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Remote Sensing for Land Degradation and Drought Monitoring II

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 12272

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Special Issue Editors

Prof. Dr. Olena Dubovyk

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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

Dr. Gidske Leknæs Andersen

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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

Dr. Tobias Landmann

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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

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Published Papers (7 papers)

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19 pages, 5636 KiB

Open AccessArticle

Machine Learning Enhances Soil Aggregate Stability Mapping for Effective Land Management in a Semi-Arid Region

by Pegah Khosravani, Ali Akbar Moosavi, Majid Baghernejad, Ndiye M. Kebonye, Seyed Roohollah Mousavi and Thomas Scholten

Remote Sens. 2024, 16(22), 4304; https://doi.org/10.3390/rs16224304 - 18 Nov 2024

Cited by 2 | Viewed by 1116

Abstract

Soil aggregate stability (SAS) is needed to evaluate the soil’s resistance to degradation and erosion, especially in semi-arid regions. Traditional laboratory methods for assessing SAS are labor-intensive and costly, limiting timely and cost-effective monitoring. Thus, we developed cost-efficient wall-to-wall spatial prediction maps for two fundamental SAS proxies [mean weight diameter (MWD) and geometric mean diameter (GMD)], across a 5000-hectare area in Southwest Iran. Machine learning algorithms coupled with environmental and soil covariates were used. Our results showed that topographic covariates were the most influential covariates in predicting these SAS proxies. Overall, our SAS maps are valuable tools for sustainable soil and natural resource management, enabling decision-making for addressing potential soil degradation and promoting sustainable land use in semi-arid regions. Full article

(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)

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14 pages, 6582 KiB

Open AccessArticle

Development of a Simple Observation System to Monitor Regional Wind Erosion

by Reiji Kimura, Jiaqi Liu, Ulgiichimg Ganzorig and Masao Moriyama

Remote Sens. 2024, 16(17), 3331; https://doi.org/10.3390/rs16173331 - 8 Sep 2024

Viewed by 1353

Abstract

Dryland occupies about 46% of the global land surface area (except Antarctica) and is the most vulnerable area to climate change. From the conditions of vegetation and land surface wetness and blown sand phenomena, a simple observation system was developed to monitor regional wind erosion and applied to Khuld of Mongolia, which is sensitive to drought and desertification. The system was composed of instruments that observed blown sand, vegetation amount, land surface wetness, and landscape features related to regional wind erosion. Sixteen blown sand and eight sandstorm events were evaluated from 5 March to 5 June 2023 (i.e., during the Asian dust season in northeast Asia). The normalized difference vegetation index and visible images showed that the vegetation amount was considerably less, and the developed moisture index related to land surface wetness indicated dry conditions. Combining the results of blown sand, these indices, and visible images, land surface conditions during the analysis period were likely to occur with blown sand events. Full article

(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)

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21 pages, 10428 KiB

Open AccessArticle

Three Decades of Inundation Dynamics in an Australian Dryland Wetland: An Eco-Hydrological Perspective

by Indishe P. Senanayake, In-Young Yeo and George A. Kuczera

Remote Sens. 2024, 16(17), 3310; https://doi.org/10.3390/rs16173310 - 6 Sep 2024

Cited by 2 | Viewed by 1545

Abstract

Wetland ecosystems are experiencing rapid degradation due to human activities, particularly the diversion of natural flows for various purposes, leading to significant alterations in wetland hydrology and their ecological functions. However, understanding and quantifying these eco-hydrological changes, especially concerning inundation dynamics, presents a formidable challenge due to the lack of long-term, observation-based spatiotemporal inundation information. In this study, we classified wetland areas into ten equal-interval classes based on inundation probability derived from a dense, 30-year time series of Landsat-based inundation maps over an Australian dryland riparian wetland, Macquarie Marshes. These maps were then compared with three simplified vegetation patches in the area: river red gum forest, river red gum woodland, and shrubland. Our findings reveal a higher inundation probability over a small area covered by river red gum forest, exhibiting persistent inundation over time. In contrast, river red gum woodland and shrubland areas show fluctuating inundation patterns. When comparing percentage inundation with the Normalized Difference Vegetation Index (NDVI), we observed a notable agreement in peaks, with a lag time in NDVI response. A strong correlation between NDVI and the percentage of inundated area was found in the river red gum woodland patch. During dry, wet, and intermediate years, the shrubland patch consistently demonstrated similar inundation probabilities, while river red gum patches exhibited variable probabilities. During drying events, the shrubland patch dried faster, likely due to higher evaporation rates driven by exposure to solar radiation. However, long-term inundation probability exhibited agreement with the SAGA wetness index, highlighting the influence of topography on inundation probability. These findings provide crucial insights into the complex interactions between hydrological processes and vegetation dynamics in wetland ecosystems, underscoring the need for comprehensive monitoring and management strategies to mitigate degradation and preserve these vital ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)

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25 pages, 14074 KiB

Open AccessArticle

A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

by Bing Guo, Rui Zhang, Miao Lu, Mei Xu, Panpan Liu and Longhao Wang

Remote Sens. 2024, 16(10), 1771; https://doi.org/10.3390/rs16101771 - 16 May 2024

Cited by 7 | Viewed by 1735

Abstract

As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of “firstly aggravation and then alleviation.” Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast’s aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification. Full article

(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring II)

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15 pages, 5099 KiB

Open AccessArticle

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 - 8 Oct 2023

Cited by 6 | Viewed by 1924

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, 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

Open AccessArticle

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

Cited by 4 | Viewed by 1677

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 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 R_p² (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

Open AccessTechnical 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 2 | Viewed by 2107

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 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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