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Remote Sensing for Land Degradation and Drought Monitoring

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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 (10 February 2023) | Viewed by 17747

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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. 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,

Land degradation (LD) and droughts are among the most serious challenges worldwide, affecting people’s livelihoods and the health of socioecological systems. The role of Earth Observation has become paramount for monitoring and assessing both phenomena. However, there are still some methodological and conceptual gaps that should be urgently addressed to advance progress in deriving spatially explicit and reliable information and indicators on LD and droughts.

This upcoming Special Issue on “Remote Sensing for Land Degradation and Drought Monitoring” calls for original research papers focused on monitoring land degradation and drought 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 encouraged. The use of integrative spatial modelling approaches for monitoring and early warning of both phenomena is also of interest.

Dr. Olena Dubovyk
Dr. Tobias Landmann
Guest Editors

Manuscript Submission Information

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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 (8 papers)

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20 pages, 3116 KiB

Open AccessArticle

Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms

by Yonghong Zhang, Donglin Xie, Wei Tian, Huajun Zhao, Sutong Geng, Huanyu Lu, Guangyi Ma, Jie Huang and Kenny Thiam Choy Lim Kam Sian

Remote Sens. 2023, 15(3), 667; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030667 - 22 Jan 2023

Cited by 7 | Viewed by 3043

Abstract

Drought is one of the major global natural disasters, and appropriate monitoring systems are essential to reveal drought trends. In this regard, deep learning is a very promising approach for characterizing the non-linear nature of drought factors. We used multi-source remote sensing data such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data to integrate drought impact factors such as precipitation, vegetation, temperature, and soil moisture. The application of convolutional long short-term memory (ConvLSTM) to construct an integrated drought monitoring model was proposed and tested, using the Xinjiang Uygur Autonomous Region as an example. To better compare the monitoring performance of ConvLSTM models, three other classical deep learning models and three classical machine learning models were also used for comparison. The results show that the composite drought index (CDI) output by the ConvLSTM model had a consistent high correlation with the drought rating of the multi-scale standardized precipitation evapotranspiration index (SPEI). The correlation coefficients between the CDI and the multi-scale standardized precipitation index (SPI) were all above 0.5 (p < 0.01), which was highly significant, and the correlation coefficient between CDI-1 and the monthly soil relative humidity at a 10 cm depth was above 0.45 (p < 0.01), which was well correlated. In addition, the spatial distribution of the CDI-6 simulated by the model was highly correlated with the degree of drought expressed by the SPEI-6 observations at the stations. This study provides a new approach for integrated regional drought monitoring. Full article

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

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17 pages, 11808 KiB

Open AccessArticle

Remote Radio-Physical Harbingers of Drought in Steppes of the South of Western Siberia

by Andrey Romanov, Ivan Ryabinin, Ilya Khvostov, Dmitry Troshkin and Dmitry Romanov

Remote Sens. 2022, 14(23), 6141; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236141 - 03 Dec 2022

Cited by 1 | Viewed by 1210

Abstract

Methods for remote sensing of the underlying surface in the microwave range based on moisture dependence of soil emissivity were successfully used in monitoring droughts and assessing water availability of the studied territories. Soil moisture influence on soil cover emissivity calibrated in units of the radio brightness temperature (T_B) was studied. We used values of T_B derived from SMOS satellite data. This paper presents the results of a comparative analysis of soil, meteorological conditions and physical characteristics of soils in the test territories of the Kulunda Plain. The experimental data were applied in computing trends of T_B and physical temperature (T) described by linear dependencies. Volume fractions of water (W) in soil were calculated based on the satellite sensing data, the results of field studies and laboratory measurements of dielectric characteristics of soils. A map scheme of spatial distribution of W was constructed and the influences of snow cover, precipitation and surface wind velocity on drought were analyzed. The comprehensive analysis of remote, field and laboratory data suggest that the rate of change in the brightness temperature (dT_BH/dD—up to 17 K per day), which characterizes the rate of fall in volume humidity of soil (ΔW—up to 0.009 cm³/cm³ per day), can be used as a short-term radio-physical harbinger of drought. An experimental dependence of the rate of change in radio brightness temperature on the rate of change in soil moisture was established. Full article

