Defining habitat covariates in camera-trap based occupancy studies

Abstract

In species-habitat association studies, both the type and spatial scale of habitat covariates need to match the ecology of the focal species. We assessed the potential of high-resolution satellite imagery for generating habitat covariates using camera-trapping data from Sabah, Malaysian Borneo, within an occupancy framework. We tested the predictive power of covariates generated from satellite imagery at different resolutions and extents (focal patch sizes, 10–500 m around sample points) on estimates of occupancy patterns of six small to medium sized mammal species/species groups. High-resolution land cover information had considerably more model support for small, patchily distributed habitat features, whereas it had no advantage for large, homogeneous habitat features. A comparison of different focal patch sizes including remote sensing data and an in-situ measure showed that patches with a 50-m radius had most support for the target species. Thus, high-resolution satellite imagery proved to be particularly useful in heterogeneous landscapes, and can be used as a surrogate for certain in-situ measures, reducing field effort in logistically challenging environments. Additionally, remote sensed data provide more flexibility in defining appropriate spatial scales, which we show to impact estimates of wildlife-habitat associations.

Quantifying mammal biodiversity co‐benefits in certified tropical forests

“…We further modelled the effect on occupancy of three habitat covariates ( Fig. 1) derived from 5-m resolution RapidEye images as described in Niedballa et al (2015): distance to water, distance to oil palm plantation and forest score. Access to water is a basic requirement of all mammals and often affects their distribution (Rondinini et al, 2011).…”

Habitat-Net: Segmentation of habitat images using deep learning

“…For ecologists, the canopy and understory habitat are important indicators of forest disturbance and, for some wildlife species, tracking these disturbances might be a warning signal of potential population declines. Furthermore, the habitat information can be combined with spatial statistics, such as species distribution models (SDMs) to assess species occurrence or abundance data (Niedballa et al , 2015). This allows researchers to determine habitat associations of little known species and the knowledge gained ecological about the species can be used for more effective conservation efforts.…”

Demarcation of Prime Farmland Protection Areas around a Metropolis Based on High-Resolution Satellite Imagery

“…The HRSI, better fused with hyperspectral data, can tremendously reduce the effort of in-situ measures25. When we select PFs from the automatically interpreted farmland information, its natural quality and its location quality should be both considered.…”

“…It has proven effective to integrate remote sensing (supervised classification) and geographic information system (GIS) techniques to zone PFPAs automatically22. Automatic interpretation of remote sensing imagery can provide rich geographical information of land use which can facilitate our understanding of its trends232425. Then, the land suitability method could be introduced to select superior farmland as PFs for the following GIS analysis, such as LESA method26, IGAS analysis27, and FLOWA model28.…”