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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES)
Gathot Winarso
Remote Sensing Application Center LAPAN
Earth & Planetary Sciences

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MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA Gathot Winarso; Yenni Vetrita; Anang D. Purwanto; Nanin Anggraini; Soni Darmawan; Doddy M. Yuwono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (624.791 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2687

Abstract

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.
VALIDATION OF COCHLODINIUM POLYKRIKOIDES RED TIDE DETECTION USING SEAWIFS-DERIVED CHLOROPHYLL-A DATA WITH NFRDI RED TIDE MAP IN SOUTH EAST KOREAN WATERS Gathot Winarso; Joji Ishizaka
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 1 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (499.097 KB) | DOI: 10.30536/j.ijreses.2017.v14.a2627

Abstract

Annual summer red tides of Cochlodinium polykrikoides have happenned at southern coastal  of the South Korea, accounted economic losses of 76.4 billion won in 1995 on fisheries and other economic substantial losses. Therefore, it is important to eliminate the damage and losses by monitoring the bloom and to forecast their development and movement. On previous study, ocean color satellite, SeaWiFS, standard chlorophyll-a data was used to detect the red tide, using threshold value of chlorophyll-a concentration ≥ 5 mg/m3, resulted a good correlation using visual comparison. However, statistic based accuracy analysis has not be done yet. In this study, the accuracy of detection method was analyzed using spatial statistic. Spatial statistical match up analysis resulted 68% of red tide area was not presented in satellite data due to masking. Within red tide area where data existed, 36% was in high chlorophyll-a area and 64% was in low chlorophyll-a area. Within the high chlorophyll-a area 13% and 87% was in and out of the red tide area. It was found that the accuracy of this detection is low. However if the accuracy was yearly splitted, its found that 75% accuracy on 2002 where visually red tide detected spead out to the off-shore area. The fail and false detection are not due to the failure of the detection method but caused by limitation of the technology due to the natural condition i.e. type of red tide spreading, cloud cover and other flags such as turbid water, stray light etc.
Co-Authors Anang D. Purwanto Doddy M. Yuwono Joji Ishizaka Nanin Anggraini Soni Darmawan Yenni Vetrita