Water is one of the vital components of the Earth environment which needs to be frequently monitored. Satellite multispectral remote sensing image has been used over decades for water body extraction. Methodology of water body extraction can be summarized to three groups: feature extraction, supervised and unsupervised classification and data fusion. These methods, however, are of pure mathematical and statistical approach and little of them explore essential characteristics of multispectral image which is based on ground object radiance absorption behaviour in each sensing spectral bands. The spectral absorption characteristics of water body in visible and infrared bands differ very much from the other ground objects. They depend only on the used spectral bands and can be considered as invariant and sensor independent. In this paper the author proposed an application of spectral pattern analysis for water body extraction using spectral bands green, red, near infrared NIR and short wave infrared SWIR. The proposed algorithm has been used for water body extraction by Spot 5 and Landsat 5 TM images. Ground truth validation was carried out in Hanoi City. The advantage of this algorithm does not base on water body extraction only but it allows to asses also water quality. Different level of turbidity and organic matter contents could be classified by using additional index.

Broadleaf deciduous forests (BDFs) or dry dipterocarp forests play an important role in biodiversity conservation in tropical regions. Observations and classification of forest phenology provide valuable inputs for ecosystem models regarding its responses to climate change to assist forest management. Remotely sensed observations are often used to derive the parameters corresponding to seasonal vegetation dynamics. Data acquired from the Sentinel-1A satellite holds a great potential to improve forest type classification at a medium-large scale. This article presents an integrated object-based classification method by using Sentinel-1A and Landsat 8 OLI data acquired during different phenological periods (rainy and dry seasons). The deciduous forest and nondeciduous forest areas are classified by using NDVI (normalized difference vegetation index) from Landsat 8 cloud-free composite images taken during dry (from February to April) and rainy (from June to October) seasons. Shorea siamensis Miq. (S. siamensis), Shorea obtusa Wall. ex Blume (S. obtusa), and Dipterocarpus tuberculatus Roxb. (D. tuberculatus) in the deciduous forest area are classified based on the correlation between phenology of BDFs in Yok Don National Park and backscatter values of time-series Sentinel-1A imagery in deciduous forest areas. One hundred and five plots were selected during the field survey in the study area, consisting of dominant deciduous species, tree height, and canopy diameter. Thirty-nine plots were used for training to decide the broadleaf deciduous forest areas of the classified BDFs by the proposed method, and the other sixty-six plots were used for validation. Our proposed approach used the changes of backscatter in multitemporal SAR images to implement BDF classification mapping with acceptable accuracy. The overall accuracy of classification is about 79%, with a kappa coefficient of 0.7. Accurate classification and mapping of the BDFs using the proposed method can help authorities implement forest management in the future.

Mapping land cover using multi-temporal sentinel-1a data: A case study in Hanoi

The study evaluated arming of risk of lanslide in Hoa Binh and Son La reservoir hydropower area on the basis of analyzing high-resolution remote sensing and geographic information systems

About factors related to the occurrence of earthquakes in the Song Tranh 2 hydropower area in period 2011-2014

U-Pb zircon age of granitogneiss of the Dai Loc complex and their geological significances