Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Browser

Land Degradation Assessment with Earth Observation II

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers
Planned Papers

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

Deadline for manuscript submissions: 1 May 2024 | Viewed by 15947

Share This Special Issue

Special Issue Editor

Dr. Elias Symeonakis

E-Mail Website
Guest Editor

Department of Natural Sciences, Manchester Metropolitan University, All Saints Building, Manchester M15 6BH, UK
Interests: remote sensing; land degradation; AI algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For decades now, land degradation has been identified as one of the most pressing problems facing the planet. Alarming estimates are often published by the academic community and intergovernmental organisations, claiming that a third of the planet is undergoing various degradation processes and almost half of the world’s population is already residing in degraded lands. Moreover, as land degradation directly affects the biophysical processes of vegetation and leads to changes in ecosystem functioning, it has a knock-on effect on habitats and, therefore, on numerous species of flora and fauna that become endangered or/and extinct.

The processes that have more commonly been identified as the driving factors behind land degradation are both anthropogenic as well as climatic, and numerous studies have thus far attempted to disentangle the nexus between the two. The most prominent causes have appeared to be the processes of soil erosion by water or wind, soil salinization, gully erosion, natural hazards, land use/cover change, agricultural expansion or abandonment, deforestation, urbanisation, grazing intensification, bush encroachment, fuelwood extraction and drought.

By far the most widely used approach in assessing land degradation has been to employ Earth observation data. Especially during the last decade, with technological advancements and the computational capacity of computers on the one hand, together with the availability of open-access remotely sensed data archives on the other, numerous works dedicated to the study of the various aspects of land degradation have been undertaken. The spectral, spatial and temporal resolution of these studies varies considerably, and multiscale, multitemporal and multisensor approaches have also evolved.

This forthcoming 2nd Volume of the Special Issue on “Land Degradation Assessment with Earth Observation” calls for original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification), but also temperate rangelands, grasslands, woodlands, peatlands and the humid tropics. Papers covering any spatial and temporal scale are welcome, and both abrupt and more salient changes and degradation processes are of interest. Time-series analysis techniques that assess the timing and duration of the reduction in biological productivity brought about by land degradation are also encouraged.

Dr. Elias Symeonakis
Guest Editor

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
Deforestation
Drought
Soil erosion
Land use/cover change
Habitat degradation
Multitemporal analysis
Time-series analysis

Published Papers (7 papers)

Download All Papers

Research

27 pages, 10421 KiB

Open AccessArticle

A New Remote Sensing Desert Vegetation Detection Index

by Zhenqi Song, Yuefeng Lu, Ziqi Ding, Dengkuo Sun, Yuanxin Jia and Weiwei Sun

Remote Sens. 2023, 15(24), 5742; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15245742 - 15 Dec 2023

Viewed by 929

Abstract

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification. Full article

(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)

► Show Figures

Graphical abstract

22 pages, 10504 KiB

Open AccessArticle

Integrated Assessments of Land Degradation in the Three-Rivers Headwater Region of China from 2000 to 2020

by Yao Pan, Yunhe Yin and Wei Cao

Remote Sens. 2023, 15(18), 4521; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15184521 - 14 Sep 2023

Viewed by 892

Abstract

Since the 1970s, certain areas within the Three-Rivers Headwater Region (TRHR) of China have faced severe land degradation due to the combined effects of climate change and human activities, leading to restricted ecological service functions and hindering the achievement of sustainable development goals (SDGs). Land degradation in the TRHR has received widespread attention. However, the current research mainly focuses on single-dimensional degradation and lacks a comprehensive evaluation of patterns and structures, as well as above-ground and underground assessments. To address this gap, this study employed the SDG indicator 15.3.1 framework, comprehensively considering fragmentation and habitat quality index based on land cover changes, grassland degradation index, and soil water erosion index. These indexes represent the three land degradation pathways of landscape degradation, vegetation degradation, and soil erosion. This study assessed land degradation patterns in the TRHR from 2000 to 2020. Results show that approximately 44.67% of the TRHR experienced land degradation during this period, mainly in meadow-dominated regions. Additionally, 5.64% of the regions experienced the superimposition of two or more land degradation pathways, with the frequent coexistence of soil erosion and grassland degradation, accounting for 4.1% of the affected areas. Landscape degradation affected approximately 2.39% of the regions, characterized by increased grassland fragmentation or habitat quality degradation. In terms of grassland degradation, 22.26% of the regions showed medium degradation, while 7.21% and 5.63% experienced moderate and severe degradation, respectively. Moreover, approximately 13.36% of the region faced a worsening situation of soil erosion. Approximately 55.34% of the study area underwent land improvement, with significant enhancements mainly concentrated in the western and eastern regions. The regrowth of grassland in the western region and the enhancement and homogenization of grassland productivity in the eastern region played pivotal roles in promoting land improvement. This study provides critical insights into the land degradation pattern in the TRHR over the past 20 years, offering valuable references for formulating and implementing measures to protect and construct the ecological security barrier of the plateau. Full article

