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Land
Special Issues
Earth Observation (EO) for Land Degradation and Disaster Monitoring
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Earth Observation (EO) for Land Degradation and Disaster Monitoring

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 22668

Special Issue Editors

Dr. Israel R. Orimoloye

E-Mail Website
Guest Editor
Department of Geography, Faculty of Food and Agriculture, University of the West Indies, St. Augustine 685509, Trinidad and Tobago
Interests: remote sensing & GIS; drought disaster; disaster management; climate science; environmental management; machine learning
Dr. Adeyemi Oludapo Olusola

E-Mail Website
Guest Editor
Faculty of Environmental and Urban Change, York University, Toronto, ON M3J 1P3, Canada
Interests: geomorphology; remote sensing; tropical environments; resource management and conservation
Dr. Chaitanya B. Pande

E-Mail Website
Guest Editor
National Institute of Rural Development, Hyderbad 500030, India
Interests: remote sensing & GIS; drought analysis and forecasting; machine learning; LULC; Google Earth Engine application in drought; climate science and disaster management and planning; environmental resources and monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Excessive usage of natural resources fosters land degradation, which reduces the efficiency of crucial ecosystem services such as drought, flood, and landslide mitigation (Orimoloye et al., 2020). This leads to increased risk from disasters, and natural hazards can further degrade the environment. Land degradation is the decline of the land's capacity to meet social and ecological goals and needs. Disaster risk is increased by this deterioration, the resulting decline in ecosystems, and the invaluable benefits we receive from those services. Hazard frequency and intensity, as well as our exposure and susceptibility to them, can all be affected by land and environmental changes.

Drought risk assessment needs up-to-date and precise information on land cover changes, slope, agriculture, natural resources, climatic factors, and the use of land development and planning. Currently, remote sensing, GIS, and the machine-learning approach are very powerful tools in disaster risk assessment, land use systems, and their influence on land degradation, drought, flood, land use changes, climatic hazards monitoring, and assessment. This Special Issue will focus on the emerging topics of development and planning of land using advanced technology and satellite data.

Therefore, it is of far-reaching significance to understand or monitor land degradation and disaster and their impact on the natural ecosystem, and the physical characteristics of the ecological environment through quantitative and qualitative evaluations using earth observation technology.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Remote sensing applications in drought and flood monitoring;
  • Land degradation appraisal from space;
  • Climate hazards assessment;
  • Landslide assessment using earth observation data;
  • Land use system and its influence on land degradation;
  • Land management;
  • Disaster risk assessment;
  • Agricultural land monitoring;
  • Urban agricultural land;
  • Machine learning in drought and agricultural land monitoring;
  • Resource Management and Conservation;
  • Coastal land monitoring;
  • Coastal hazards and climate adaptation.

Dr. Israel R. Orimoloye
Dr. Adeyemi Oludapo Olusola
Dr. Chaitanya B. Pande
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. Land is an international peer-reviewed open access monthly 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 2600 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

  • remote sensing
  • Earth observation technology
  • climate hazards
  • land use change
  • drought
  • flood
  • land degradation
  • climate change
  • disaster science

