Shrimp culture is a sector of aquaculture that has a high potential for poverty alleviation and rural development in Vietnam. However, the development of this activity induces changes that potentially have negative impacts on the environment, one of which is wetland deterioration. This paper describes the use of a proposed change detection methodology in the assessment of mangrove forest alterations caused by aquaculture development, as well as the effectiveness of the measures taken to mitigate deforestation in the district of Giao Thuy, Vietnam, between 1986Vietnam, between , 1992Vietnam, between and 2001. Geometric and radiometric corrections were applied to Landsat images prior to identifying changes through comparison of unsupervised classifications. Changes were afterwards validated using a thresholding method based on Tasselled Cap feature image differencing and a rule-based feature selection matrix. The matrix is used to identify the feature that is most efficient at detecting the presence of change between given land-cover classes. The proposed approach aims to minimize commission errors in the postclassification change detection process. The results suggest that 63% of mangrove areas apparent in 1986 had been replaced by shrimp ponds in 2001. Between 1986 and 1992, 440 ha of adult mangrove trees had disappeared, whereas the mangrove extent increased by 441 ha between 1992 and 2001. This recovery is attributed to reforestation projects and conservation efforts that promoted natural regeneration.
Purpose
– Satellite and airborne images are increasingly used at different stages of disaster management, especially in the detection of infrastructure damage. Although semi- or full automatic techniques to detect damage have been proposed, they have not been used in emergency situations. Damage maps produced by international organisations are still based on visual interpretation of images, which is time- and labour-consuming. The purpose of this paper is to investigate how an automatic mapping of damage can be helpful for a first and rapid assessment of building damage.
Design/methodology/approach
– The study area is located in Port-au-Prince (Haiti) stricken by an earthquake in January 2010. To detect building damage, the paper uses optical images (15 cm of spatial resolution) coupled with height data (LiDAR, 1 m of spatial resolution). By undertaking an automatic object-oriented classification, the paper identifies three categories of building damages: intact buildings, collapsed buildings and debris.
Findings
– Data processing for the study area covering 11 km2 took about 15 hours. The accuracy of the classification varies from 70 to 79 per cent depending to the methods of assessment. Causes of errors are numerous: limited spectral information of the optical images, resolution difference between the two data, high density of buildings but most importantly, certain types of building collapses could not be detected by vertically taken images (the case of data in this study).
Originality/value
– The automatic damage mapping developed in this paper proves to be reliable and could be used in emergency situations. It could also be combined with manual visual interpretation to accelerate the planning of humanitarian rescues and reconstruction.
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