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Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 40292

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Special Issue Editors

Dr. Todd Robinson

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Guest Editor

School of Earth and Planetary Sciences, Curtin University, GPO Box U 1987, Perth, WA 6845, Australia
Interests: remote sensing; spatial analysis and modelling; restoration; invasive species; lidar; rangeland ecology; condition monitoring; impact of mining; endemics
Special Issues, Collections and Topics in MDPI journals

Dr. Paul Nevill

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Guest Editor

School of Molecular and Life Sciences, Curtin University, GPO Box U 1987, Perth, WA 6845, Australia
Interests: molecular ecology; conservation and restoration of plant species; landscape genetics

Special Issue Information

Dear Colleagues,

In a world threatened with mass extinction, primarily caused by human activities, effective conservation and restoration is paramount. Analyses of remotely sensed imagery have the potential to assist in many ways. For example, monitoring programs can determine how ecosystems respond to groundwater depletion or the presence of pollutants; near real-time approaches can induce rapid response to critical events such as oil spills, clearing, and mortality; spatial modelling approaches can assist in predicting the potential of successful restoration in areas with highly conflicting land use objectives; and the success of post-mining restoration can be quantified more accurately and in a more timely manner using remote sensing based metrics than by ground-based observations alone.

In this Special Issue, we seek highly interdisciplinary approaches to conservation and restoration problems that can be solved, or solutions advanced, using remotely sensed data sources. Studies may be local in nature, but the methods should be portable (where possible) and the application novel and sophisticated. As the emphasis is on driving new approaches to solve conservation and restoration issues, review contributions are unlikely to be suitable.

Dr. Todd Robinson
Dr. Paul Nevill
Guest Editors

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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

Near real-time ecosystem monitoring
Refugia and protected areas
Conservation effectiveness
Anthropogenic disturbance
Rangeland monitoring and degradation
Biodiversity
Species distributions
Species modelling and movement
Restoration completion criteria

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Published Papers (11 papers)

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16 pages, 7300 KiB

Open AccessArticle

Continuous Land Cover Change Detection in a Critically Endangered Shrubland Ecosystem Using Neural Networks

by Glenn R. Moncrieff

Remote Sens. 2022, 14(12), 2766; https://doi.org/10.3390/rs14122766 - 9 Jun 2022

Cited by 6 | Viewed by 2983

Abstract

Existing efforts to continuously monitor land cover change using satellite image time series have mostly focused on forested ecosystems in the tropics and the Northern Hemisphere. The notable difference in spectral reflectance that occurs following deforestation allows land cover change to be detected with relative accuracy. Less progress has been made in detecting change in low productivity or disturbance-prone vegetation such as grasslands and shrublands where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5–10% of its original extent remaining in small, highly fragmented patches. I demonstrate that classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence and that trained models are suitable for operational continuous monitoring. A dataset of precisely dated vegetation change events between 2016 and 2021 was obtained from daily, high resolution Planet Labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously detect land cover change events in time series of vegetation activity from Sentinel 2 satellite data. The best model correctly identified 89% of land cover change events at the pixel-level, achieving a f-score of 0.93, a 79% improvement over the f-score of 0.52 achieved using a method designed for forested ecosystems based on trend analysis. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for continuous monitoring of habitat loss in non-forest ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated datasets of land cover change events with which to train machine-learning classifiers. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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17 pages, 4779 KiB

Open AccessArticle

Using Remote Sensing to Estimate Understorey Biomass in Semi-Arid Woodlands of South-Eastern Australia

by Linda Riquelme, David H. Duncan, Libby Rumpff and Peter Anton Vesk

Remote Sens. 2022, 14(10), 2358; https://doi.org/10.3390/rs14102358 - 13 May 2022