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

23 pages, 5071 KiB  
Article
Assessment of Spatial–Temporal Variations of Soil Erosion in Hulunbuir Plateau from 2000 to 2050
by Jianglong Yuan, Xiaohuang Liu, Hongyu Li, Ran Wang, Xinping Luo, Liyuan Xing, Chao Wang and Honghui Zhao
Land 2023, 12(6), 1214; https://doi.org/10.3390/land12061214 - 11 Jun 2023
Cited by 8 | Viewed by 2026
Abstract
The study area was the Hulunbuir Plateau in northeastern China, based on a natural resource element observation study. The assessment of the spatial and temporal variation of soil erosion is crucial for implementing environmental management in the fragile ecosystem of the Hulunbuir Plateau. [...] Read more.
The study area was the Hulunbuir Plateau in northeastern China, based on a natural resource element observation study. The assessment of the spatial and temporal variation of soil erosion is crucial for implementing environmental management in the fragile ecosystem of the Hulunbuir Plateau. The study provides an interesting basis for soil erosion control on the Hulunbuir Plateau and other areas with similar climatic conditions, with the aim of providing sound data to support environmental protection policies in the study area. In this study, the spatial and temporal variations in soil erosion in the region from 2000 to 2020 were quantitatively assessed using the Revised Universal Soil Loss Equation. Furthermore, the patch-generating land use simulation model predicted future soil erosion. Land use prediction data were examined using Kappa coefficients. The prediction of future land use types using CMIP6 data and natural social data in the PLUS model were used to predict soil erosion for different future scenarios. The results showed that the soil erosion rate on the Hulunbuir Plateau showed a significant increasing trend in time from 2000 to 2020. Spatially, soil erosion increases gradually from the west to the east. Soil erosion occurs mainly on grasslands, while cultivated lands show a significant increasing trend by 2020. Slope erosion occurs mainly in areas between 15° and 35°. From 2020 to 2050, soil erosion will increase significantly due to increased precipitation. The soil erosion in SSP2–4.5 is better than the other scenarios. Full article
(This article belongs to the Special Issue Earth Observation (EO) for Land Degradation and Disaster Monitoring)
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40 pages, 9758 KiB  
Article
Assessing and Mapping Spatial Variation Characteristics of Natural Hazards in Pakistan
by Muhammad Awais Hussain, Shuai Zhang, Muhammad Muneer, Muhammad Aamir Moawwez, Muhammad Kamran and Ejaz Ahmed
Land 2023, 12(1), 140; https://doi.org/10.3390/land12010140 - 31 Dec 2022
Cited by 8 | Viewed by 7371
Abstract
One nation with the highest risk of climate catastrophes is Pakistan. Pakistan’s geographical nature makes it susceptible to natural hazards. Pakistan is facing regional differences in terms of climate change. The frequency and intensity of natural hazards due to climate change vary from [...] Read more.
One nation with the highest risk of climate catastrophes is Pakistan. Pakistan’s geographical nature makes it susceptible to natural hazards. Pakistan is facing regional differences in terms of climate change. The frequency and intensity of natural hazards due to climate change vary from place to place. There is an urgent need to recognize the spatial variations in natural hazards inside the country. To address such problems, it might be useful to map out the areas that need resources to increase resilience and accomplish adaptability. Therefore, the main goal of this research was to create a district-level map that illustrates the multi-hazard zones of various regions in Pakistan. In order to comprehend the geographical differences in climate change and natural hazards across Pakistan, this study examines the relevant literature and data currently available regarding the occurrence of natural hazards in the past. Firstly, a district-level comprehensive database of Pakistan’s five natural hazards (floods, droughts, earthquakes, heatwaves, and landslides) was created. Through consultation with specialists in related areas, hazard and weighting factors for a specific hazard were specified based on the structured district-level historical disaster database of Pakistan. After that, individual and multi-hazard ratings were computed for each district. Then, using estimated multi-hazard scores, the districts of Pakistan were classified into four zones. Finally, a map of Pakistan’s multi-hazard zones was created per district. The study results are essential and significant for policymakers to consider when making decisions on disaster management techniques, that is, when organizing disaster preparedness, mitigation, and prevention plans. Full article
(This article belongs to the Special Issue Earth Observation (EO) for Land Degradation and Disaster Monitoring)
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22 pages, 56746 KiB  
