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Remote Sensing of Dryland Environment

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 28327

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Special Issue Editors

Dr. Olena Dubovyk

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Guest Editor

Centre for Remote Sensing of Land Surfaces (ZFL), Geography Institute, University of Bonn, Genscherallee 3, D-53113 Bonn, Germany
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)
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Dr. Zhiwei Xu

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Guest Editor

School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Interests: geomorphology; landscape dynamics; arid zone; quaternary geology; climate change; global environmental change
Special Issues, Collections and Topics in MDPI journals

Dr. Tobias Landmann

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Guest Editor

Remote Sensing Solutions GmbH, Dingolfinger Str. 9, 81673 Munich, Germany
Interests: understanding socio-ecological systems in Africa and Asia; developing of geo-spatial solutions for environmental health monitoring (degradation and deforestation); cropland and rangeland productivity mapping; spatial epidemiology in Africa; ecosystems services analysis and reporting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A large portion of the human population lives in dryland areas, and these biomes also offer a variety of ecosystem services to the local people. In spite of their importance, systematically mapping and characterizing them has been somewhat neglected by the remote sensing community. This is in part due to their bio-complexity and the diffuse scattering of satellite signals in open and sparsely-vegetated areas. Moreover, drylands exhibit large temporal dynamics because of their dependence on rainfall and are, thus, very sensitive to climate variability and human-driven land degradation. Capturing these intricate spatial and temporal land change processes requires multi-source and multi-scale data sets and fusion algorithms that intelligently integrate in situ data, remote sensing observations and modelling results. To reflect their intra-annual and inter-annual variations, the use of well-processed time series data is imperative. Specifically, monitoring dryland phenology from space plays an important role in assessing the anthropogenic pressures and drivers in drylands. Further combining remote sensing with process-based models offer the opportunity to unravel land change effects and consequences in drylands.

This Special Issue, therefore, calls for manuscripts that deal with assessing environmental issues in drylands using multi-scale and multi-source data in an integrated way. Specifically, manuscripts are encouraged that illustrate the possibilities of how multi-source data sets in terms of better dealing with land degradation in drylands, invasive species encroachment and land management issues and policy and decision support.

Dr. Olena Dubovyk
Assoc. Prof. Zhiwei Xu
Dr. Tobias Landmann
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. 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

Time series analysis and land surface phenology
Dryland characterization and modelling
Land use and land cover dynamics and rangeland integrity
Deep learning, machine learning and artificial intelligence
Data fusion (multi-source satellite data, in situ data, crowd sourcing , mobile sensors, or other ground sensors)
Monitoring and assessment of land degradation and restoration in drylands
Unmanned aerial system (UAS)-based applications

Published Papers (6 papers)

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Research

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19 pages, 2966 KiB

Open AccessArticle

Drone-Based Remote Sensing for Research on Wind Erosion in Drylands: Possible Applications

by Junzhe Zhang, Wei Guo, Bo Zhou and Gregory S. Okin

Remote Sens. 2021, 13(2), 283; https://doi.org/10.3390/rs13020283 - 15 Jan 2021

Cited by 13 | Viewed by 3095

Abstract

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements. Full article

(This article belongs to the Special Issue Remote Sensing of Dryland Environment)

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17 pages, 2993 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach

by Gohar Ghazaryan, Simon König, Ehsan Eyshi Rezaei, Stefan Siebert and Olena Dubovyk

Remote Sens. 2020, 12(24), 4030; https://doi.org/10.3390/rs12244030 - 09 Dec 2020

Cited by 11 | Viewed by 4333

Abstract

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks. Full article

(This article belongs to the Special Issue Remote Sensing of Dryland Environment)

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20 pages, 7909 KiB

Open AccessArticle

Extraction of Yardang Characteristics Using Object-Based Image Analysis and Canny Edge Detection Methods

by Weitao Yuan, Wangle Zhang, Zhongping Lai and Jingxiong Zhang

Remote Sens. 2020, 12(4), 726; https://doi.org/10.3390/rs12040726 - 22 Feb 2020

Cited by 11 | Viewed by 3843

Abstract

Parameters of geomorphological characteristics are critical for research on yardangs. However, methods which are low-cost, accurate, and automatic or semi-automatic for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R² = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R² = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23% with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138). Full article

(This article belongs to the Special Issue Remote Sensing of Dryland Environment)

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17 pages, 4222 KiB

Open AccessArticle

Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony

by Neal J. Pastick, Devendra Dahal, Bruce K. Wylie, Sujan Parajuli, Stephen P. Boyte and Zhouting Wu

Remote Sens. 2020, 12(4), 725; https://doi.org/10.3390/rs12040725 - 22 Feb 2020

Cited by 30 | Viewed by 6366

Abstract

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers. Full article

(This article belongs to the Special Issue Remote Sensing of Dryland Environment)

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17 pages, 6218 KiB

Open AccessArticle

Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval

by Cuicui Ji, Xiaosong Li, Huaidong Wei and Sike Li

Remote Sens. 2020, 12(1), 115; https://doi.org/10.3390/rs12010115 - 01 Jan 2020

Cited by 27 | Viewed by 3279

Abstract

It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (f_PV and f_NPV) using multispectral satellite sensors because estimations of f_PV and f_NPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10–30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract f_PV and f_NPV through the novel method of spectral-mixture analysis. Full article

(This article belongs to the Special Issue Remote Sensing of Dryland Environment)

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Review

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32 pages, 2426 KiB

Open AccessReview

A Review of Terrestrial Carbon Assessment Methods Using Geo-Spatial Technologies with Emphasis on Arid Lands

by Salem Issa, Basam Dahy, Taoufik Ksiksi and Nazmi Saleous

Remote Sens. 2020, 12(12), 2008; https://doi.org/10.3390/rs12122008 - 23 Jun 2020

Cited by 36 | Viewed by 6081

Abstract

Geo-spatial technologies (i.e., remote sensing (RS) and Geographic Information Systems (GIS)) offer the means to enable a rapid assessment of terrestrial carbon stock (CS) over large areas. The utilization of an integrated RS-GIS approach for above ground biomass (AGB) estimation and precision carbon management is a timely and cost-effective solution for implementing appropriate management strategies at a localized and regional scale. The current study reviews various RS-related techniques used in the CS assessment, with emphasis on arid lands, and provides insight into the associated challenges, opportunities and future trends. The study examines the traditional methods and highlights their limitations. It explores recent and developing techniques, and identifies the most significant RS variables in depicting biophysical predictors. It further demonstrates the usefulness of geo-spatial technologies for assessing terrestrial CS, especially in arid lands. RS of vegetation in these ecosystems is constrained by unique challenges specific to their environmental conditions, leading to high inaccuracies when applying biomass estimation techniques developed for other ecosystems. This study reviews and highlights advantages and limitations of the various techniques and sensors, including optical, RADAR and LiDAR, that have been extensively used to estimate AGB and assess CS with RS data. Other new methods are introduced and discussed as well. Finally, the study highpoints the need for further work to fill the gaps and overcome limitations in using these emerging techniques for precision carbon management. Geo-spatial technologies are shown to be a valuable tool for estimating carbon sequestered especially in difficult and remote areas such as arid land. Full article

(This article belongs to the Special Issue Remote Sensing of Dryland Environment)

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