(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)

► Show Figures

Graphical abstract

22 pages, 9872 KiB

Open AccessArticle

Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

by Zhuopeng Zhang, Xiaojie Li, Shuang Zhou, Yue Zhao and Jianhua Ren

Remote Sens. 2023, 15(13), 3249; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133249 - 23 Jun 2023

Viewed by 1109

Abstract

Soil salinity is one of the parameters used for determining the extent of soil salinization. During water evaporation, the surface of salt-affected soils in the Songnen Plain, China, exhibits obvious shrinkage and cracking phenomena due to the high salt content. The aim of this current study is to quantify the influence of the salt content on the surface shrinkage–cracking process and to achieve quantitative extraction of soil salinity parameters based on different crack parameter types. In order to achieve the above objectives, a controlled shrinkage–cracking experiment was conducted. Subsequently, three kinds of crack characteristics such as crack length, box-counting dimension, and 12 gray-level co-occurrence matrix (GLCM) texture features were quantitatively extracted from the standard binary crack patterns. In order to predict the soil physical–chemical properties of salt-affected soils in the Songnen Plain, three models such as multiple linear regression (MLR), multiple stepwise regression (MSR), and artificial neural network (ANN) were developed and compared based on the crack length, box-counting dimension, and the first two principal components of GLCM texture features. The results show that the extent of desiccation cracks was determined by soil salinity since the water film caused by exchangeable cations and the thickness of DDL determined by soil salinity can promote desiccation cracking. Although the three methods have high prediction accuracy for Na⁺, electrical conductivity (EC), and total soil salinity, the ANN-based method showed the best prediction with R² values for Na⁺, EC, and soil salinity as high as 0.91, 0.91, and 0.89, and ratio of performance to deviation (RPD) values for Na⁺, EC, and soil salinity corresponding to 2.96, 3.47, and 2.95. Full article

(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)

► Show Figures

Graphical abstract

19 pages, 3316 KiB

Open AccessArticle

Trends of Aboveground Net Primary Productivity of Patagonian Meadows, the Omitted Ecosystem in Desertification Studies

by Matías Curcio, Gonzalo Irisarri, Guillermo García Martínez and Martín Oesterheld

Remote Sens. 2023, 15(10), 2531; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102531 - 11 May 2023

Cited by 1 | Viewed by 1731

Abstract

The United Nations defines desertification as the loss of productivity in arid and semiarid environments. The extended steppes of Patagonia harbor small meadows whose compounded area is comparatively small, but their aboveground net primary production (ANPP) is up to ten times higher than their surroundings. These meadows then represent a key ecosystem for cattle grazing systems, but there are no descriptions of the trends in their ANPP and, consequently, their carrying capacity, and, as a result, their degradation syndromes. Our objectives were as follows: (1) analyze the trends of mean and spatial heterogeneity of annual ANPP in meadows and neighboring steppes and relate them with precipitation and temperature, (2) evaluate the impact on the livestock carrying capacity of meadows in the region, and (3) evaluate the degradation trends of these meadows, based on a novel description proposed to characterize the trend syndromes of these type of ecosystems. We identified meadow areas across a subcontinental scale in Patagonia, covering a mean annual precipitation range from 129 to 936 mm. We estimated ANPP on a monthly basis from 2000 to 2019 via regional calibrated remote sensing information. In the last two decades, ANPP decreased in 74% of the studied meadow areas, while remaining relatively stable in the nearby steppes. This decrease was relatively higher in the arid end of the analyzed precipitation gradient. Hence, the global carrying capacity for all the studied meadow areas decreased by 8%. Finally, we identified four trend syndromes based on the combination of the ANPP trend and its spatial heterogeneity, calculated as the spatial standard deviation. The predominant trend syndrome, in 55% of the area, was associated with a negative trend of both ANPP and spatial heterogeneity. These results could help prioritize areas where specific management decisions, given the different trend syndromes, could help revert ANPP negative trends. Full article

(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)

► Show Figures

Figure 1

20 pages, 3648 KiB

Open AccessArticle

Assessing Elevation-Based Forest Dynamics over Space and Time toward REDD+ MRV in Upland Myanmar

by Siqi Lu, Chuanrong Zhang, Jinwei Dong, Muhammad Adil and Heli Lu

Remote Sens. 2022, 14(23), 6117; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236117 - 02 Dec 2022

Cited by 2 | Viewed by 1851

Abstract

Implementation of a measuring, reporting, and verifying (MRV) framework is essential for reducing emissions from deforestation and forest degradation (REDD+). According to the United Nations Framework Convention on Climate Change, MRV can be regarded as an important mechanism to mitigate global warming. Upland Myanmar, with an elevation of ~80–2600 m, is experiencing tropical deforestation, which is commonly explained by the expansion of shifting cultivation. The vegetation change tracker algorithm, with its high-automation and wild-adaptation features, and the enhanced integrated forest z-score were applied in this elevation-based study of time series deforestation monitoring in upland Myanmar using data from 2003 to 2015. Four spatial patterns of deforestation, namely stripes, adjacent, filled, and staggered, were found in the research area. Moreover, our work showed that the center of elevation of deforestation was ~1000 m. Further analysis revealed that this center tended to shift to a higher elevation over time; a “golden cross”/changeover could be deciphered at ~1000 m, indicating that the scale and intensity of shifting cultivation continue to expand vertically. The results suggest the need to track the elevation-based signature of vegetation clearings to help achieve the goals of REDD+ at the regional level in tropical rainforest countries. Full article

