Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns

Laurent, Edward J.; Shi, Yanhong; Gatziolis, Demetrios; LeBouton, Joseph P.; Walters, Michael B.; Liu, Jianguo

doi:10.1016/j.rse.2005.04.015

Cited by 46 publications

(29 citation statements)

References 43 publications

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“…vegetation or canopy cover categories). Although classified maps are commonly used predictor variables, wildlife may respond to continuous environmental gradients that are not captured in the classification schemes (Laurent et al 2005). By using unclassified spectral data, species' occurrence can be predicted by spectrally detectable components of their habitat, rather than predetermined classification schemes that may inaccurately delineate boundaries between cover types and under-represent habitat heterogeneity (St-Louis et al 2006).…”

Section: Introductionmentioning

confidence: 99%

“…By using unclassified spectral data, species' occurrence can be predicted by spectrally detectable components of their habitat, rather than predetermined classification schemes that may inaccurately delineate boundaries between cover types and under-represent habitat heterogeneity (St-Louis et al 2006). Using unclassified spectral reflectance in the distribution model may minimize errors in the resulting predictive maps (Laurent et al 2005).…”

Section: Introductionmentioning

confidence: 99%

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Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Cleve¹,

Perrine

Holzman³

et al. 2011

Endang. Species. Res.

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The Sierra Nevada red fox Vulpes vulpes necator is listed as a threatened species under the California Endangered Species Act. It originally occurred throughout California's Cascade and Sierra Nevada mountain regions. Its current distribution is unknown but should be determined in order to guide management actions. We used occurrence data from the only known population, in the Lassen Peak region of northern California, combined with climatic and remotely sensed variables, to predict the species' potential distribution throughout its historic range. These model predictions can guide future surveys to locate additional fox populations. Moreover, they allow us to compare the relative performances of presence-absence (logistic regression) and presence-only (maximum entropy, or Maxent) modeling approaches using occurrence data with potential false absences and geographical biases. We also evaluated the recently revised Maxent algorithm that reduces the effect of geographically biased occurrence data by subsetting background pixels to match biases in the occurrence data. Within the Lassen Peak region, all models had good fit to the test data, with high values for the true skill statistic (76-83%), percent correctly classified (86-92%), and area under the curve (0.94-0.96), with Maxent models yielding slightly higher values. Outside the Lassen Peak region, the logistic regression model yielded the highest predictive performance, providing the closest match to the fox's historic range and also predicting a site where red foxes were subsequently detected in autumn 2010. Subsetting background pixels in Maxent reduced but did not eliminate the effect that geographically biased occurrence data had on prediction results relative to the Maxent model using full background pixels.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Cleve¹,

Perrine

Holzman³

et al. 2011

Endang. Species. Res.

View full text Add to dashboard Cite

show abstract

“…Laurent et al (2005), using rather simple ranking methods and Landsat 7 ETM+ images, obtained good results studding the distribution of three North American bird species. Such birds are associated to a very specific vegetation type, which can be spectrally recognized during the plant reproduction period.…”

Section: Discussionmentioning

confidence: 99%

“…However, satellite imagery shows the world in two dimensions. Thus, supplementary data of topography are needed in niche modeling (Laurent et al, 2005). The altitude data from SRTM (Shuttle Radar Topography Mission -NASA), in association with Landsat 7 images gives good results for studying habitat traits and species distribution (Turner et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

Modeling the spatial distribution of the endemic and threatened palm shrub Syagrus glaucescens (Arecaceae)

Miola

Freitas

Barbosa

et al. 2011

Neotropical Biology and Conservation

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This study tests the use of accessible geoprocessing techniques to identify and describe the distribution of Syagrus glaucescens, a threatened palm shrub endemic to rupestrian fields (high-altitude grasslands) of the southern Espinhaço Range, in Minas Gerais, Brazil. A hundred and ninety five occurrence points and 24 absence points of S. glaucescens were recorded on a GPS device during field trips. Free databases of eight abiotic variables for the surveyed area were also used. Ninety-eight of the occurrence points were used to generate a spectral signature in ArcView to identify variables best characterizing S. glaucescens' distribution area and produce a probability map. The species' distribution was strongly associated to rocky outcrops whose geological composition is Quartzite Filite and Conglomerates, above 1.000 m of altitude, with declivities of 5-30%. When the accuracy of the probability map was tested, 87.63% of the 97 occurrence points used fitted areas of very high occurrence probability on the map, while 100% of the 24 non-occurrence points matched areas of null probability; therefore confirming the high accuracy of the model in predicting the occurrence of S. glaucescens. Although the altitudinal grasslands of Espinhaço are areas of great biodiversity and high degree of plant species endemism, they remain poorly studied. Thus, this model can be a helpful tool in designing management and conservation strategies not only for S. glaucescens but also for other species associated to rocky outcrops of such environment.

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“…This index has shown to be either positively or negatively associated with NPP, depending on the scale (Waide et al 1999). Also, NDVI has been used in several studies for modeling species occurrence (Laurent et al 2005) or species richness at regional and local scales (Oindo & Skidmore 2002, Fairbanks & McGwire 2004. A further factor related to species richness is the structural and compositional complexity of habitats, which can be measured by either the variability of spectral indices (such as NDVI) or a band in the immediate neighborhood of each sampling unit, or using approaches that calculate spectral variability using multiple bands (Rocchini et al 2007, Oldeland et al 2010.…”

Section: Introductionmentioning

confidence: 99%

Determining Pleiades satellite data capability for tree diversity modeling

Akbari¹,

Kalbi²

2017

iForest

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Modeling of the spatial distribution of tree species based on survey data has recently been applied to conservation planning. Numerous methods have been developed for building species habitat suitability models. The aim of this study was to investigate the suitability of Pleiades satellite data for modeling tree species diversity of Hyrcanian forests in northern Iran (Mazandaran Province). One-hundred sample plots were established over an area of 2,600 ha and surveyed for tree diversity, and the Simpson's index (D), Shannon's index (H') and the reciprocal of Simpson's index (1/D) were calculated for each plot. Spectral variables and several parameters derived by texture analysis were obtained from multispectral images of the study area and used as predictors of tree diversity of sample plots. Two different methods, including generalized additive models (GAMs) and multivariate adaptive regression splines (MARS), were used for modeling. The results revealed a fairly good prediction of plot tree diversity obtained using the developed models (adj-R 2 = 0.542-0.731). Shannon's H' and Simpson's 1/D indices were more accurately predicted using GAMbased methods, while MARS models were more suitable for predicting Simpson's D. We concluded that Pleiades satellite data can be conveniently used for estimating, assessing and monitoring tree species diversity in the mixed hardwood Hyrcanian forest of northern Iran.

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Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns

Cited by 46 publications

References 43 publications

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

Modeling the spatial distribution of the endemic and threatened palm shrub Syagrus glaucescens (Arecaceae)

Determining Pleiades satellite data capability for tree diversity modeling

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