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Remote Sensing
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Earth Observations for Ecosystem Resilience
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Earth Observations for Ecosystem Resilience

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 23178

Special Issue Editors

Dr. Stefanie Herrmann

E-Mail Website
Guest Editor
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA
Interests: drylands; human–environment relationships; land degradation; vegetation dynamics
Dr. Donald Falk

E-Mail Website
Guest Editor
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA
Interests: resilience ecology; ecological disturbance; forest dynamics; climate change ecology

Special Issue Information

Dear Colleagues,

As highlighted in recent issues of Remote Sensing and other journals, remote sensing has evolved as a tool of choice to monitor and assess social–ecological systems systematically, encompassing the natural and managed environment. The concept of social–ecological systems reflects the understanding that humans are an integral part of virtually all ecosystems: Human activity affects the biophysical environment and the biophysical environment conditions of human decision-making and action. Resilience describes the capacity of a system to change and adapt continually while remaining within critical thresholds or undergoing state change to adapt to new environmental space. Resilience is a central emergent property of social–ecological systems, describing the ways that systems respond to stress and adapt to change. Consequently, the extent to which natural or human systems will be resilient to current and future stresses, including the dominant role of climate change, will play a key role in their sustainability.

The aim of this Special Issue is to document the utility of Earth Observation tools and techniques for monitoring and evaluating the resilience of social–ecological systems. We invite articles at scales from local to global that explore remote sensing-based indicators of overall system resilient behavior, as well as the mechanisms and factors that contribute to resilience. We also welcome submissions that quantify ecosystem responses to stressors and disturbances such as drought, wildland fire, and disease and insect outbreaks, to illustrate the limits of resilience. We encourage a wide range of contributions from basic and theoretical research to applied research that can be used to inform policy and management decisions. Research that examines the complexity of social–ecological systems by addressing (a) the interplay among multiple parameters of resilience, (b) responses to multiple stressors, and (c) interactions across multiple scales is of particular interest.

Dr. Stefanie Herrmann
Dr. Donald Falk
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

  • Natural and anthropogenic disturbance processes 
  • Ecological recovery
  • Successional pathways
  • Climate–disturbance interactions
  • Landscape spatial patterns
  • Ecological thresholds
  • Alternative states
  • Cross scale interactions

Published Papers (5 papers)

