Mangrove forests in many parts of the world are declining at an alarming ratedpossibly even more rapidly than inland tropical forests. The rate and causes of such changes are not known. The forests themselves are dynamic in nature and are undergoing constant changes due to both natural and anthropogenic forces. Our research objective was to monitor deforestation and degradation arising from both natural and anthropogenic forces. We analyzed multi-temporal satellite data from 1970s, 1990s, and 2000s using supervised classification approach. Our spatiotemporal analysis shows that despite having the highest population density in the world in its periphery, areal extent of the mangrove forest of the Sundarbans has not changed significantly (approximately 1.2%) in the last w25 years. The forest is however constantly changing due to erosion, aggradation, deforestation and mangrove rehabilitation programs. The net forest area increased by 1.4% from the 1970s to 1990 and decreased by 2.5% from 1990 to 2000. The change is insignificant in the context of classification errors and the dynamic nature of mangrove forests. This is an excellent example of the co-existence of humans with terrestrial and aquatic plant and animal life. The strong commitment of governments under various protection measures such as forest reserves, wildlife sanctuaries, national parks, and international designations, is believed to be responsible for keeping this forest relatively intact (at least in terms of area). While the measured net loss of mangrove forest is not that high, the change matrix shows that turnover due to erosion, aggradation, reforestation and deforestation was much greater than net change. The forest is under threat from natural and anthropogenic forces leading to forest degradation, primarily due to top-dying disease and over-exploitation of forest resources. 2007 Elsevier Ltd. All rights reserved.

Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000

Because shrimp culture in the Mekong Delta develops rapidly, it has negatively impacted the environment, socio-economics and natural resources. In particular, mangrove forests have been altered by the shrimp culture. The area of mangrove forests in the region has been reduced and this is seen especially in Tra Vinh province. The results obtained from GIS (Geography Information System) and RS (Remote Sensing) show the status of mangrove forests in Tra Vinh province in 1965, 1995 (Northeastern part of Tra Vinh Province) and 2001. In 1965, the area of mangrove forests was 21,221 ha making up 56% of total land-use, while in 2001 it was 12,797 ha making up 37% of total land-use. Also based on GIS analysis, over the 36 years (1965e2001), the total coverage of mangrove forests have decreased by 50% since 1965. However, the speed of mangrove forest destruction in the period from 1965 to 1995 was much less than that in the period from 1995 to 2001. The average annual reduction in mangrove forest coverage in the first period (1965e1995) was 0.2% whereas it was 13.1% in the later period (1995e2001). For the long time, mangrove deforestation has been caused by war, collection of firewood and clearing for agriculture, and recently, shrimp farming has significantly contributed rate of mangrove destruction. 2006 Elsevier Ltd. All rights reserved.

Status and changes of mangrove forest in Mekong Delta: Case study in Tra Vinh, Vietnam

Using both IKONOS and in situ LAI-2000 sensor data, a map of estimated LAI, based on NDVI, was created for the Agua Brava Lagoon, Mexican Pacific. The LAI values were then aggregated according to four classes; red mangrove (Rhizophora mangle), healthy white mangrove (Laguncularia racemosa), poor condition white mangrove and dead mangrove. Of the live mangrove, calculated at approximately 85% of the forest, mean LAI values of 2.49, 1.74 and 0.85 were determined for the red, healthy white and poor condition white mangrove, respectively. Excluding the dead areas, an overall estimated mangrove LAI value of 1.81 was ascertained for the 71 km2 of mapped mangrove forest. Although the results do suggest the technique as a very rapid and effective method for monitoring the condition of mangroves at the species level, potential limitations are also discussed.

Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific

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.

/pdf/monitoring-mangrove-forest-changes-using-remote-sensing-and-4fd9oqedrq.pdf

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

Given the alarming global rates of mangrove forest loss it is important that resource managers have access to updated information regarding both the extent and condition of their mangrove forests. Mexican mangroves in particular have been identified as experiencing an exceptional high annual rate of loss. However, conflicting studies, using remote sensing techniques, of the current state of many of these forests may be hindering all efforts to conserve and manage what remains. Focusing on one such system, the Teacapan–Agua Brava–Las Haciendas estuarine–mangrove complex of the Mexican Pacific, an attempt was made to develop a rapid method of mapping the current condition of the mangroves based on estimated LAI. Specifically, using an AccuPAR LP-80 Ceptometer, 300 indirect in situ LAI measurements were taken at various sites within the black mangrove (Avicennia germinans) dominated forests of the northern section of this system. From this sample, 225 measurements were then used to develop linear regression models based on their relationship with corresponding values derived from QuickBird very high resolution optical satellite data. Specifically, regression analyses of the in situ LAI with both the normalized difference vegetation index (NDVI) and the simple ration (SR) vegetation index revealed significant positive relationships [LAI versus NDVI (R2 = 0.63); LAI versus SR (R2 = 0.68)]. Moreover, using the remaining sample, further examination of standard errors and of an F test of the residual variances indicated little difference between the two models. Based on the NDVI model, a map of estimated mangrove LAI was then created. Excluding the dead mangrove areas (i.e. LAI = 0), which represented 40% of the total 30.4 km2 of mangrove area identified in the scene, a mean estimated LAI value of 2.71 was recorded. By grouping the healthy fringe mangrove with the healthy riverine mangrove and by grouping the dwarf mangrove together with the poor condition mangrove, mean estimated LAI values of 4.66 and 2.39 were calculated, respectively. Given that the former healthy group only represents 8% of the total mangrove area examined, it is concluded that this mangrove system, considered one of the most important of the Pacific coast of the Americas, is currently experiencing a considerable state of degradation. Furthermore, based on the results of this investigation it is suggested that this approach could provide resource managers and scientists alike with a very rapid and effective method for monitoring the state of remaining mangrove forests of the Mexican Pacific and, possibly, other areas of the tropics.

Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach

There have been major advances in Canada in the development and operation of airborne electro‐optical sensors for resource management. Systems developed include the laser‐based remote sensors for profiling and coastal mapping, and the multispectral array imagers for land and ocean monitoring. The paper describes the state‐of‐the‐art systems and their applications, and addresses their role in resource management.

S.M. Till

Papers

Airborne electro‐optical sensors for resource management