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12
Review Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists
, 2010
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Article Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery
, 2014
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Remote Sensing
, 2010
"... Evaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to nam ..."
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Evaluating ecological patterns and processes is crucial for the conservation of ecosystems [1]. In this view, remote sensing is a powerful tool for monitoring their status and change. This involves several tasks like biodiversity estimate, landscape ecology, and species distribution modeling, to name a few [2]. Due to the difficulties associated with field-based data collection [3], the use of remote sensing for estimating ecological status and change is promising since it provides a synoptic view of an area with a high temporal resolution [4]. Of course in some cases remote sensing should be viewed as a help to plan a field survey rather than a replacement of it. Further, its improper use may lead to pitfalls and misleading results. This special issue “Ecological Status and Change by Remote Sensing ” is devoted to provide an almost complete overview of robust methods applied to ecological status and change estimate by remote sensing. The contributions published in this special issue cover most of the ecological fields of research involving remote sensing, in particular: (i) mapping vegetation, species distribution modeling and land use status and change; (ii) estimating environmental processes; (iii) developing landscape ecology metrics; (iv) assessing community biodiversity; and (v) estimating climatic parameters. The authors submitting their manuscript to this special issue of Remote Sensing are amongst the
Remote characte...
, 2013
"... Abstract: Airborne remote sensing has an important role to play in mapping and monitoring biodiversity over large spatial scales. Techniques for applying this technology to biodiversity mapping have focused on remote species identification of individual crowns; however, this requires collection of a ..."
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Abstract: Airborne remote sensing has an important role to play in mapping and monitoring biodiversity over large spatial scales. Techniques for applying this technology to biodiversity mapping have focused on remote species identification of individual crowns; however, this requires collection of a large number of crowns to train a classifier, which may limit the usefulness of this approach in many study regions. Based on the premise that the spectral variation among sites is related to their ecological dissimilarity, we asked whether it is possible to estimate the beta diversity, or turnover in species composition, among sites without the use of training data. We evaluated alternative methods using simulated communities constructed from the spectra of field-identified tree and shrub crowns from an African savanna. A method based on the k-means clustering of crown spectra produced beta diversity estimates (measured as Bray-Curtis dissimilarity) among sites with an average pairwise correlation of ~0.5 with the true beta diversity, compared to an average correlation of ~0.8 obtained by a supervised species classification approach. When applied to savanna landscapes, the unsupervised clustering method produced beta diversity estimates similar to those obtained from supervised
Open Access
"... Clusterin is a potential molecular predictor for ovarian cancer patient's survival: targeting Clusterin improves response to paclitaxel Hassan et al. ..."
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Clusterin is a potential molecular predictor for ovarian cancer patient's survival: targeting Clusterin improves response to paclitaxel Hassan et al.
www.mdpi.com/journal/remotesensing Article Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images
, 2010
"... Abstract: While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment c ..."
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Abstract: While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This research evaluates the use of high spatial resolution (IKONOS) and medium spatial resolution (Landsat ETM+) satellite imagery for assessing vegetation diversity in a dry tropical forest in central India. Contrary to expectations, across multiple measures of plant distribution and diversity, the resolution of IKONOS data is too fine for the purpose of plant diversity assessment and Landsat imagery performs better.
www.mdpi.com/journal/remotesensing Article Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data
, 2012
"... Abstract: Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Li ..."
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Abstract: Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76 % was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges—reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas—enables species mapping at
Article Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring
, 2014
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"... Entropy measured by the spatial variation of remotely sensed spectral signal may be a powerful proxy for species diversity at a number of spatial scales (Rocchini 2007). The entropy of the Earth’s surface is closely related to physical ..."
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Entropy measured by the spatial variation of remotely sensed spectral signal may be a powerful proxy for species diversity at a number of spatial scales (Rocchini 2007). The entropy of the Earth’s surface is closely related to physical