Cited by 1 | Viewed by 2203

Abstract

Monitoring ground layer biomass, and therefore forage availability, is important for managing large, vertebrate herbivore populations for conservation. Remote sensing allows for frequent observations over broad spatial scales, capturing changes in biomass over the landscape and through time. In this study, we explored different satellite-derived vegetation indices (VIs) for their utility in estimating understorey biomass in semi-arid woodlands of south-eastern Australia. Relationships between VIs and understorey biomass data have not been established in these particular semi-arid communities. Managers want to use forage availability to inform cull targets for western grey kangaroos (Macropus fuliginosus), to minimise the risk that browsing poses to regeneration in threatened woodland communities when grass biomass is low. We attempted to develop relationships between VIs and understorey biomass data collected over seven seasons across open and wooded vegetation types. Generalised Linear Mixed Models (GLMMs) were used to describe relationships between understorey biomass and VIs. Total understorey biomass (live and dead, all growth forms) was best described using the Tasselled Cap (TC) greenness index. The combined TC brightness and Modified Soil Adjusted Vegetation Index (MSAVI) ranked best for live understorey biomass (all growth forms), and grass (live and dead) biomass was best described by a combination of TC brightness and greenness indices. Models performed best for grass biomass, explaining 70% of variation in external validation when predicting to the same sites in a new season. However, we found empirical relationships were not transferrable to data collected from new sites. Including other variables (soil moisture, tree cover, and dominant understorey growth form) improved model performance when predicting to new sites. Anticipating a drop in forage availability is critical for the management of grazing pressure for woodland regeneration, however, predicting understorey biomass through space and time is a challenge. Whilst remotely sensed VIs are promising as an easily-available source of vegetation information, additional landscape-scale data are required before they can be considered a cost-efficient method of understorey biomass estimation in this semi-arid landscape. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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25 pages, 9141 KiB

Open AccessArticle

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon (Oncorhynchus tshawytscha) along the Sacramento River, California, Using Remote Sensing Imagery

by Francisco J. Bellido-Leiva, Robert A. Lusardi and Jay R. Lund

Remote Sens. 2022, 14(6), 1443; https://doi.org/10.3390/rs14061443 - 17 Mar 2022

Cited by 3 | Viewed by 2755

Abstract

Off-channel areas are one of the most impacted aquatic habitats by humans globally, as extensive agricultural and urban development has limited them to roughly 10% of historical extent. This is also true for California’s Sacramento River Valley, where historically frequent widespread inundation has been reduced to a few off-channel water bodies along the mid-Sacramento River. This remaining shallow-water habitat provides crucial ecological benefits to multiple avian and fish species, but especially to floodplain-adapted species such as Chinook salmon (Oncorhynchus tshawytscha). Characterizing spatiotemporal off-channel dynamics, including inundation extent and residence time, is fundamental to better understanding the intrinsic value of such habitats and their potential to support recovery actions. Remote sensing techniques have been increasingly used to map surface water at regional and local scales, with improved resolutions. As such, this study maps off-channel inundation areas and describes their temporal dynamics by analyzing pixel-based time- series of multiple water indices, modified Normalized Difference Water Index (mNDWI) and the Automated Water Extraction Index (AWEI), generated from LandSat-8 and Sentinel-2 data between 2013–2021. Quantified off-channel area was similar with each water index and method used, but improved performance was associated with Sentinel-2 products and AWEI index to identify wetted areas under lower mainstem discharges. Results indicate an uneven distribution of off-channel habitat in the study area, with limited inundated areas in upstream reaches (<16% of total off-channel area for greater flows). In addition, much less habitat exists for flows under 400 m³/s, an important migration cue for endangered winter-run Chinook salmon, limiting juvenile access to areas with enhanced rearing conditions. Off-channel habitat residence times averaged between 7 and 16 days, primarily defined by the rate of receding flows, with rapid flow recession providing marginal off-channel habitat. This study shows reasonable performance of moderate resolution LandSat-8 and Sentinel-2 remote sensing imagery to characterize shallow-water inundated habitat in higher-order rivers, and as a method to inform restoration and native fish recovery efforts. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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13 pages, 1443 KiB

Open AccessArticle

Landscape Structure of Woody Cover Patches for Endangered Ocelots in Southern Texas

by Jason V. Lombardi, Humberto L. Perotto-Baldivieso, Maksim Sergeyev, Amanda M. Veals, Landon Schofield, John H. Young and Michael E. Tewes