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Research

28 pages, 16352 KiB  
Article
On the Use of Standardized Multi-Temporal Indices for Monitoring Disturbance and Ecosystem Moisture Stress across Multiple Earth Observation Systems in the Google Earth Engine
by Tyson L. Swetnam, Stephen R. Yool, Samapriya Roy and Donald A. Falk
Remote Sens. 2021, 13(8), 1448; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081448 - 08 Apr 2021
Cited by 4 | Viewed by 4586
Abstract
In this work we explore three methods for quantifying ecosystem vegetation responses spatially and temporally using Google’s Earth Engine, implementing an Ecosystem Moisture Stress Index (EMSI) to monitor vegetation health in agricultural, pastoral, and natural landscapes across the entire era of spaceborne remote [...] Read more.
In this work we explore three methods for quantifying ecosystem vegetation responses spatially and temporally using Google’s Earth Engine, implementing an Ecosystem Moisture Stress Index (EMSI) to monitor vegetation health in agricultural, pastoral, and natural landscapes across the entire era of spaceborne remote sensing. EMSI is the multitemporal standard (z) score of the Normalized Difference Vegetation Index (NDVI) given as I, for a pixel (x,y) at the observational period t. The EMSI is calculated as: zxyt = (Ixyt − µxyT)/σxyT, where the index value of the observational date (Ixyt) is subtracted from the mean (µxyT) of the same date or range of days in a reference time series of length T (in years), divided by the standard deviation (σxyT), during the same day or range of dates in the reference time series. EMSI exhibits high significance (z > |2.0 ± 1.98σ|) across all geographic locations and time periods examined. Our results provide an expanded basis for detection and monitoring: (i) ecosystem phenology and health; (ii) wildfire potential or burn severity; (iii) herbivory; (iv) changes in ecosystem resilience; and (v) change and intensity of land use practices. We provide the code and analysis tools as a research object, part of the findable, accessible, interoperable, reusable (FAIR) data principles. Full article
(This article belongs to the Special Issue Earth Observations for Ecosystem Resilience)
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19 pages, 7453 KiB  
Article
Assessing Vegetation Response to Multi-Scalar Drought across the Mojave, Sonoran, Chihuahuan Deserts and Apache Highlands in the Southwest United States
by Pratima Khatri-Chhetri, Sean M. Hendryx, Kyle A. Hartfield, Michael A. Crimmins, Willem J. D. van Leeuwen and Van R. Kane
Remote Sens. 2021, 13(6), 1103; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061103 - 14 Mar 2021
Cited by 6 | Viewed by 3309
Abstract
Understanding the patterns and relationships between vegetation productivity and climatic conditions is essential for predicting the future impacts of climate change. Climate change is altering precipitation patterns and increasing temperatures in the Southwest United States. The large-scale and long-term effects of these changes [...] Read more.
Understanding the patterns and relationships between vegetation productivity and climatic conditions is essential for predicting the future impacts of climate change. Climate change is altering precipitation patterns and increasing temperatures in the Southwest United States. The large-scale and long-term effects of these changes on vegetation productivity are not well understood. This study investigates the patterns and relationships between seasonal vegetation productivity, represented by Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Evapotranspiration Index (SPEI) across the Mojave, Sonoran, and Chihuahuan Deserts and the Apache Highlands of the Southwest United States over 16 years from 2000 to 2015. To examine the spatiotemporal gradient and response of vegetation productivity to dry and wet conditions, we evaluated the linear trend of different SPEI timescales and correlations between NDVI and SPEI. We found that all four ecoregions are experiencing more frequent and severe drought conditions in recent years as measured by negative SPEI trends and severe negative SPEI values. We found that changes in NDVI were more strongly correlated with winter rather than summer water availability. Investigating correlations by vegetation type across all four ecoregions, we found that grassland and shrubland productivity were more dependent on summer water availability whereas sparse vegetation and forest productivity were more dependent on winter water availability. Our results can inform resource management and enhance our understanding of vegetation vulnerability to climate change. Full article
(This article belongs to the Special Issue Earth Observations for Ecosystem Resilience)
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18 pages, 2052 KiB  
Article
FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019)
by Jennifer K. Balch, Lise A. St. Denis, Adam L. Mahood, Nathan P. Mietkiewicz, Travis M. Williams, Joe McGlinchy and Maxwell C. Cook
Remote Sens. 2020, 12(21), 3498; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213498 - 24 Oct 2020
Cited by 28 | Viewed by 5631
Abstract
Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and [...] Read more.
Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and temporal characteristics of fire, or the classic fire regime concept, we need to critically define fire events from remote sensing data. Events, fundamentally a geographic concept with delineated spatial and temporal boundaries around a specific phenomenon that is homogenous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe Fire Events Delineation (FIRED), an event-delineation algorithm, that has been used to derive fire events (N = 51,871) from the MODIS MCD64 burned area product for the coterminous US (CONUS) from January 2001 to May 2019. The optimized spatial and temporal parameters to cluster burned area pixels into events were an 11-day window and a 5-pixel (2315 m) distance, when optimized against 13,741 wildfire perimeters in the CONUS from the Monitoring Trends in Burn Severity record. The linear relationship between the size of individual FIRED and Monitoring Trends in Burn Severity (MTBS) events for the CONUS was strong (R2 = 0.92 for all events). Importantly, this algorithm is open-source and flexible, allowing the end user to modify the spatio-temporal threshold or even the underlying algorithm approach as they see fit. We expect the optimized criteria to vary across regions, based on regional distributions of fire event size and rate of spread. We describe the derived metrics provided in a new national database and how they can be used to better understand US fire regimes. The open, flexible FIRED algorithm could be utilized to derive events in any satellite product. We hope that this open science effort will help catalyze a community-driven, data-integration effort (termed OneFire) to build a more complete picture of fire. Full article
(This article belongs to the Special Issue Earth Observations for Ecosystem Resilience)
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25 pages, 7164 KiB  
Article
Extreme Climate Event and Its Impact on Landscape Resilience in Gobi Region of Mongolia
by Oyudari Vova, Martin Kappas, Tsolmon Renchin and Steven R. Fassnacht
Remote Sens. 2020, 12(18), 2881; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182881 - 05 Sep 2020
Cited by 4 | Viewed by 4110
Abstract
The dzud, a specific type of climate disaster in Mongolia, is responsible for serious environmental and economic damage. It is characterized by heavy snowfall and severe winter conditions, causing mass livestock deaths that occur through the following spring. These events substantially limit [...] Read more.
The dzud, a specific type of climate disaster in Mongolia, is responsible for serious environmental and economic damage. It is characterized by heavy snowfall and severe winter conditions, causing mass livestock deaths that occur through the following spring. These events substantially limit socioeconomic development in Mongolia. In this research, we conducted an analysis of several dzud events (2000, 2001, 2002, and 2010) to understand the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. The present paper also establishes how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and livestock data. We also correlated aAIZ, NDVI, and seasonal precipitation in the varied ecosystems of the study area. The results illustrate that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in humid areas. Another result is that snowy winters can cause a 10 to 20-day early peak in NDVI and a following increase in vegetation growth. During a drought year with dry winter conditions, the vegetation growth phase begins later due to water deficiency, which leads to weaker vegetation growth. Livestock loss and the reduction of grazing pressure play a crucial role in vegetation recovery after extreme climatic events in Mongolia. Full article
(This article belongs to the Special Issue Earth Observations for Ecosystem Resilience)
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21 pages, 16716 KiB  
Article
Uncovering Dryland Woody Dynamics Using Optical, Microwave, and Field Data—Prolonged Above-Average Rainfall Paradoxically Contributes to Woody Plant Die-Off in the Western Sahel
by Paulo N. Bernardino, Martin Brandt, Wanda De Keersmaecker, Stéphanie Horion, Rasmus Fensholt, Ilié Storms, Jean-Pierre Wigneron, Jan Verbesselt and Ben Somers
Remote Sens. 2020, 12(14), 2332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142332 - 21 Jul 2020
Cited by 11 | Viewed by 4181
Abstract
Dryland ecosystems are frequently struck by droughts. Yet, woody vegetation is often able to recover from mortality events once precipitation returns to pre-drought conditions. Climate change, however, may impact woody vegetation resilience due to more extreme and frequent droughts. Thus, better understanding how [...] Read more.
Dryland ecosystems are frequently struck by droughts. Yet, woody vegetation is often able to recover from mortality events once precipitation returns to pre-drought conditions. Climate change, however, may impact woody vegetation resilience due to more extreme and frequent droughts. Thus, better understanding how woody vegetation responds to drought events is essential. We used a phenology-based remote sensing approach coupled with field data to estimate the severity and recovery rates of a large scale die-off event that occurred in 2014–2015 in Senegal. Novel low (L-band) and high-frequency (Ku-band) passive microwave vegetation optical depth (VOD), and optical MODIS data, were used to estimate woody vegetation dynamics. The relative importance of soil, human-pressure, and before-drought vegetation dynamics influencing the woody vegetation response to the drought were assessed. The die-off in 2014–2015 represented the highest dry season VOD drop for the studied period (1989–2017), even though the 2014 drought was not as severe as the droughts in the 1980s and 1990s. The spatially explicit Die-off Severity Index derived in this study, at 500 m resolution, highlights woody plants mortality in the study area. Soil physical characteristics highly affected die-off severity and post-disturbance recovery, but pre-drought biomass accumulation (i.e., in areas that benefited from above-normal rainfall conditions before the 2014 drought) was the most important variable in explaining die-off severity. This study provides new evidence supporting a better understanding of the “greening Sahel”, suggesting that a sudden increase in woody vegetation biomass does not necessarily imply a stable ecosystem recovery from the droughts in the 1980s. Instead, prolonged above-normal rainfall conditions prior to a drought may result in the accumulation of woody biomass, creating the basis for potentially large-scale woody vegetation die-off events due to even moderate dry spells. Full article
(This article belongs to the Special Issue Earth Observations for Ecosystem Resilience)
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