Special Issue "Applications of Remote Sensing in Rangelands Research"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (26 July 2020).

Special Issue Editors

Dr. Robert Washington-Allen
Website
Guest Editor
Department of Agriculture, Veterinary and Rangeland Sciences, University of Nevada, Reno, NV 89557, USA
Interests: dryland ecology; LiDAR; remote sensing; rangeland management
Special Issues and Collections in MDPI journals
Dr. R. Douglas Ramsey
Website
Guest Editor
Director of the Remote Sensing and GIS Laboratory, Department of Wildland Resources, Quinney College of Natural Resources, Utah State University, Logan, Utah 84322, USA
Interests: Application of remote sensing to solve problems in natural resource management, geology, landscape ecology and archeology. Airborne digital photography, ecosystem modeling, GIS database development to aid in resource management and research. Development of analytical techniques for analyzing spatial data. Research in Western U.S., Mexico, Egypt, Iceland, India, and China in semi-arid, forested, agricultural, and urbanized landscapes

Special Issue Information

Dear Colleagues,

According to the United Nations Convention to Combat Desertification (UNCCD), drylands make up some 41% of the terrestrial surface and provide ~ 1 billion humans with US $1 trillion dollars in ecosystem services that include food, fiber, shelter, clothing, and energy. Despite this justification for the necessity of conducting research to understand the sustainability of drylands, a large uncertainty still remains as to the degree of dryland degradation, estimated at between 10–50 %. This is due in part to drylands being understudied in comparison to forest ecosystems, particularly in the application of remote sensing technologies to understand their ecological dynamics. 

To address the issue of bias in dryland remote sensing research, in 2013 we conducted a keyword search across published journal articles for the word “Landsat”. This resulted 14,326 hits which were subsequently searched for articles containing “forest(s) and tree(s)” while excluding “rangelands, grazing, shrublands, shrub, grassland, semi-arid and arid land”. This search yielded 3,222 hits. We then searched the 14,236 hits for the keywords: “rangelands, grazing, shrublands, shrub, grassland, grass, semi-arid and arid lands” and excluded “forest(s) and tree(s) from the search yielding 1,692 papers. Consequently, of the total of 4,914 forest and tree and rangeland remote sensing papers, drylands made up 34% of the remote sensing papers and forests (and other classes) accounted for 66%, thus indicating a bias towards forest remote sensing and support for the view that drylands are understudied.

This lack of research into drylands has led to surprises or counterintuitive findings that are consistent with drylands exhibiting complex systems behavior including multiple dynamic regimes and both discontinuous (i.e., thresholds) and continuous behavior. Surprises like the discovery that drylands that evolved under grazing show compensatory feedbacks under moderate grazing regimes, or that in 2011 drylands were globally the largest carbon sink rather than forests, and that drylands exhibited carbon dynamics that are similar to deciduous forests. These and future findings are of critical importance if we are to understand the role of drylands in the Earth system.

Consequently, to close the knowledge gap and to increase the publication of remote sensing studies on drylands we offer this Special Issue to our colleagues that will include, but will not be limited to, the following topics on the remote sensing of drylands:

  • Land Use/Land Cover Change (Ecological Sites/State-and-Transition Models)
  • Detection and Assessment of Belowground Biomass
  • Use of Lidar for Above-Ground Carbon Storage in Drylands
  • Dryland Ecohydrology/Soil Moisture Assessments
  • Use of Emerging Technologies
  • Drones/UAS
  • Phenocams
  • Soil Erosion
  • Terrestrial Laser Scanning
  • National Level Early Warning Systems
Dr. Robert A. Washington-Allen
Dr. R. Douglas Ramsey
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 papers will be 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 2400 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

  • Drylands 
  • Belowground Biomass
  • Aboveground Biomass (AGB) 
  • Net Primary Productivity (NPP) 
  • Early Warning Systems
  • Dynamic Regimes
  • Catastrophe Theory 
  • Ground Penetrating Radar
  • Terrestrial Laser Scanning
  • Phenocams
  • Solar Induced Florescence 
  • Forage Quality

Published Papers (3 papers)

