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Special Issue "Earth Observations for Biodiversity and Ecosystems of Mediterranean-Type Climate Regions"

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 6305

Special Issue Editors

Dr. Emma C. Underwood
E-Mail Website
Guest Editor
Department of Environmental Science and Policy, University of California, Davis, Davis, CA, USA
Interests: application of geospatial tools to environmental decision making; areas of interest include ecosystem services; fire ecology; mediterranean-type ecosystems; species distribution modeling; invasive plant species; estimating conservation return on investment
Mr. Charlie Schrader-Patton
E-Mail Website
Guest Editor
USDA Forest Service Western Wildlands Environmental Threat Assessment Center, Prineville, OR, USA
Interests: remote sensing and GIS analysis of natural ecosystems—forests, rangelands; using geospatial tools and machine learning techniques to discern spatial and temporal patterns in natural ecosystems and how they are changing

Special Issue Information

Dear Colleagues,

Mediterranean-type climate regions are present in five areas of the world—the Cape Region of South Africa, southern California, central Chile, Southwest Australia, and the Mediterranean Basin. Characterized by warm dry summers and cool wet winters, these areas are known for high levels of biodiversity and provide valuable ecosystem services at local to global scales, including carbon storage, water runoff and recharge, erosion control, and recreation opportunities. However, despite their importance, they all experience stresses from rapid land-use change, urbanization, invasion of non-native species, increases in fire occurrence, and changing climates.

Mediterranean-type ecosystems have high spatial and temporal heterogeneity encompassing forests, shrublands, and annual and herbaceous perennial species: Diversity that is driven, in part, by natural disturbances such as fire. Remote sensing techniques provide an important contribution to our understanding of Mediterranean-type ecosystems and their dynamic nature, and contribute timely information to guide resource management. In this Special Issue, we illustrate how remote sensing can be used to classify vegetation of Mediterranean-type ecosystems, assess biomass and carbon storage, evaluate the recovery of vegetation post-fire, and monitor the success of restoration efforts to inform land management. In addition, we will highlight the use of geospatial techniques to monitor stresses including conversion from native shrubland to non-native grassland, expansion of urban areas into wildlands, and modification of species distributions associated with changing climates.


Dr. Emma C. Underwood
Mr. Charlie Schrader-Patton
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 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

  • Estimating biomass and carbon storage
  • Chaparral shrublands
  • Fire regimes and fire severity
  • Mapping changes in MTE communities
  • Predicting species distributions
  • Vegetation classification

Published Papers (5 papers)

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Article
A Camera-Based Method for Collecting Rapid Vegetation Data to Support Remote-Sensing Studies of Shrubland Biodiversity
Remote Sens. 2022, 14(8), 1933; https://doi.org/10.3390/rs14081933 - 16 Apr 2022
Viewed by 748
Abstract
The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions [...] Read more.
The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome. Full article
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Article
New Biomass Estimates for Chaparral-Dominated Southern California Landscapes
Remote Sens. 2021, 13(8), 1581; https://doi.org/10.3390/rs13081581 - 19 Apr 2021
Cited by 3 | Viewed by 816
Abstract
Chaparral shrublands are the dominant wildland vegetation type in Southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrublands [...] Read more.
Chaparral shrublands are the dominant wildland vegetation type in Southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrublands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict aboveground live biomass in Southern California using remote sensing data (Landsat Normalized Difference Vegetation Index (NDVI)) and a suite of geophysical variables. By substituting the NDVI and precipitation predictors for any given year, we were able to apply the model to each year from 2000 to 2019. Using a total of 980 field plots, our model had a k-fold cross-validation R2 of 0.51 and an RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE = 9.7) for Sierran mixed-conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers, we calculated the mean year 2010 shrubland biomasses for the four national forests that ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomasses from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measurements in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire. Full article
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Article
Assessing the Potential Replacement of Laurel Forest by a Novel Ecosystem in the Steep Terrain of an Oceanic Island
Remote Sens. 2020, 12(24), 4013; https://doi.org/10.3390/rs12244013 - 08 Dec 2020
Cited by 4 | Viewed by 2059
Abstract
Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes [...] Read more.
Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12–17% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images. Full article
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Article
Shrub Fractional Cover Estimation and Mapping of San Clemente Island Shrubland Based on Airborne Multispectral Imagery and Lidar Data
Remote Sens. 2020, 12(21), 3608; https://doi.org/10.3390/rs12213608 - 03 Nov 2020
Viewed by 814
Abstract
The purpose of this study is to map shrub distributions and estimate shrub cover fractions based on the classification of high-spatial-resolution aerial orthoimagery and light detection and ranging (LiDAR) data for portions of the highly disturbed coastal sage scrub landscapes of San Clemente [...] Read more.
The purpose of this study is to map shrub distributions and estimate shrub cover fractions based on the classification of high-spatial-resolution aerial orthoimagery and light detection and ranging (LiDAR) data for portions of the highly disturbed coastal sage scrub landscapes of San Clemente Island, California. We utilized nine multi-temporal aerial orthoimage sets for the 2010 to 2018 period to map shrub cover. Pixel-based and object-based image analysis (OBIA) approaches to image classification of growth forms were tested. Shrub fractional cover was estimated for 10, 20 and 40 m grid sizes and assessed for accuracy. The most accurate estimates of shrub cover were generated with the OBIA method with both multispectral brightness values and canopy height estimates from a normalized digital surface model (nDSM). Fractional cover products derived from 2015 and 2017 orthoimagery with nDSM data incorporated yielded the highest accuracies. Major factors that influenced the accuracy of shrub maps and fractional cover estimates include the time of year and spatial resolution of the imagery, the type of classifier, feature inputs to the classifier, and the grid size used for fractional cover estimation. While tracking actual changes in shrub cover over time was not the purpose, this study illustrates the importance of consistent mapping approaches and high-quality inputs, including very-high-spatial-resolution imagery and an nDSM. Full article
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Technical Note
Locating and Dating Land Cover Change Events in the Renosterveld, a Critically Endangered Shrubland Ecosystem
Remote Sens. 2021, 13(5), 834; https://doi.org/10.3390/rs13050834 - 24 Feb 2021
Cited by 2 | Viewed by 1334
Abstract
Land cover change is the leading cause of global biodiversity decline. New satellite platforms allow for monitoring of habitats in increasingly fine detail, but most applications have been limited to forested ecosystems. I demonstrate the potential for detailed mapping and accurate dating of [...] Read more.
Land cover change is the leading cause of global biodiversity decline. New satellite platforms allow for monitoring of habitats in increasingly fine detail, but most applications have been limited to forested ecosystems. I demonstrate the potential for detailed mapping and accurate dating of land cover change events in a highly biodiverse, Critically Endangered, shrubland ecosystem—the Renosterveld of South Africa. Using supervised classification of Sentinel 2 data, and subsequent manual verification with very high resolution imagery, I locate all conversion of Renosterveld to non-natural land cover between 2016 and 2020. Land cover change events are further assigned dates using high temporal frequency data from Planet labs. A total area of 478.6 hectares of Renosterveld loss was observed over this period, accounting for 0.72% of the remaining natural vegetation in the region. In total, 50% of change events were dated to within two weeks of their actual occurrence, and 87% to within two months. The Renosterveld loss identified here is almost entirely attributable to conversion of natural vegetation to cropland through ploughing. Change often preceded the planting and harvesting seasons of rainfed annual grains. These results show the potential for new satellite platforms to accurately map land cover change in non-forest ecosystems, and detect change within days of its occurrence. There is potential to use this and similar datasets to automate the process of change detection and monitor change continuously. Full article
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