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

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18 pages, 6576 KiB

Open AccessArticle

Responses of the Remote Sensing Drought Index with Soil Information to Meteorological and Agricultural Droughts in Southeastern Tibet

by Ziyu Wang, Zegen Wang, Junnan Xiong, Wen He, Zhiwei Yong and Xin Wang

Remote Sens. 2022, 14(23), 6125; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236125 - 02 Dec 2022

Cited by 4 | Viewed by 1536

Abstract

The Temperature–Vegetation–Precipitation–Drought Index (TVPDI) has a good performance in drought monitoring in China. However, different regions have different responses to droughts due to terrain differences. In southeastern Tibet, the drought monitoring capacity of some drought indices without soil information has to be assessed on account of the poor sensitivity between temperature and soil humidity. Therefore, soil moisture was added to calculate a new drought index based on TVPDI in southeastern Tibet, named the Temperature–Vegetation–Soil-Moisture–Precipitation–Drought Index (TVMPDI). Then, the TVMPDI was validated by using the Standardized Precipitation Evapotranspiration Index (SPEI) and other remote sensing drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), during the growing seasons of 2003–2018. The Standardized Precipitation Index (SPI) and SPEI were used to represent meteorological drought and Gross Primary Productivity (GPP) was used to represent agricultural drought. The relation between TVMPDI and these drought indices was compared. Finally, the time trends of TVMPDI were also analyzed. The relation coefficients of TVMPDI and SPEI were above 0.5. The correlations between TVMPDI and drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), also had a good performance. The correlation between the meteorological drought indices (SPI and SPEI) and TVMPDI were not as good as for the TVPDI, but the temporal correlation between the TVMPDI and GPP was greater than that between the TVPDI and GPP. This indicates that the TVMPDI is more suitable for monitoring agricultural drought than the TVPDI. In addition, historical drought monitoring had values that were consistent with those of the actual situation. The trend of the TVMPDI showed that drought in the study area was alleviated from 2003 to 2018. Furthermore, GPP was negatively correlated with SPEI (r = −0.4) and positively correlated with Soil Moisture (SM) drought index (TVMPDI, SMCI) (r = 0.4) in the eastern part of the study area, which suggests that SM, rather than precipitation, could promote the growth of vegetation in the region. A correct understanding of the role of soil information in drought comprehensive indices may monitor meteorological drought and agricultural drought more accurately. Full article

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

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21 pages, 49928 KiB

Open AccessArticle

Remote Sensing Monitoring of Vegetation Reclamation in the Antaibao Open-Pit Mine

by Jiameng Hu, Baoying Ye, Zhongke Bai and Yu Feng

Remote Sens. 2022, 14(22), 5634; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225634 - 08 Nov 2022

Cited by 7 | Viewed by 1679

Abstract

After the regreening of the open-pit mine dump, vegetation usually needs to be managed and protected manually for several years before it reaches stability. Due to the spontaneous combustion of coal gangue, surface collapse, and other reasons, secondary damage may occur at any time. Regreening monitoring plays a vital role in the restoration and reconstruction of the mining ecosystem and can provide support for the timely replenishment of seedlings in the damaged area. In this study, remote sensing images were collected from 1986 to 2020 to obtain the NDVI distribution of dumps in the Antaibao open-pit coal mine. In order to obtain the overall growth law of regreening vegetation over time, the study adopted the unary regression analysis method and tested the correlation between NDVI and time by the Pearson correlation coefficient. However, through the Sen+Mann–Kendall trend analysis, it was found that there were differences in the trends of NDVI within the same dump. Next, by means of the Mann–Kendall mutation test and interactive interpretation, information, such as stable nodes of different regreening vegetation and vegetation growth patterns in degraded areas, were obtained. Through the above methods, the following conclusions were drawn: (1) The earlier the dumps were regreened, the more the areas were covered by significantly improved vegetation. In this study: 97.31% (the proportion of significantly improved vegetation in the south dump) >95.58% (the proportion in the west dump) >86.56% (the proportion in the inner dump) >79.89% (the proportion in the west expansion dump). (2) Different vegetation types have different time nodes for reaching stability. It takes about three years for wood, shrub, and a mix of grass, shrub, and wood to reach stability, but only one year for grass. (3) The destruction in mining areas is expansive and repeatable. Monitoring the growth patterns of regreening vegetation is conducive to understanding the reclamation effect, and provides a scientific basis for land reclamation planning and land management policies in the mining area. At the same time, the trend analysis method in this study can quickly extract problem areas after dump regreening and is applicable in most dumps. Full article

