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

Liu, Kai; Xia, Li; Shi, Xun; Wang, Shugong

doi:10.1672/06-91.1

Cited by 131 publications

(77 citation statements)

References 41 publications

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“…As a typical tropical-subtropical wetland ecological system in the Pearl River Estuary, Qi'ao Island is the largest conservation area for artificially restored mangroves in China, covering an area of approximately 700 ha [40,41]. Forests within this dynamic landscape are characterized by uneven-aged trees and high spatial variability.…”

Section: Study Areamentioning

confidence: 99%

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

et al. 2015

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Abstract:Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a "Weights" method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% OPEN ACCESSRemote Sens. 2015, 7 12193 lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation.

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Section: Study Areamentioning

confidence: 99%

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

et al. 2015

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“…A DEM was integrated with the spectral data after being resampled from 90 m to 30 m spatial resolution, to improve the visual discrimination between mangroves and other vegetation located in the study area and to exclude non-mangrove pixels that had similar spectral attributes with mangrove pixels, but were located above the elevation limiting line [26].…”

Section: Digital Elevation Modelmentioning

confidence: 99%

Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM

Alsaaideh¹,

Al-Hanbali²,

Tateishi³

et al. 2013

JGIS

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A regional map of mangrove forests was produced for six islands located in the southern part of Japan by integrating the spectral analyses of Landsat Enhanced Thematic Mapper plus (ETM+) images with a digital elevation model (DEM). Several attempts were applied to propose a reliable method, which can be used to map the distribution of mangrove forests at a regional scale. The methodology used in this study comprised of obtaining the difference between Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), band ratio 5/4, and band 5, from Landsat ETM+, and integrating them with the topographic information. The integration of spectral analyses with topographic data has clearly separated the mangrove forests from other vegetation. An accuracy assessment was carried out in order to check the accuracy of the results. High overall accuracy ranging from 89.3% to 93.6% was achieved, which increased the opportunity to use this methodology in other countries rich in mangrove forests.

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“…Collection of ground control points [29] and training polygons using global positioning system (GPS) helps in the geo-registration, classification and accuracy assessment [30]. Data of Landsat series has limitations due to the coarse spatial resolution has often resulted in the underestimation of mangrove areas at locations where the spatial coverage is relatively small and fragmented [29][30][31][32][33]. Complex tropical atmospheric conditions, high spatial variability further pose challenges in the selection of sensors and appropriate data analysis methods.…”

Section: Ancillary Datamentioning

confidence: 99%

Inventorying, Mapping and Monitoring of Mangroves towards Sustainable Management of West Coast, India

Setturu¹

2014

J Geophys Remote Sens

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Mangroves are one of the productive and highly adaptive ecosystems on the Earth, and provide invaluable services to the coastal communities. Ecological and sustainable management of the mangrove ecosystem requires crucial knowledge of variability and dynamics over a time and space. There are no reliable recent spatial extent estimates of mangroves or trend of coverage in West coast. The mapping from medium spatial resolution remote sensing data often leads to the underestimation of spatial extent. An attempt has been made to provide detailed information of mangroves species distribution using remote sensing data of high spatial resolution integrated with the other collateral field information through GIS. The coastline changes and the mangrove dynamics during 1989-2010 were assessed using supervised classifier technique provided the spatial distribution of mangrove species namely Rhizophora mucronata, Sonneratia caseolaris, Avicennia officinalis, Sonneratia alba, and Kandelia candel with classification good accuracy. Delineation of mangroves at species wise records based on extensive field data will be invaluable for appropriate management (e.g. plantation, eco-tourism) and conservation measures for estuaries of Central Western Ghats. Mangroves play pivotal role in providing vital ecosystem goods and services, but are under threat due to anthropogenic activities, affecting habitat for specialised fauna and food resources for humans, birds and fish. This necessitates interventions of regulatory authorities to partner with the local communities in the restoration and conservation of mangrove habitats.

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

Cited by 131 publications

References 41 publications

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM

Inventorying, Mapping and Monitoring of Mangroves towards Sustainable Management of West Coast, India

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