Remote Sens. 2021, 13(19), 4001; https://doi.org/10.3390/rs13194001 - 6 Oct 2021

Cited by 15 | Viewed by 3716

Abstract

Few ecological studies have explored landscape suitability using the gradient concept of landscape structure for wildlife species. Identification of conditions influencing the landscape ecology of endangered species allows for development of more robust recovery strategies. Our objectives were to (i) identify the range of landscape metrics (i.e., mean patch area; patch and edge densities; percent land cover; shape, aggregation, and largest patch indices) associated with woody vegetation used by ocelots (Leopardus pardalis), and (ii) quantify the potential distribution of suitable woody cover for ocelots across southern Texas. We used the gradient concept of landscape structure and the theory of slack combined with GPS telemetry data from 10 ocelots. Spatial distribution of high suitable woody cover is comprised of large patches, with low shape-index values (1.07–2.25), patch (27.21–72.50 patches/100 ha), and edge (0–191.50 m/ha) densities. High suitability landscape structure for ocelots occurs in 45.27% of woody cover in southern Texas. Our study demonstrates a new approach for measuring landscape suitability for ocelots in southern Texas. The range of landscape values identified that there are more large woody patches containing the spatial structure used by ocelots than previously suspected, which will aid in evaluating recovery and road planning efforts. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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15 pages, 3708 KiB

Open AccessArticle

Missing the Forest and the Trees: Utility, Limits and Caveats for Drone Imaging of Coastal Marine Ecosystems

by Leigh W. Tait, Shane Orchard and David R. Schiel

Remote Sens. 2021, 13(16), 3136; https://doi.org/10.3390/rs13163136 - 7 Aug 2021

Cited by 17 | Viewed by 4321

Abstract

Coastal marine ecosystems are under stress, yet actionable information about the cumulative effects of human impacts has eluded ecologists. Habitat-forming seaweeds in temperate regions provide myriad irreplaceable ecosystem services, but they are increasingly at risk of local and regional extinction from extreme climatic events and the cumulative impacts of land-use change and extractive activities. Informing appropriate management strategies to reduce the impacts of stressors requires comprehensive knowledge of species diversity, abundance and distributions. Remote sensing undoubtedly provides answers, but collecting imagery at appropriate resolution and spatial extent, and then accurately and precisely validating these datasets is not straightforward. Comprehensive and long-running monitoring of rocky reefs exist globally but are often limited to a small subset of reef platforms readily accessible to in-situ studies. Key vulnerable habitat-forming seaweeds are often not well-assessed by traditional in-situ methods, nor are they well-captured by passive remote sensing by satellites. Here we describe the utility of drone-based methods for monitoring and detecting key rocky intertidal habitat types, the limitations and caveats of these methods, and suggest a standardised workflow for achieving consistent results that will fulfil the needs of managers for conservation efforts. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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21 pages, 2737 KiB

Open AccessArticle

Progress in Grassland Cover Conservation in Southern European Mountains by 2020: A Transboundary Assessment in the Iberian Peninsula with Satellite Observations (2002–2019)

by Antonio T. Monteiro, Cláudia Carvalho-Santos, Richard Lucas, Jorge Rocha, Nuno Costa, Mariasilvia Giamberini, Eduarda Marques da Costa and Francesco Fava

