Remotely sensed landscape heterogeneity as a rapid tool for assessing local biodiversity value in a highly modified New Zealand landscape

Abstract

Abstract. The widespread conversion of natural habitats to agricultural land has created a need to integrate intensively managed landscapes into conservation management priorities. However, there are no clearly defined methods for assessing the conservation value of managed landscapes at the local scale. We used remotely sensed landscape heterogeneity as a rapid practical tool for the assessment of local biodiversity value within a predominantly agricultural landscape in Canterbury, New Zealand. Bird diversity was highly significantly correlated with landscape heterogeneity, distance from rivers and the Christchurch central business district, altitude and average annual household income, indicating that remotely sensed landscape heterogeneity is a good predictor of local biodiversity patterns. We discuss the advantages and limitations of using geographic information systems to determine local areas of high conservation value.

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

“…Most of these characteristics represent conditions of environmental variability on a local scale. Comparable results have been reported for regional scales [24,36], showing a variable relative importance depending upon the scale [18]. According to Kolasa & Rollo [63], heterogeneity may be mostly explained by three components: (i) the environmental variability or range in which the different environmental conditions of the site are variable (number of habitat types); (ii) the spatial configuration of the different habitats of the site; and (iii) by the historical variation over time.…”

“…The results of this research did not allow us to confirm the spectral variation hypothesis on a local scale, even though Oldeland et al [56] suggested that the use of hyperspectral information should improve monitoring of species diversity. In this light, recent studies [43,44] have proven that the hyperspectral information from airborne sensors can significantly enhance the predictive power of models not only because of the spectral information but also because of the improvement provided by the spatial resolution [24,26,41,58,59]. Another relevant source of information could be an analysis of seasonal and annual variability that can provide an additional element in estimating biodiversity [24], particularly in deciduous forests like Monte Oscuro.…”

“…In this light, recent studies [43,44] have proven that the hyperspectral information from airborne sensors can significantly enhance the predictive power of models not only because of the spectral information but also because of the improvement provided by the spatial resolution [24,26,41,58,59]. Another relevant source of information could be an analysis of seasonal and annual variability that can provide an additional element in estimating biodiversity [24], particularly in deciduous forests like Monte Oscuro. Another possible way to improve the hyperspectral contribution could be a more refined methodology to optimize the information contained in the hyperspectral image; this might have contributed better to managing the complexity of the multidimensional structure which hyperspectral data possess [33].…”

“…On the one hand, a number of studies have associated the biodiversity of different sites with the information obtained by passive remote sensors [23][24][25][26][27], correlating biological diversity directly with spectral reflectance values [28], with different spectral vegetation indexes [19,25,[29][30][31], and different types of feature extraction like principal components analysis (PCA) [32] or minimum noise fraction (MNF) [26]. Taking this into account, it is expected that hyperspectral sensors, which have great ability to detect characteristics associated with the biochemical, physiological, and structural spectral variability of the vegetation in the electromagnetic spectrum, will provide valuable information for the evaluation of biodiversity [33,34].…”

The use of fuzzy plant species density to indicate the effects of land-cover changes on biodiversity

“…The area was chosen for method application since suitable data for the approach were available and since the location included protected areas (PAs) and non-protected areas. We consider the presence of PAs within the borders of the study area useful for the discussion of results, since today it is well recognized that successful biodiversity conservation can be achieved more effectively if the management plans of the PAs are integrated with those of the surrounding landscape matrix (Eigenbrod et al, 2008;Foody, 2008;Turner et al, 2008;Ewers et al, 2005;Vandewoestijne et al, 2005;Fahrig, 2003;Hannah et al, 2002).…”