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

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29 pages, 8759 KiB

Open AccessArticle

Evaluating Satellite Soil Moisture Datasets for Drought Monitoring in Australia and the South-West Pacific

by Jessica Bhardwaj, Yuriy Kuleshov, Zhi-Weng Chua, Andrew B. Watkins, Suelynn Choy and Qian (Chayn) Sun

Remote Sens. 2022, 14(16), 3971; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14163971 - 16 Aug 2022

Cited by 7 | Viewed by 2086

Abstract

Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific. Full article

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

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17 pages, 30468 KiB

Open AccessArticle

Quantifying Vegetation Vulnerability to Climate Variability in China

by Liangliang Jiang, Bing Liu and Ye Yuan

Remote Sens. 2022, 14(14), 3491; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143491 - 21 Jul 2022

Cited by 5 | Viewed by 1877

Abstract

Climate variability has profound effects on vegetation. Spatial distributions of vegetation vulnerability that comprehensively consider vegetation sensitivity and resilience are not well understood in China. Furthermore, the combination of cumulative climate effects and a one-month-lagged autoregressive model represents an advance in the technical approach for calculating vegetation sensitivity. In this study, the spatiotemporal characteristics of vegetation sensitivity to climate variability and vegetation resilience were investigated at seasonal scales. Further analysis explored the spatial distributions of vegetation vulnerability for different regions. The results showed that the spatial distribution pattern of vegetation vulnerability exhibited spatial heterogeneity in China. In spring, vegetation vulnerability values of approximately 0.9 were mainly distributed in northern Xinjiang and northern Inner Mongolia, while low values were scattered in Yunnan Province and the central region of East China. The highest proportion of severe vegetation vulnerability to climate variability was observed in the subhumid zone (28.94%), followed by the arid zone (26.27%). In summer and autumn, the proportions of severe vegetation vulnerability in the arid and humid zones were higher than those in the other climate zones. Regarding different vegetation types, the highest proportions of severe vegetation vulnerability were found in sparse vegetation in different seasons, while the highest proportions of slight vegetation vulnerability were found in croplands in different seasons. In addition, vegetation with high vulnerability is prone to change in Northeast and Southwest China. Although ecological restoration projects have been implemented to increase vegetation cover in northern China, low vegetation resilience and high vulnerability were observed in this region. Most grasslands, which were mainly concentrated on the Qinghai–Tibet Plateau, had high vulnerability. Vegetation areas with low resilience were likely to be degraded in this region. The areas with highly vulnerable vegetation on the Qinghai–Tibet Plateau could function as warning signals of vegetation degradation. Knowledge of spatial patterns of vegetation resilience and vegetation vulnerability will help provide scientific guidance for regional environmental protection. Full article

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

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18 pages, 5222 KiB

Open AccessArticle

Remote Sensing Monitoring of the Spatial Pattern of Greening and Browning in Xilin Gol Grassland and Its Response to Climate and Human Activities

by Jiawei Hui, Zanxu Chen, Baoying Ye, Chu Shi and Zhongke Bai

Remote Sens. 2022, 14(7), 1765; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071765 - 06 Apr 2022

Cited by 4 | Viewed by 2510

Abstract

As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area’s vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection. Full article

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

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13 pages, 4892 KiB

Open AccessTechnical Note

Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015

by Jianfang Kang, Yaonan Zhang and Asim Biswas

Remote Sens. 2021, 13(17), 3516; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173516 - 04 Sep 2021

Cited by 13 | Viewed by 2344

Abstract

Land degradation and development (LDD) has become an urgent global issue. Quick and accurate monitoring of LDD dynamics is key to the sustainability of land resources. By integrating normalized difference vegetation index (NDVI) and net primary productivity (NPP) based on the Euclidean distance method, a LDD index (LDDI) was introduced to detect LDD processes, and to explore its quantitative relationship with climate change and human activity in China from 1985 to 2015. Overall, China has experienced significant land development, about 45% of China’s mainland, during the study period. Climate change (temperature and precipitation) played limited roles in the affected LDD, while human activity was the dominant driving force. Specifically, LDD caused by human activity accounted for about 58% of the total, while LDD caused by climate change only accounted for 0.34% of the total area. Results from the present study can provide insight into LDD processes and their driving factors and promote land sustainability in China and around the world. Full article

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

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