Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery

Abstract

Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.83.

Mapping invasive Lupinus polyphyllus Lindl. in semi-natural grasslands using object-based analysis of UAV-borne images

“…Our findings confirm results from other studies, where area and perimeter were essential variables in discrimination models, e.g. species mapping in arid areas as demonstrated by Silver et al (2019).…”

“…Previous studies (Chabot et al, 2018;Silver et al, 2019) with OBIA have proven that textural information was useful. However, they utilised many more texture attributes than employed in our study.…”

Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images

“…Our findings confirm results from other studies, where area and perimeter were essential variables in discrimination models, e.g. species mapping in arid areas as demonstrated by Silver et al (2019). To reduce the computation complexity, only two texture parameters (angular second moment, inverse difference moment) were computed out of many existing parameters.…”

“…Segmentation and classification are the two main steps in OBIA (Silver et al 2019). The segmentation is the first step and by definition "it divides an image or any raster or point data into spatially continuous, disjoint and homogeneous regions, referred to as segments or image objects" (Blaschke et al 2014).…”

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

“…We illustrated a more focused comparison of the 5×5 window (which was taken as the best) with the 7×7 (Fig 5B) and 3×3 ( Fig 5C) window sizes of the entropy GLCM. The better similarity of 5×5 with 7×7 than with 3×3, irrespective of the predictor size, shows the need to use fairly large window sizes to reduce the effect of noise commonly encountered in small windows [87]. It should be noted that the comparison of windows can be conditioned to the GLCM statistics used.…”