Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed, Taiwan

We developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m<sup>2</sup>. The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level

Yüksek uzaysal çözünürlüklü uydu görüntülerinin watershed kullanılarak çok ölçekli otomatik bölütlenmesi.

Useful information extraction from satellite images for the use of other higher level applications such as road network extraction and update, city planning etc. is a very important and active research area. It is seen that pixel-based techniques becomes insufficient for this task with increasing spatial resolution of satellite imaging sensors day by day. Therefore, the use of object-based techniques becomes indispensable and the segmentation method selection is very crucial for object-based techniques. In this thesis, various segmentation algorithms applied in remote sensing literature are presented and a segmentation process that is based on watersheds and multi-scale segmentation is proposed to use as the segmentation step of an object-based classifier. For every step of the proposed segmentation process, qualitative and quantitative comparisons with alternative approaches are done. The ones which provide best performance are incorporated into the proposed algorithm. Also, an unsupervised segmentation accuracy metric to determine all parameters of the algorithm is proposed. By this way, the proposed segmentation algorithm has become a fully automatic approach. Experiments that are done on a database formed with images taken from Google Earth® software provide promising results.M.S. - Master of Scienc

Assessment of the Predictive Reliability of a SWAT Flow Model and the Evaluation of Runoff Generation and BMP effectiveness in a Shale-Gas Impacted Watershed Using a Modeling Approach

In order to ensure a harmonious harness of shale-gas resources while ensuring minimal damage to the environment, it is imperative that studies to conduct to inform various aspects of managing the environment. This includes the development of reliable hydrologic models to inform in decisions concerning water and the environment. The first objective of this study was to evaluate the predictive reliability of the Soil and Water Assessment Tool (SWAT) model based on respective methods of LULC data classification and data type spatial resolution. Results showed that the high-resolution data classified with object-oriented image method does not provide any significant advantage in terms of the model\u27s flow predictive reliability. The second goal focused on an application of the object-oriented image analysis technique for change detection related to shale-gas infrastructure and subsequently evaluates the impact of shale-gas infrastructure on stream-flow in the South Fork of the Little Red River (SFLRR). Results showed that since the upsurge in shale-gas related activities in the Fayetteville Shale Play (between 2006 and 2010), shale-gas related infrastructure in the SFLRR have increased by 78% corresponding to a differential increase on storm water flow by approximately 10% over a projected period of simulation. The last objective deals with the evaluation of BMP effectiveness in a shale-gas watershed using a modeling approach. Three BMPs identified to control flow were introduced and simulated for a simulation (2000 to 2009) and projected (2010 to 2020) periods. The differences in the flow output at the watershed outlet for each BMP scenario were derived by comparing baseline and respective BMP scenarios. Results indicate that the BMPs have an average effectiveness of approximately 80% in reducing storm water flow attributable to shale-gas

Semantic segmentation of Brazilian Savanna vegetation using high spatial resolution satellite data and U-net

Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome. © Authors 2020. All rights reserved

Extraction of cartographic objects in high resolution satellite images for object model generation

The aim of this study is to detect man-made cartographic objects in high-resolution satellite images. New generation satellites offer a sub-metric spatial resolution, in which it is possible (and necessary) to develop methods at object level rather than at pixel level, and to exploit structural features of objects. With this aim, a method to generate structural object models from manually segmented images has been developed. To generate the model from non-segmented images, extraction of the objects from the sample images is required. A hybrid method of extraction (both in terms of input sources and segmentation algorithms) is proposed: A region based segmentation is applied on a 10 meter resolution multi-spectral image. The result is used as marker in a "marker-controlled watershed method using edges" on a 2.5 meter resolution panchromatic image. Very promising results have been obtained even on images where the limits of the target objects are not apparent

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment