Predicting species diversity in agricultural environments using Landsat TM imagery

Duro, Dennis C.; Girard, Jude; King, Douglas J.; Fahrig, Lenore; Mitchell, Scott; Lindsay, Kathryn; Tischendorf, Lutz

doi:10.1016/j.rse.2014.01.001

Cited by 48 publications

(44 citation statements)

References 96 publications

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“…In most ecological studies, it is usual to make the acquisition date of images consistent with the ground observations in order to avoid misinterpretations [14,17,52,53]. However, as showed by the results of this study, when NDVI is used as proxy to estimate species diversity at fine scale, this common sens rule must be revised since NDVI is time-dependent.…”

Section: Best Single-date Ndvi Is Not Synchronous With the Acquisitiomentioning

confidence: 96%

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Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Bonthoux

Lefèvre²,

Herrault

et al. 2018

Remote Sensing

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Abstract:Continuous-based predictors of habitat characteristics derived from satellite imagery are increasingly used in species distribution models (SDM). This is especially the case of Normalized Difference Vegetation Index (NDVI) which provides estimates of vegetation productivity and heterogeneity. However, when NDVI predictors are incorporated into SDM, synchrony between biological observations and image acquisition must be questionned. Due to seasonal variations of NDVI during the year, landscape patterns of habitats are revealed differently from one date to another leading to variations in models' performance. In this paper, we investigated the influence of acquisition time period of NDVI to explain and predict bird community patterns over France. We examined if the NDVI acquisition period that best fit the bird data depends on the dominant land cover context. We also compared models based on single time period of NDVI with one model built from the Dynamic Habitat Index (DHI) components which summarize variations in vegetation phenology throughout the year from the fraction of radiation absorbed by the canopy (fPAR). Bird species richness was calculated as response variable for 759 plots of 4 km 2 from the French Breeding Bird Survey. Bird specialists and generalists to habitat were considered. NDVI and DHI predictors were both derived from MODIS products. For NDVI, five time periods in 2010 were compared, from late winter to begin of autumn. A climate predictor was also used and Generalized Additive Models were fitted to explain and predict bird species richness. Results showed that NDVI-based proxies of dominant habitat identity and spatial heterogeneity explain more bird community patterns than DHI-based proxies of annual productivity and seasonnality. We also found that models' performance was both time and context-dependent, varying according to the bird groups. In general, best time period of NDVI did not match with the acquisition period of bird data because in case of synchrony, differences in habitats are less pronounced. These findings suggest that the most powerful approach to estimate bird community patterns is the simplest one. It only requires NDVI predictors from a single appropriate time period, in addition to climate, which makes the approach very operational.

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Section: Best Single-date Ndvi Is Not Synchronous With the Acquisitiomentioning

confidence: 96%

“…These indirect surrogates can explain and predict community patterns with equal or higher performances than those obtained with land cover maps [11,[13][14][15]. The normalized difference vegetation index (NDVI) which is calculated from near-infrared and red bands is widely used to reflect environmental factors [16].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Bonthoux

Lefèvre²,

Herrault

et al. 2018

Remote Sensing

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“…As an example, WorldView-2 data, and particularly a thresholding classifier using the Coastal band (400-450 nm), detected whales with up to 84.6% PA and 76.3% UA (Fretwell et al 2014). Instead, the most common way to estimate distribution of animal species, including mammals, birds, fishes, or invertebrates, is to model it based on proxies, such as spectral or structural properties (Suarez-Seoane et al 2002;Buchanan et al 2005;Vogeler et al 2014;Bejarano et al 2010;Mairota et al 2015), habitat suitability (Duro et al 2014;Yen et al 2012;Melin et al 2013), or detection of colonies (Fretwell and Trathan 2009;Fretwell et al 2012). Suarez-Seoane et al (2002) combined AVHRR with topographic and Geographic Information System (GIS) data to model the occurrence of three agricultural steppe birds in Spain, using PCA and Generalized Additive Models (GAM).…”

Section: Animal Speciesmentioning

confidence: 99%

“…Suarez-Seoane et al (2002) combined AVHRR with topographic and Geographic Information System (GIS) data to model the occurrence of three agricultural steppe birds in Spain, using PCA and Generalized Additive Models (GAM). Other studies included Landsat imagery, either individually (Buchanan et al 2005;Duro et al 2014) or in synergy with SAR data (Bergen et al 2007), to derive forest parameters and relate them with species distribution through linear regression. The fusion of LiDAR structure variables with spectral information appears beneficial for avian species distribution assessment (Vogeler et al 2014;Clawges et al 2008).…”

Section: Animal Speciesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…Carter, Knapp, Anderson, Hoch, & Smith, 2005;Duro et al, 2014;Levin, Shmida, Levanoni, Tamari, & Kark, 2007;Lucas & Carter, 2008). Here we employed the CV (Eq.…”

Section: Dry Forestmentioning

confidence: 99%

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

Somers

Asner

Martin

et al. 2015

Remote Sensing of Environment

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Predicting species diversity in agricultural environments using Landsat TM imagery

Cited by 48 publications

References 96 publications

Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

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