This paper presents a decision-tree method for identifying mangroves in the Pearl River Estuary using multi-temporal Landsat TM data and ancillary GIS data. Remote sensing can be used to obtain mangrove distribution information. However, serious confusion in mangrove classification using conventional methods can develop because some types of land cover (e.g., agricultural land and forests) have similar spectral behaviors and distribution features to mangroves. This paper develops a decision- tree learning method for integrating Landsat TM data and ancillary GIS data (e.g., DEM and proximity variables) to solve this problem. The analysis has demonstrated that this approach can produce superior mangrove classification results to using only imagery or ancillary data. Three temporal maps of mangroves in the Pearl River Estuary were obtained using this decision-tree method. Monitoring results indicated a rapid decline of mangrove forest area in recent decades because of intensified human activities.

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Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning

Change detection study has been made for the mangrove forest of the Sunderbans (Bangladesh) using remote sensing and other ancillary data (1933–1987). At the advent of the British rule the forest was double their present extent. Its present area is about 6000.0 Sq. Km. The productive land area has been increased from 1960. Gewa (Excoecaria agallocha)‐Sundri (Heritiera fames) cover type areas have been increased at the expense of pure Sundri type. Height classes of the forest have been changed, basal area/ha has decreased. It is observed that there is a net decrease in Sundri standing volume of the order of 40% and that of Gewa 45% over the past 25 years (1960–1985). Total timber volume for all of the species has been reduced to near about a half. Timber volume/ha and basal area/ha for Gewa have increased in the Sharankhola Island of the forest. Sundri tress are being replaced by Gewa. CIR aerial photographs have been found most suitable for cover type analysis and other change detection study. Re...

Remote sensing for change detection in the Sunderbands, Bangladesh

Mapping land cover from remotely sensed imagery for input to hydrological models

(1987). The detection of vegetative stress utilizing remotely sensed data and soil sampling. Geocarto International: Vol. 2, No. 2, pp. 57-59.

M.S. Akhavi

Papers

The detection of vegetative stress utilizing remotely sensed data and soil sampling