Remote Sens. 2021, 13(15), 3019; https://doi.org/10.3390/rs13153019 - 1 Aug 2021

Cited by 5 | Viewed by 3111

Abstract

Conservation and policy agendas, such as the European Biodiversity strategy, Aichi biodiversity (target 5) and Common Agriculture Policy (CAP), are overlooking the progress made in mountain grassland cover conservation by 2020, which has significant socio-ecological implications to Europe. However, because the existing data near 2020 is scarce, the shifting character of mountain grasslands remains poorly characterized, and even less is known about the conservation outcomes because of different governance regimes and map uncertainty. Our study used Landsat satellite imagery over a transboundary mountain region in the northwestern Iberian Peninsula (Peneda-Gerês) to shed light on these aspects. Supervised classifications with a multiple classifier ensemble approach (MCE) were performed, with post classification comparison of maps established and bias-corrected to identify the trajectory in grassland cover, including protected and unprotected governance regimes. By analysing class-allocation (Shannon entropy), creating 95% confidence intervals for the area estimates, and evaluating the class-allocation thematic accuracy relationship, we characterized uncertainty in the findings. The bias-corrected estimates suggest that the positive progress claimed internationally by 2020 was not achieved. Our null hypothesis to declare a positive progress (at least equality in the proportion of grassland cover of 2019 and 2002) was rejected (X2 = 1972.1, df = 1, p < 0.001). The majority of grassland cover remained stable (67.1 ± 10.1 relative to 2002), but loss (−32.8 ± 7.1% relative to 2002 grasslands cover) overcame gain areas (+11.4 ± 6.6%), indicating net loss as the prevailing pattern over the transboundary study area (−21.4%). This feature prevailed at all extents of analysis (lowlands, −22.9%; mountains, −17.9%; mountains protected, −14.4%; mountains unprotected, −19.7%). The results also evidenced that mountain protected governance regimes experienced a lower decline in grassland extent compared to unprotected. Shannon entropy values were also significantly lower in correctly classified validation sites (z = −5.69, p = 0.0001, n = 708) suggesting a relationship between the quality of pixel assignment and thematic accuracy. We therefore encourage a post-2020 conservation and policy action to safeguard mountain grasslands by enhancing the role of protected governance regimes. To reduce uncertainty, grassland gain mapping requires additional remote sensing research to find the most adequate spatial and temporal data resolution to retrieve this process. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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22 pages, 13172 KiB

Open AccessArticle

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

by Francisco Argüello, Dora B. Heras, Alberto S. Garea and Pablo Quesada-Barriuso

Remote Sens. 2021, 13(14), 2687; https://doi.org/10.3390/rs13142687 - 8 Jul 2021

Cited by 8 | Viewed by 3014

Abstract

Watershed management is the study of the relevant characteristics of a watershed aimed at the use and sustainable management of forests, land, and water. Watersheds can be threatened by deforestation, uncontrolled logging, changes in farming systems, overgrazing, road and track construction, pollution, and invasion of exotic plants. This article describes a procedure to automatically monitor the river basins of Galicia, Spain, using five-band multispectral images taken by an unmanned aerial vehicle and several image processing algorithms. The objective is to determine the state of the vegetation, especially the identification of areas occupied by invasive species, as well as the detection of man-made structures that occupy the river basin using multispectral images. Since the territory to be studied occupies extensive areas and the resulting images are large, techniques and algorithms have been selected for fast execution and efficient use of computational resources. These techniques include superpixel segmentation and the use of advanced texture methods. For each one of the stages of the method (segmentation, texture codebook generation, feature extraction, and classification), different algorithms have been evaluated in terms of speed and accuracy for the identification of vegetation and natural and artificial structures in the Galician riversides. The experimental results show that the proposed approach can achieve this goal with speed and precision. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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20 pages, 3970 KiB

Open AccessArticle

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

by Hyeyeong Choe, Junhwa Chi and James H. Thorne

Remote Sens. 2021, 13(13), 2490; https://doi.org/10.3390/rs13132490 - 25 Jun 2021

Cited by 10 | Viewed by 5111

Abstract

The spatial patterns of species richness can be used as indicators for conservation and restoration, but data problems, including the lack of species surveys and geographical data gaps, are obstacles to mapping species richness across large areas. Lack of species data can be overcome with remote sensing because it covers extended geographic areas and generates recurring data. We developed a Deep Learning (DL) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) products and modeled potential species richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial patterns of potential plant species richness across the Korean Peninsula, including inaccessible North Korea, where survey data are limited?” First, we estimated plant species richness in South Korea by combining the probability-based SDM results of 1574 species and used independent plant surveys to validate our potential species richness maps. Next, DL-based species richness models were fitted to the species richness results in South Korea, and a time-series of the normalized difference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually developed models from South Korea were statistically tested using datasets that were not used in model training and obtained high accuracy outcomes (0.98, Pearson correlation). Finally, the proposed models were combined to estimate the richness patterns across the Korean Peninsula at a higher spatial resolution than the species survey data. From the statistical feature importance tests overall, growing season NDVI-related features were more important than LAI features for quantifying biodiversity from remote sensing time-series data. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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18 pages, 5104 KiB

