Disaster detection and damage estimation using satellite imagery and land-use information

2005 ◽  
Author(s):  
J. Rau ◽  
Liang-Chien Chen ◽  
C. Tseng ◽  
Dong-Hsiung Wu ◽  
Min-Huo Xie
Keyword(s):  
Land Use ◽  
Satellite Imagery ◽  
Damage Estimation

scholarly journals Evaluating Satellite Imagery‐Based Land Use Data for Describing Forestland Development in Western Washington

2009 ◽  
Vol 24 (4) ◽  
pp. 214-222 ◽  
Author(s):  
Jeffrey D. Kline ◽  
Alissa Moses ◽  
David Azuma ◽  
Andrew Gray
Keyword(s):  
Land Use ◽  
Satellite Imagery ◽  
Econometric Model ◽  
Landsat Imagery ◽  
Forest Inventory And Analysis ◽  
The Us ◽  
Western Washington ◽  
Us Census ◽  
The Impact ◽  
Density Development

Abstract Forestry professionals are concerned about how forestlands are affected by residential and other development. To address those concerns, researchers must find appropriate data with which to describe and evaluate rates and patterns of forestland development and the impact of development on the management of remaining forestlands. We examine land use data gathered from Landsat imagery for western Washington and evaluate its usefulness for characterizing low-density development of forestland. We evaluate the accuracy of the satellite imagery‐based land use classifications by comparing them with other data from US Forest Service's Forest Inventory and Analysis inventories and the US census. We then use the data to estimate an econometric model describing development as a function of socioeconomic and topographic factors and project future rates of development and forestland loss to 2020. We conclude by discussing how best to meet the land use data needs of researchers, forestry policymakers, and managers.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals Estimation of land-use changes on Kitulo Plateau, Tanzania, using satellite imagery

Oryx ◽  
1994 ◽  
Vol 28 (3) ◽  
pp. 173-182
Author(s):  
Jon C. Lovett ◽  
Erik Prins
Keyword(s):  
Land Use ◽  
Satellite Imagery ◽  
Vascular Plants ◽  
Satellite Images ◽  
Nature Reserve ◽  
Land Use Changes ◽  
Bare Soil ◽  
Digital Analysis ◽  
Threatened Plants ◽  
Strong Case

The Kitulo Plateau of southern Tanzania is a lava plateau covering 273 sq km at an altitude of over 2500 m. The vegetation is predominately grassland with more than 350 taxa of vascular plants, of which nearly 5 per cent are of restricted distribution. Although the plateau is extensive, much of it is now cultivated. Digital analysis of satellite images showed that at least 24 per cent of the plateau was bare soil, modified grassland or cultivation between 1973 and 1989. The botanical importance of the plateau and increase in cultivation make a strong case for the establishment of a nature reserve to protect its rare and threatened plants.

Sign in / Sign up

Export Citation Format

Share Document