Synthetic Aperture Radar (SAR) images improve habitat suitability models

Context

The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors.

Objectives

The objective of this study was to evaluate the information provided by a synthetic aperture radar (SAR) image for landscape connectivity modeling compared to aerial photographs (APs).

Methods

We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance. More precisely, we compared how two resistance maps constructed using landscape and/or local metrics derived from AP or SAR imagery yield different connectivity values (based on graph theory), considering hedgerow networks and forest carabid beetle species as a model.

Results

We found that resistance maps using landscape and local metrics derived from SAR imagery improve landscape connectivity measures. The SAR model is the most informative, explaining 58% of the variance in forest carabid beetle abundance. This model calculates resistance values associated with homogeneous patches within hedgerows according to their suitability (canopy cover density and landscape grain) for the model species.

Conclusions

Our approach combines two important methods in landscape ecology: the construction of resistance maps and the use of buffers around sampling points to determine the importance of landscape factors. This study was carried out through an interdisciplinary approach involving remote sensing scientists and landscape ecologists. This study is a step forward in developing landscape metrics from satellites to monitor biodiversity.

     

Sentinel-2 images reveal functional biophysical heterogeneities in crop mosaics

Landscape Ecology - Identifying landscape structure and understanding its functions are crucial for biological control. However, the relationship between the crop mosaic phenological heterogeneity...     

Exploring subtle land use and land cover changes: a framework for future landscape studies

Land cover and land use changes can have a wide variety of ecological effects, including significant impacts on soils and water quality. In rural areas, even subtle changes in farming practices can affect landscape features and functions, and consequently the environment. Fine-scale analyses have to be performed to better understand the land cover change processes. At the same time, models of land cover change have to be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to propose and implement sustainable and efficient environmental policies. Future landscape studies can provide a framework to forecast how land use and land cover changes is likely to react differently to subtle changes. This paper proposes a four step framework to forecast landscape futures at fine scales by coupling scenarios and landscape modelling approaches. This methodology has been tested on two contrasting agricultural landscapes located in the United States and France, to identify possible landscape changes based on forecasting and backcasting agriculture intensification scenarios. Both examples demonstrate that relatively subtle land cover and land use changes can have a large impact on future landscapes. Results highlight how such subtle changes have to be considered in term of quantity, location, and frequency of land use and land cover to appropriately assess environmental impacts on water pollution (France) and soil erosion (US). The results highlight opportunities for improvements in landscape modelling.     

Multiscale ecological assessment of remote sensing images

In landscape ecology, the importance of map extent and resolution on the value of landscape indices is widely discussed, but the information content of the map, mostly derived from remote sensing images, is not. In this study, we sought (1) to understand the influence of changes in maps’ spatial and spectral resolution of agricultural landscape elements, taking hedgerow networks as a case study, and (2) to explore how predictions of species distribution might be affected by maps’ resolutions, taking two carabid species as a case study. To do so, we compared maps from different remote sensors, derived two landscape characterization variables from the maps related to patterns known to drive ecological processes, and analyzed their predictive power on biological data distribution to assess the information content of these maps. The results show that (1) the use of several methods, including landscape metrics, was useful to assess map validity; (2) the spatial resolution of satellite images is not the only important factor; changes in spectral resolution significantly alter maps; (3) the relevant definition of “hedgerow” to construct functional maps is species and process specific; thus the different maps are not either good or bad, but rather provide complementary information; (4) the more a species responds to network structure and over small areas, the less the different maps can be substitutable one to another.