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Open AccessArticle
Mapping and Monitoring Fractional Woody Vegetation Cover in the Arid Savannas of Namibia Using LiDAR Training Data, Machine Learning, and ALOS PALSAR Data
Remote Sens. 2019, 11(22), 2633; https://doi.org/10.3390/rs11222633 - 11 Nov 2019
Cited by 3
Abstract
Namibia is a very arid country, which has experienced significant bush encroachment and associated decreased livestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities, including selective bush thinning and complete bush clearing. The aim of study was to [...] Read more.
Namibia is a very arid country, which has experienced significant bush encroachment and associated decreased livestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities, including selective bush thinning and complete bush clearing. The aim of study was to develop a system to map and monitor fractional woody cover (FWC) at national scales (50 m and 75 m resolution) using Synthetic Aperture Radar (SAR) satellite data (Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics, 2009, 2010, 2015, 2016) and ancillary variables (mean annual precipitation—MAP, elevation), with machine learning models that were trained with diverse airborne Light Detection and Ranging (LiDAR) data sets (244,032 ha, 2008–2014). When only the SAR variables were used, an average R2 of 0.65 (RSME = 0.16) was attained. Adding either elevation or MAP, or both ancillary variables, increased the mean R2 to 0.75 (RSME = 0.13), and 0.79 (RSME = 0.12). The inclusion of MAP addressed the overestimation of FWC in very arid areas, but resulted in anomalies in the form of sharp gradients in FWC along a MAP contour which were most likely caused by to the geographic distribution of the LiDAR training data. Additional targeted LiDAR acquisitions could address this issue. This was the first attempt to produce SAR-derived FWC maps for Namibia and the maps contain substantially more detailed spatial information on woody vegetation structure than existing national maps. During the seven-year study period the Shrubland–Woodland Mosaic was the only vegetation structural class that exhibited a regional net gain in FWC of more than 0.2 across 9% (11,906 km2) of its area that may potentially be attributed to bush encroachment. FWC change maps provided regional insights and detailed local patterns related to debushing and regrowth that can inform national rangeland policies and debushing programs. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Rangelands Research)
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Open AccessArticle
Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data
Remote Sens. 2019, 11(9), 1014; https://doi.org/10.3390/rs11091014 - 29 Apr 2019
Cited by 7
Abstract
Grassland ecosystems in China have experienced degradation caused by natural processes and human activities. Time series segmentation and residual trend analysis (TSS-RESTREND) was applied to grasslands in eastern China. TSS-RESTREND is an extended version of the residual trend (RESTREND) methodology. It considers breakpoint [...] Read more.
Grassland ecosystems in China have experienced degradation caused by natural processes and human activities. Time series segmentation and residual trend analysis (TSS-RESTREND) was applied to grasslands in eastern China. TSS-RESTREND is an extended version of the residual trend (RESTREND) methodology. It considers breakpoint detection to identify pixels with abrupt ecosystem changes which violate the assumptions of RESTREND. With TSS-RESTREND, in Xilingol (111°59′–120°00′E and 42°32′–46°41′E) and Hulunbuir (115°30′–122°E and 47°10′–51°23′N) grassland, 6% and 3% of the area experienced a decrease in greenness between 1984 and 2009, 80% and 73% had no significant change, 5% and 3% increased in greenness, and 9% and 21% were undetermined, respectively. RESTREND may underestimate the greening trend in Xilingol, but both TSS-RESTREND and RESTREND revealed no significant differences in Hulunbuir. The proposed TSS-RESTREND methodology captured both the time and magnitude of vegetation changes. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Rangelands Research)
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Open AccessTechnical Note
Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types
Remote Sens. 2019, 11(12), 1427; https://doi.org/10.3390/rs11121427 - 15 Jun 2019
Cited by 1
Abstract
Understanding and monitoring the dynamics of rangeland heterogeneity through time and across space is critical for the effective management and conservation of rangeland systems and the sustained supply of the ecosystem goods and services they provide. Conventional approaches (both field-based and remote sensing) [...] Read more.
Understanding and monitoring the dynamics of rangeland heterogeneity through time and across space is critical for the effective management and conservation of rangeland systems and the sustained supply of the ecosystem goods and services they provide. Conventional approaches (both field-based and remote sensing) to monitoring rangeland productivity fail to effectively capture important aspects of this heterogeneity. While field methods can effectively capture high levels of detail at fine spatial and temporal resolutions, they are limited in their applicability and scalability to larger spatial extents and longer time periods. Alternatively, remote sensing based approaches that scale broad spatiotemporal extents simplify important heterogeneity occurring at fine scales. We address these limitations to monitoring rangeland productivity by combining a continuous plant functional type (PFT) fractional cover dataset with a Landsat derived gross primary production (GPP) and net primary production (NPP) model. Integrating the annual PFT dataset with a 16-day Landsat normalized difference vegetation (NDVI) composite dataset enabled us to disaggregate the pixel level NDVI values to the sub-pixel PFTs. These values were incorporated into the productivity algorithm, enabling refined estimations of 16-day GPP and annual NPP for the PFTs that composed each pixel. We demonstrated the results of these methods on a set of representative rangeland sites across the western United States. Partitioning rangeland productivity to sub-pixel PFTs revealed new dynamics and insights to aid the sustainable management of rangelands. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Rangelands Research)
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