Calculation of area, mapping and vulnerability assessment of a geomorphosite from GPS survey and high resolution Google Earth satellite image: a study in Mama Bhagne Pahar, Dubrajpur C. D. block, Birbhum district, West Bengal

Surface reflectance products obtained through the absolute atmospheric correction of multispectral satellite images are useful for precise scientific applications. For broader applications, the reflectance products computed using high-resolution images need to be validated with field measurement data. This study dealt with 2.2-m resolution Korea Multi-Purpose Satellite (KOMPSAT)-3A images with four multispectral bands, which were used to obtain top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The open-source Orfeo Toolbox (OTB) extension was used to generate these products. Next, these were subsequently validated by considering three sites (i.e., Railroad Valley Playa, NV, USA (RVUS), Baotou, China (BTCN), and La Crau, France (LCFR)) in RadCalNet, as well as a calibration and validation portal for remote sensing. We conducted the validations comparing satellite image-based reflectance products and field measurement reflectance based on data sets acquired at different times. The experimental results showed that the overall trend of validation accuracy of KOPSAT-3A was well fitted in all the RadCalNet sites and that the accuracy remained quite constant. Reflectance bands showing the minimum and maximum differences between the sets of experimental data are presented in this paper. The vegetation indices (i.e., the atmospherically resistant vegetation index (ARVI) and the structure insensitive pigment index (SIPI)) and three TOC reflectance bands obtained from KOMPSAT-3A were computed as a case study and used to achieve a detailed vegetation interpretation; finally, the correspondent results were compared with those obtained from Landsat-8 images (downloaded from the Google Earth Engine (GEE)). The validation and the application scheme presented in this study can be potentially applied to the generation of analysis ready data from high-resolution satellite sensor images.

Download Full-text

MAPPING OF HIGHLY HETEROGENEOUS URBAN STRUCTURE TYPE FOR FLOOD VULNERABILITY ASSESSMENT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w9-229-2018 ◽

2018 ◽

Vol XLII-4/W9 ◽

pp. 229-235

Author(s):

T. H. Tam ◽

M. Z. Abd Rahman ◽

S. Harun ◽

I. U. Kaoje

Keyword(s):

High Resolution ◽

Random Forest ◽

Vulnerability Assessment ◽

Satellite Image ◽

Structure Type ◽

Image Features ◽

Kappa Coefficient ◽

Urban Structure ◽

Flood Vulnerability ◽

Flood Vulnerability Assessment

<p><strong>Abstract.</strong> Vulnerability plays an important role in risk assessment. For flood vulnerability assessment, the map and characteristics of elements-at-risk at different scales are strongly required depending on the risk and vulnerability assessment requirements. This study proposes a methodology to classify urban structure type by combining object-based image classification and different high resolution remote sensing data. In this study, a high resolution satellite image and LiDAR have been acquired over Kota Bharu, Kelantan which consists of highly heterogeneous urban structure type (UST) classes. The first stage is data pre-processing that includes orthorectification and pansharpening of Geoeye satellite image, image resampling for normalised Digital Surface Model (nDSM) and followed by image segmentation for creating meaningful objects. The second stage comprises of derivation of image features, generation of training and testing datasets, and classification of UST. The classification was based on three types of machine learning classifiers, i.e. Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Tree (CART). The results obtained from the classification processes were compared using individual omission and commission error, overcall accuracy and Kappa coefficient. The results show that Random Forest classifier with all image features achieved the highest overall accuracy (93.5%) and Kappa coefficient (0.94). This is followed by CART classifier with overall accuracy of 93.7% and Kappa coefficient of 0.92. Finally, SVM classifier produced the lowest overall accuracy and Kappa coefficient with 88.6% and 0.86, respectively. The UST classification result can be further used to assist detailed building characterisation for large scale flood vulnerability assessment.</p>

Download Full-text

Study of the dynamic natural forest groups of Lebanese rice Cedrus libani A. Richard in Syria: دراسة ديناميكية المجموعات الحرجية الطبيعية للأرز اللبناني Cedrus libani A. Richard في سورية

Journal of engineering sciences and information technology - مجلة العلوم الهندسية و تكنولوجيا المعلومات ◽

10.26389/ajsrp.a090917 ◽

2017 ◽

Vol 1 (3) ◽

Author(s):

Abeer Ahmed Ibrahim

Keyword(s):

Health Status ◽

High Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Google Earth ◽

Landsat Images ◽

Cedrus Libani ◽

Ndvi Index ◽

Natural Stands

The aim of this study is to assess the dynamics of the forest stands of Cedrus libani A. Richard in its only natural area in Syria - Slenfeh and Jawbat Burghal. The spatial and temporal change of the natural stands of Cedrus libani during the period 1984-2011 and their health status during the period 1984-2014 were assessed using Remote Sensing and Geographic Information Systems (GIS). A high-resolution satellite image was used in 2011 and 17 Landsat images Landsat various sensors; Landsat_4, 5 and 8 and the NDVI Index were used during 1984-2014, high-resolution Google Earth (2 m). The direction and amount of the NDVI index of the Cedrus libani samples studied during the years of study were determined using ANOVA in the SPSS. The results showed a clear decrease in the Cedrus libani area size in both study sites Slenfeh and Jawbat Burghal in 2011 compared to 1984. The results also revealed a signiﬁcant increase trend of Normalized Difference Vegetation Index (NDVI) for natural stands of Cedrus libani in Slenfeh and Jawbat Burghal during 1984-2014, which reflects the good health status of the natural Cedar stands in Syria.

Download Full-text