Multiscale mapping of species diversity under changed land use using imaging spectroscopy

Abstract

Land use changes are one of the most important factors causing environmental transformations and species diversity alterations. The aim of the current study was to develop a geoinformatics-based framework to quantify alpha and beta diversity indices in two sites in Israel with different land uses, i.e., an agricultural system of fruit orchards, an afforestation system of planted groves, and an unmanaged system of groves. The framework comprises four scaling steps: (1) classification of a tree species distribution (SD) map using imaging spectroscopy (IS) at a pixel size of 1 m; (2) estimation of local species richness by calculating the alpha diversity index for 30-m grid cells; (3) calculation of beta diversity for different land use categories and sub-categories at different sizes; and (4) calculation of the beta diversity difference between the two sites. The SD was classified based on a hyperspectral image with 448 bands within the 380-2500 nm spectral range and a spatial resolution of 1 m. Twenty-three tree species were classified with high overall accuracy values of 82.57% and 86.93% for the two sites. Significantly high values of the alpha index characterize the unmanaged land use, and the lowest values were calculated for the agricultural land use. In addition, high values of alpha indices were found at the borders between the polygons related to the "edge-effect" phenomenon, whereas low alpha indices were found in areas with high invasion species rates. The beta index value, calculated for 58 polygons, was significantly lower in the agricultural land use. The suggested framework of this study succeeded in quantifying land use effects on tree species distribution, evenness, and richness. IS and spatial statistics techniques offer an opportunity to study woody plant species variation with a multiscale approach that is useful for managing land use, especially under increasing environmental changes.

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

“…The success of vegetation mapping by remote sensing is derived directly from the mapping objectives and the properties of the sensor [6][7][8]. Precise identification of individual species usually requires state-of-the-art methods, such as hyperspectral optical sensors with high spatial resolution [9][10][11], preferably combined with morphological and structural data such as LiDAR (Light Detection and Ranging) [12,13]. However, using airborne hyperspectral cutting edge sensors involves great difficulties when applied to species mapping over large areas, due to cost-effectiveness considerations and technical issues [1].…”

Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics

“…In the following sections, we describe the approach and methodology for mapping IPS based on multispectral imagery and studying the environmental and human factors that determined IPS spread in the landscape (Figure 3). Our approach included the use of species distribution maps developed by Paz-Kagan et al [35] to train the lower resolution of multi-spectral satellite data, in conjunction with IPS identification. Then, we developed IPS density maps for studying the environmental and human factor mediation their spreads.…”

“…We used two inputs: (1) WorldView-2 images with 2 m spatial resolution and eight spectral bands, acquired on 1 April, 2017, and covering the 100 km 2 study area. The spring season was selected because the Acacia flowering is at its peak; (2) The AisaFENIX airborne hyperspectral system was used by Paz-Kagan et al [35] to create species distribution maps of two sub-areas, the Dorot and Negba sites, totaling 2 km 2 . This system was characterized by continuous wavelengths, covering the visible, near infrared, and short-wave infrared regions (380-2500 nm) with 448 spectral bands.…”

“…The second stage included two consecutive steps: (1) identification of the focal IPS, using the species distribution maps of the two sub-areas that were based only on leaf spectral signals, and (2) IPS mapping based on the flowering signal from multispectral data over the full study area that was trained based on the first step. Paz-Kagan, et al [35], developed species maps that included overall 28 woody plant species in the two sub-areas. We used these species distribution maps for developing an IPS map by selecting the focal species class and integrating all the other classes to woody vegetation.…”

“…First, to develop an integrated approach for detecting and identifying Acacia by training data from hyper to multispectral sensors. The integration was achieved by using species distribution maps developed by Paz-Kagan et al [35], which is based on high spatial and spectral resolution airborne IS data. We used these maps to train lower resolution multispectral satellite data for detecting Acacia's flowering stage over a large spatial area.…”