(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)

► Show Figures

Graphical abstract

20 pages, 11220 KiB

Open AccessArticle

Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020

by Hosea Opedes, Sander Mücher, Jantiene E. M. Baartman, Shafiq Nedala and Frank Mugagga

Remote Sens. 2022, 14(10), 2423; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102423 - 18 May 2022

Cited by 8 | Viewed by 3082

Abstract

Analyzing the dominant forms and extent of land cover changes in the Mount Elgon region is important for tracking conservation efforts and sustainable land management. Mount Elgon’s rugged terrain limits the monitoring of these changes over large areas. This study used multitemporal satellite imagery to analyze and quantify the land cover changes in the upper Manafwa watershed of Mount Elgon, for 42 years covering an area of 320 km². The study employed remote sensing techniques, geographic information systems, and software to map land cover changes over four decades (1978, 1988, 2001, 2010, and 2020). The maximum likelihood classifier and post-classification comparison technique were used in land cover classification and change detection analysis. The results showed a positive percentage change (gain) in planted forest (3966%), built-up (890%), agriculture (186%), and tropical high forest low-stocked (119%) and a negative percentage change (loss) in shrubs (−81%), bushland (−68%), tropical high forest well-stocked (−50%), grassland (−44%), and bare and sparsely vegetated surfaces (−14%) in the period of 1978–2020. The observed changes were concentrated mainly at the peripheries of the Mount Elgon National Park. The increase in population and rising demand for agricultural land were major driving factors. However, regreening as a restoration effort has led to an increase in land area for planted forests, attributed to an improvement in conservation-related activities jointly implemented by the concerned stakeholders and native communities. These findings revealed the spatial and temporal land cover changes in the upper Manafwa watershed. The results could enhance restoration and conservation efforts when coupled with studies on associated drivers of these changes and the use of very-high-resolution remote sensing on areas where encroachment is visible in the park. Full article

(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)

► Show Figures

Graphical abstract

19 pages, 4505 KiB

Open AccessArticle

Agents of Forest Disturbance in the Argentine Dry Chaco

by Teresa De Marzo, Nestor Ignacio Gasparri, Eric F. Lambin and Tobias Kuemmerle

Remote Sens. 2022, 14(7), 1758; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071758 - 06 Apr 2022

Cited by 11 | Viewed by 2691

Abstract

Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances. Here, we apply an approach for attributing agents of forest disturbance across large areas of tropical dry forests, based on the Landsat image time series. Focusing on the 489,000 km² Argentine Dry Chaco, we derived metrics on the spectral characteristics and shape of disturbance patches. We then used these metrics in a random forests classification framework to estimate the area of logging, fire, partial clearing, riparian changes and drought. Our results highlight that partial clearing was the most widespread type of forest disturbance from 1990–to 2017, extending over 5520 km² (±407 km²), followed by fire (4562 ± 388 km²) and logging (3891 ± 341 km²). Our analyses also reveal marked trends over time, with partial clearing generally becoming more prevalent, whereas fires declined. Comparing the spatial patterns of different disturbance types against accessibility indicators showed that fire and logging prevalence was higher closer to fields, while smallholder homesteads were associated with less burning. Roads were, surprisingly, not associated with clear trends in disturbance prevalence. To our knowledge, this is the first attribution of disturbance agents in tropical dry forests based on satellite-based indicators. While our study reveals remaining uncertainties in this attribution process, our framework has considerable potential for monitoring tropical dry forest disturbances at scale. Tropical dry forests in South America, Africa and Southeast Asia are some of the fastest disappearing ecosystems on the planet, and more robust monitoring of forest degradation in these regions is urgently needed. Full article

(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)

► Show Figures

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle After 2018 Hurricane Michael
Authors: Diana Mitsova; Yanmei Li; Ross Einsteder; Alka Sapat; Ann-Margaret Esnard; Tiffany Roberts-Briggs
Affiliation: Florida Atlantic University; Georgia State University University
Abstract: The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often knock down power lines and trees and damage electrical substations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA's National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013-2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socio-economic factors. The analysis revealed notable differences in power restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. Our study indicates that near-real-time satellite imagery can facilitate rapid assessments and timely delivery of emergency services to the hardest hit areas, and provide decision support for community recovery plans.

Journal Menu

Journal Browser

Land Degradation Assessment with Earth Observation II

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Planned Papers

Further Information

Guidelines

MDPI Initiatives

Follow MDPI