Open AccessArticle

Monitoring of Vegetation Disturbance around Protected Areas in Central Tanzania Using Landsat Time-Series Data

by Atupelye W. Komba, Teiji Watanabe, Masami Kaneko and Mohan Bahadur Chand

Remote Sens. 2021, 13(9), 1800; https://doi.org/10.3390/rs13091800 - 5 May 2021

Cited by 13 | Viewed by 4252

Abstract

Understanding vegetation disturbance around protected areas (PAs) is critical as it significantly affects the sustainable conservation of wildlife. However, there is a lack of analyses of consistent long-term data on vegetation disturbance. In this study, the LandTrendr algorithm and Google Earth Engine were used to access satellite data and explore the vegetation dynamics history across the Ruaha–Rungwa landscape, Tanzania. We characterized vegetation disturbance patterns and change attributes, including disturbance occurrence trends, rate, and severity, by using each pixel’s normalized burn ratio index time series. Between 2000 and 2019, 36% of the vegetation was significantly disturbed by anthropogenic activities. The results of this study show that the disturbance trends, severity, and patterns are highly variable and strongly depend on the management approaches implemented in the heterogeneous landscape: Ruaha National Park (RNP), Rungwa–Kizigo–Muhesi Game Reserves (RKMGR), and the surrounding zones. The disturbance rates and severity were pronounced and increased toward the edges of the western RKMGR. However, the disturbance in the areas surrounding the RNP was lower. The characterization of the vegetation disturbance over time provides spatial information that is necessary for policy makers, managers, and conservationists to understand the ongoing long-term changes in large PAs. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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18 pages, 2449 KiB

Open AccessArticle

Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies

by Annie S. Guillaume, Kevin Leempoel, Estelle Rochat, Aude Rogivue, Michel Kasser, Felix Gugerli, Christian Parisod and Stéphane Joost

Remote Sens. 2021, 13(8), 1588; https://doi.org/10.3390/rs13081588 - 20 Apr 2021

Cited by 10 | Viewed by 4077

Abstract

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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12 pages, 3023 KiB

Open AccessTechnical Note

Fishing for Feral Cats in a Naturally Fragmented Rocky Landscape Using Movement Data

by Sandra D. Williamson, Richard van Dongen, Lewis Trotter, Russell Palmer and Todd P. Robinson

Remote Sens. 2021, 13(23), 4925; https://doi.org/10.3390/rs13234925 - 4 Dec 2021

Cited by 4 | Viewed by 2742

Abstract

Feral cats are one of the most damaging predators on Earth. They can be found throughout most of Australia’s mainland and many of its larger islands, where they are adaptable predators responsible for the decline and extinction of many species of native fauna. Managing feral cat populations to mitigate their impacts is a conservation priority. Control strategies can be better informed by knowledge of the locations that cats frequent the most. However, this information is rarely captured at the population level and therefore requires modelling based on observations of a sample of individuals. Here, we use movement data from collared feral cats to estimate home range sizes by gender and create species distribution models in the Pilbara bioregion of Western Australia. Home ranges were estimated using dynamic Brownian bridge movement models and split into 50% and 95% utilisation distribution contours. Species distribution models used points intersecting with the 50% utilisation contours and thinned by spacing points 500 m apart to remove sampling bias. Male cat home ranges were between 5 km² (50% utilisation) and 34 km² (95% utilisation), which were approximately twice the size of the female cats studied (2–17 km²). Species distribution modelling revealed a preference for low-lying riparian habitats with highly productive vegetation cover and a tendency to avoid newly burnt areas and topographically complex, rocky landscapes. Conservation management can benefit by targeting control effort in preferential habitat. Full article

(This article belongs to the Special Issue Utilising Remotely Sensed Imagery for Effective Conservation and Restoration Outcomes)

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