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      Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

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          Abstract

          Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

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          Most cited references193

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          Object based image analysis for remote sensing

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            Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation.

            The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring, analyzing, and mapping temporal and spatial distributions of physiological and biophysical characteristics of vegetation. It is well documented that the NDVI approaches saturation asymptotically under conditions of moderate-to-high aboveground biomass. While reflectance in the red region (rho(red)) exhibits a nearly flat response once the leaf area index (LAI) exceeds 2, the near infrared (NIR) reflectance (PNIR) continue to respond significantly to changes in moderate-to-high vegetation density (LAI from 2 to 6) in crops. However, this higher sensitivity of the rho(NIR) has little effect on NDVI values once the rho(NIR) exceeds 30%. In this paper a simple modification of the NDVI was proposed. The Wide Dynamic Range Vegetation Index, WDRVI = (a * rho(NIR-rho(red))/(a * rho(NIR) + rho(red)), where the weighting coefficient a has a value of 0.1-0.2, increases correlation with vegetation fraction by linearizing the relationship for typical wheat, soybean, and maize canopies. The sensitivity of the WDRVI to moderate-to-high LAI (between 2 and 6) was at least three times greater than that of the NDVI. By enhancing the dynamic range while using the same bands as the NDVI, the WDRVI enables a more robust characterization of crop physiological and phenological characteristics. Although this index needs further evaluation, the linear relationship with vegetation fraction and much higher sensitivity to change in LAI will be especially valuable for precision agriculture and monitoring vegetation status under conditions of moderate-to-high density. It is anticipated that the new index will complement the NDVI and other vegetation indices that are based on the red and NIR spectral bands.
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              Estimation of tropical forest structural characteristics using large-footprint lidar

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                2010
                1 November 2010
                : 10
                : 11
                : 9647-9667
                Affiliations
                [1 ] Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK S7N 5C8, Canada; E-Mail: xulin.guo@ 123456usask.ca
                [2 ] Trent University, Peterborough, ON K9J 7B8, Canada; E-Mail: sfranklin@ 123456trentu.ca
                [3 ] Department of Veterinary Pathology, University of Saskatchewan, Saskatoon, SK S7N 5B4, Canada; E-Mail: marc.cattet@ 123456usask.ca
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: kai.wang@ 123456usask.ca ; Tel.: +1-306-9661488; Fax: +1-306-9665680.
                Article
                sensors-10-09647
                10.3390/s101109647
                3231003
                22163432
                2bdf2e12-779c-461c-9d27-dab5c9ca9c12
                © 2010 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/.)

                History
                : 19 September 2010
                : 14 October 2010
                : 28 October 2010
                Categories
                Review

                Biomedical engineering
                thermal infrared,integration of remote sensing (rs) and geographic information system (gis),small-satellite constellation,data fusion,image classification,lidar,ebc (ecology, biodiversity and conservation),remote sensing

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