A post-classifier for mangrove mapping using ecological data

Vaiphasa, Chaichoke; Skidmore, Andrew K.; Boer, W.F. de

doi:10.1016/j.isprsjprs.2006.05.005

Cited by 65 publications

(52 citation statements)

References 24 publications

(28 reference statements)

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“…Mangrove is one of the most threatened and vulnerability ecosystems. Based on the importance and vulnerable of mangrove ecosystems faced, many studies on mangrove have been conducted to solve these issues in difference scales, long-term monitoring and detecting mangrove by using remote sensing techniques (Blasco et al, 2001;Everitt et al, 2008;Giri et al, 2007;Green, 1998;Seto & Fragkias, 2007;and Vaiphasa et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Mapping and Change Analysis in Mangrove Forest by Using Landsat Imagery

Dan

Chen

Chiang

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mangrove is located in the tropical and subtropical regions and brings good services for native people. Mangrove in the world has been lost with a rapid rate. Therefore, monitoring a spatiotemporal distribution of mangrove is thus critical for natural resource management. This research objectives were: (i) to map the current extent of mangrove in the West and Central Africa and in the Sundarbans delta, and (ii) to identify change of mangrove using Landsat data. The data were processed through four main steps: (1) data pre-processing including atmospheric correction and image normalization, (2) image classification using supervised classification approach, (3) accuracy assessment for the classification results, and (4) change detection analysis. Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa coefficient of 0.74 in the West and Central Africa and 83.0% and 0.73 in the Sundarbans, respectively. The result shows that mangrove areas have changed significantly. In the West and Central Africa, mangrove loss from 1988 to 2014 was approximately 16.9%, and only 2.5% was recovered or newly planted at the same time, while the overall change of mangrove in the Sundarbans increased approximately by 900 km 2 of total mangrove area. Mangrove declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove and the results could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in these regions.

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Section: Introductionmentioning

confidence: 99%

Mapping and Change Analysis in Mangrove Forest by Using Landsat Imagery

Dan

Chen

Chiang

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Recent advances in sensor technologies (e.g., IRS, IKONOS, and QuickBird) have enhanced the ability of RS programme to provide multi-resolution data. RS data are supplemented with different types of collateral vector data and environmental attributes and spectral information [27,28] for high precision mapping. Collection of ground control points [29] and training polygons using global positioning system (GPS) helps in the geo-registration, classification and accuracy assessment [30].…”

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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“…The field data were collected 8 months after the image acquisition. With 5 years of experience in the study area [11,21] the Pak Phanang mangroves are deemed to be unchanged over this period of 8 months as the composition of mangrove forests is generally resilient to natural interference [81]. A stratified random sampling method was used for locating the sampling plots.…”

Section: Field Data Collectionmentioning

confidence: 99%

“…Fast and accurate mapping is the key element for sustainable conservation of mangrove forests [20][21][22][23][24]. Earth Observation remote sensing has been recognized as a powerful tool for this purpose [23,[25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

Koedsin

Vaiphasa

2013

Remote Sensing

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Understanding the dynamics of mangroves at the species level is the key for securing sustainable conservation of mangrove forests around the globe. This study demonstrates the capability of the hyper-dimensional remote sensing data for discriminating diversely-populated tropical mangrove species. It was found that five different tropical mangrove species of Southern Thailand, including Avicennia alba, Avicennia marina, Bruguiera parviflora, Rhizophora apiculata, and Rhizophora mucronata, were correctly classified. The selected data treatment (a well-established spectral band selector) helped improve the overall accuracy from 86% to 92%, despite the remaining confusion between the two members of the Rhizophoraceae family and the pioneer species. It is therefore anticipated that the methodology presented in this study can be used as a practical guideline for detailed mangrove species mapping in other study areas. The next stage of this work will be to exploit the differences between the leaf textures of the two Rhizophoraceae mangroves in order to refine the classification outcome.

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A post-classifier for mangrove mapping using ecological data

Cited by 65 publications

References 24 publications

Mapping and Change Analysis in Mangrove Forest by Using Landsat Imagery

Mapping and Change Analysis in Mangrove Forest by Using Landsat Imagery

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

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

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