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Special Issue "Land Degradation Assessment with Earth Observation II"

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

Deadline for manuscript submissions: 31 July 2023 | Viewed by 8333

Special Issue Editor

Department of Natural Sciences, Manchester Metropolitan University, All Saints Building, Manchester M15 6BH, UK
Interests: land use/cover change; land degradation; desertification; multi-temporal analysis; sub-Saharan Africa; Mediterranean
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 2500 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 (4 papers)

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Research

Article
Trends of Aboveground Net Primary Productivity of Patagonian Meadows, the Omitted Ecosystem in Desertification Studies
Remote Sens. 2023, 15(10), 2531; https://doi.org/10.3390/rs15102531 - 11 May 2023
Viewed by 856
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 [...] Read more.
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)
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Article
Assessing Elevation-Based Forest Dynamics over Space and Time toward REDD+ MRV in Upland Myanmar
Remote Sens. 2022, 14(23), 6117; https://doi.org/10.3390/rs14236117 - 02 Dec 2022
Viewed by 949
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 [...] Read more.
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)
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Article
Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020
Remote Sens. 2022, 14(10), 2423; https://doi.org/10.3390/rs14102423 - 18 May 2022
Cited by 4 | Viewed by 1992
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 [...] Read more.
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)
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Article
Agents of Forest Disturbance in the Argentine Dry Chaco
Remote Sens. 2022, 14(7), 1758; https://doi.org/10.3390/rs14071758 - 06 Apr 2022
Cited by 5 | Viewed by 1725
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 [...] Read more.
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)
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