Landslide detection using deep learning and object-based image analysis

Landslides - Recent landslide detection studies have focused on pixel-based deep learning (DL) approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on...     

Apple orchard phenology response to desiccation and temperature changes in Urmia Lake region

Agricultural production activities, such as those for various fruits and cereals, play a significant role in the local economy and food security of the Urmia Lake region. In particular, this region has thousands of hectares of apple orchards, which have an important socioeconomic impact on the life of people. Climate and land cover changes over the past several decades threaten the apple orchards phenology (AOP). Recent studies have emphasized the effect of temperature on plant phenology; however, they have overlooked the influence of land cover changes, such as Lake Desiccation, on plant phenology. Meanwhile, how climate change and Lake Desiccation will affect the AOP is still not very well understood. Therefore, in this study, we used the Enhanced Vegetation Index (EVI) extracted from remote sensing images acquired by the MODIS sensor spanning from 2003 to 2014, in order to extract the AOP events. Furthermore, we used a random forest regression (RFR) for analyzing the relationship between temperature changes/Lake Desiccation and AOP. The results revealed that EVI is a very useful tool for estimating the AOP with a mean root-mean-square error of 6.25 days. Moreover, there is a linear trend toward the early start of season in this region. The end of season (EOS) and the growing season length have also increased in the areas closer to the lake until 2008. This seems that the delayed EOS in the area closest to Urmia Lake has been associated with the lake microclimate since 2008.     

Object-based classification of hyperspectral data using Random Forest algorithm

This paper presents a new framework for object-based classification of high-resolution hyperspectral data. This multi-step framework is based on multi-resolution segmentation (MRS) and Random Forest classifier (RFC) algorithms. The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images. Given the high number of input features, an automatic method is needed for estimation of this parameter. Moreover, we used the Variable Importance (VI), one of the outputs of the RFC, to determine the importance of each image band. Then, based on this parameter and other required parameters, the image is segmented into some homogenous regions. Finally, the RFC is carried out based on the characteristics of segments for converting them into meaningful objects. The proposed method, as well as, the conventional pixel-based RFC and Support Vector Machine (SVM) method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics. These data were acquired by the HyMap, the Airborne Prism Experiment (APEX), and the Compact Airborne Spectrographic Imager (CASI) hyperspectral sensors. The experimental results show that the proposed method is more consistent for land cover mapping in various areas. The overall classification accuracy (OA), obtained by the proposed method was 95.48, 86.57, and 84.29% for the HyMap, the APEX, and the CASI data-sets, respectively. Moreover, this method showed better efficiency in comparison to the spectral-based classifications because the OAs of the proposed method was 5.67 and 3.75% higher than the conventional RFC and SVM classifiers, respectively.