Article
Monitoring of Inland Excess Water Inundations Using Machine Learning Algorithms
by Balázs Kajári, Csaba Bozán and Boudewijn Van Leeuwen
Land 2023, 12(1), 36; https://doi.org/10.3390/land12010036 - 22 Dec 2022
Cited by 6 | Viewed by 2168
Abstract
Nowadays, climate change not only leads to riverine floods and flash floods but also to inland excess water (IEW) inundations and drought due to extreme hydrological processes. The Carpathian Basin is extremely affected by fast-changing weather conditions during the year. IEW (sometimes referred [...] Read more.
Nowadays, climate change not only leads to riverine floods and flash floods but also to inland excess water (IEW) inundations and drought due to extreme hydrological processes. The Carpathian Basin is extremely affected by fast-changing weather conditions during the year. IEW (sometimes referred to as water logging) is formed when, due to limited runoff, infiltration, and evaporation, surplus water remains on the surface or in places where groundwater flowing to lower areas appears on the surface by leaking through porous soil. In this study, eight different machine learning approaches were applied to derive IEW inundations on three different dates in 2021 (23 February, 7 March, 20 March). Index-based approaches are simple and provide relatively good results, but they need to be adapted to specific circumstances for each area and date. With an overall accuracy of 0.98, a Kappa of 0.65, and a QADI score of 0.020, the deep learning method Convolutional Neural Network (CNN) gave the best results, compared to the more traditional machine learning approaches Maximum Likelihood (ML), Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN) that were evaluated. The CNN-based IEW maps can be used in operational inland excess water control by water management authorities. Full article
(This article belongs to the Special Issue Earth Observation (EO) for Land Degradation and Disaster Monitoring)
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24 pages, 11323 KiB  
Article
Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree
by Chaitanya B. Pande, Nadhir Al-Ansari, N. L. Kushwaha, Aman Srivastava, Rabeea Noor, Manish Kumar, Kanak N. Moharir and Ahmed Elbeltagi
Land 2022, 11(11), 2040; https://doi.org/10.3390/land11112040 - 14 Nov 2022
Cited by 51 | Viewed by 4446
Abstract
Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their [...] Read more.
Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations. Full article
(This article belongs to the Special Issue Earth Observation (EO) for Land Degradation and Disaster Monitoring)
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28 pages, 37302 KiB  
Article
Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation
by Markos Mathewos, Semaria Moga Lencha and Misgena Tsegaye
Land 2022, 11(10), 1632; https://doi.org/10.3390/land11101632 - 22 Sep 2022
Cited by 32 | Viewed by 4701
Abstract
Land use and land cover change (LULC) is known worldwide as a key factor of environmental modification that significantly affects natural resources. The aim of this study was to evaluate the dynamics of land use and land cover in the Matenchose watershed from [...] Read more.
Land use and land cover change (LULC) is known worldwide as a key factor of environmental modification that significantly affects natural resources. The aim of this study was to evaluate the dynamics of land use and land cover in the Matenchose watershed from the years 1991, 2003, and 2020, and future prediction of land use changes for 2050. Landsat TM for 1991, ETM+ for 2003, and Landsat-8 OLI were used for LULC classification for 2020. A supervised image sorting method exhausting a maximum likelihood classification system was used, with the application using ERDAS Imagine software. Depending on the classified LULC, the future LULC 2050 was predicted using CA-Markov and Land Change Models by considering the different drivers of LULC dynamics. The 1991 LULC data showed that the watershed was predominantly covered by grassland (35%), and the 2003 and 2020 LULC data showed that the watershed was predominantly covered by cultivated land (36% and 52%, respectively). The predicted results showed that cultivated land and settlement increased by 6.36% and 6.53%, respectively, while forestland and grassland decreased by 63.76% and 22.325, respectively, from 2020 to 2050. Conversion of other LULC categories to cultivated land was most detrimental to the increase in soil erosion, while forest and grassland were paramount in reducing soil loss. The concept that population expansion and relocation have led to an increase in agricultural land and forested areas was further reinforced by the findings of key informant interviews. This study result might help appropriate decision making and improve land use policies in land management options. Full article
(This article belongs to the Special Issue Earth Observation (EO) for Land Degradation and Disaster Monitoring)
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