000 to 2020
by Jiali Xie, Zhixiang Lu, Shengchun Xiao and Changzhen Yan
Remote Sens. 2023, 15(19), 4867; https://doi.org/10.3390/rs15194867 - 08 Oct 2023
Cited by 1 | Viewed by 773
Abstract
Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, [...] Read more.
Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, including ETM+ images obtained in 2000, TM images obtained in 2010, and OLI images obtained in 2020, were used to extract three periods of desertification land information using the classification and regression tree (CART) decision tree classification method in Alxa League. The spatio-temporal variation characteristics of desertification land were analyzed by combining the transfer matrix and barycenter migration model; the effects of climate change and human activities on regional desertification evolution were separated and recombined using the multiple regression residual analysis method and by considering the influence of non-zonal factors. The results showed that from 2000 to 2020, the overall area of desertification land in Alxa League was reduced, the desertification degree was alleviated, the desertification trend was reversed, and the desertification degree in the northern part of the region was more serious than in the southern part. The barycenter of the slight, moderate, and severe desertification land migrated to the southeast, whereas the serious desertification land’s barycenter migrated to the northwest in the period of 2000–2010; however, all of them hardly moved from 2010 to 2020. The degree of desertification reversal in the south was more significant than in the north. Regional desertification reversal was mainly influenced by the combination of human activities and climate change, and the area accounted for 61.5%; meanwhile, the localized desertification development was mainly affected by human activities and accounted for 76.8%. Full article
(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)
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16 pages, 7304 KiB  
Article
Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS)
by Xianglong Fan, Xiaoyan Kang, Pan Gao, Ze Zhang, Jin Wang, Qiang Zhang, Mengli Zhang, Lulu Ma, Xin Lv and Lifu Zhang
Remote Sens. 2023, 15(13), 3358; https://doi.org/10.3390/rs15133358 - 30 Jun 2023
Viewed by 931
Abstract
Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from [...] Read more.
Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with Rp2 (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas. Full article
(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)
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17 pages, 11301 KiB  
Technical Note
New Functionalities and Regional/National Use Cases of the Anomaly Hotspots of Agricultural Production (ASAP) Platform
by Felix Rembold, Michele Meroni, Viola Otieno, Oliver Kipkogei, Kenneth Mwangi, João Maria de Sousa Afonso, Isidro Metódio Tuleni Johannes Ihadua, Amílcar Ernesto A. José, Louis Evence Zoungrana, Amjed Hadj Taieb, Ferdinando Urbano, Maria Dimou, Hervé Kerdiles, Petar Vojnovic, Matteo Zampieri and Andrea Toreti
Remote Sens. 2023, 15(17), 4284; https://doi.org/10.3390/rs15174284 - 31 Aug 2023
Cited by 1 | Viewed by 926
Abstract
The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last [...] Read more.
The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria’s 2021 and 2022 winter crop seasons that were affected by drought. Full article
(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)
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