Pramaditya Wicaksono

Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing

ABSTRACT Among vegetated coastal habitats, mangrove forests are among the densest carbon pools. They store their organic carbon in the surrounding soil and thus the sequestered carbon stays in the sediment for a long time and cannot be easily returned to the atmosphere. Additionally, mangroves also provide various important ecosystem services in coastal areas and surroundings. Accordingly, it is important to understand the distribution of biomass carbon stock in mangrove habitats in a spatial and temporal context, not only to reduce CO2 concentrations in the atmosphere, but also for their sustainability. The objectives of this research are to map the mangrove carbon stock and estimate the total biomass carbon stock sheltered by mangrove forests, with the Karimunjawa Islands as a study site, using the widely available passive remote sensing system ALOS AVNIR-2. The modelling and mapping of mangrove carbon stock incorporates the integration of image pixel values and mangroves field data via empirical modelling. Vegetation indices and PC bands at different levels of radiometric corrections were all used as the input in the mangrove carbon stock modelling so that the effectiveness and sensitivity of different image transformations to particular radiometric correction levels could be analysed and understood. Afterward, the accuracy and effectiveness of each mangrove carbon stock-mapping routine was compared and evaluated. The accuracy of the best mangrove above-ground carbon stock (AGC) map modelled from vegetation index is 77.1% (EVI1, SE 5.89 kg C m−2), and for mangrove below-ground carbon stock (BGC) it is 60.0% (GEMI, SE 2.54 kg C m−2). The mangrove carbon stock map from ALOS AVNIR-2 PC bands showed a maximum accuracy of 77.8% (PC2, SE 5.71 kg C m−2) and 60.8% (PC2, SE 2.48 kg C m−2) for AGC and BGC respectively. From the resulting maps, the Karimunjawa Islands are estimated to shelter 96,482 tonnes C of mangroves AGC with a mean value of 21.64 kg C m−2 and 24,064 tonnes C of mangroves BGC with a mean value of 5.39 kg C m−2. Potentially, there are approximately 120,546 tonnes C of mangrove biomass carbon stock in the Karimunjawa Islands. Remote-sensing reflectance can successfully model mangrove carbon stock based on the relationship between mangrove canopy properties, represented by leaf area index (LAI) and the tree or root biomass carbon stock. The accuracy of the mangrove carbon stock map is subject to errors, which are sourced mainly from: (1) the absence of a species-specific biomass allometric equation for several species present in the study area; (2) the generalized standard conversion value of mangrove biomass to mangrove carbon stock; (3) the relationship between mangrove reflectance and mangrove LAI; (4) the relationship between mangrove reflectance and above-ground mangrove biomass and carbon stock due to its relationship with LAI; (5) the relationship between mangrove LAI and mangrove below-ground parts; (6) the inability to perform mangrove carbon stock modelling at the species level due to the complexities of the mangrove forest in the study area; (7) background reflectance and atmospheric path radiance that could not be completely minimized using image radiometric corrections and transformations; and (8) spatial displacement between the actual location of the mangrove forest in the field and the corresponding pixel in the image. The availability of mangrove biomass carbon stock maps is beneficial for carrying out various management activities, and is also very important for the resilience of mangroves to changing environments.

Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping

Abstract Information of seagrass LAI is still lacking in most parts of the world due to the high cost of comprehensive mapping. In this paper, we described the use of remote sensing as the cost and time effective solution to perform continuous seagrass LAI mapping, and discussed the issues and difficulties encountered during the mapping. ASTER VNIR and ALOS AVNIR-2 were used to perform the mapping. We proposed at life-form seagrass classification scheme to accommodate the low accuracy of at species level mapping. We also developed sampling mapping unit consist of several factors affecting the distribution of seagrass LAI. The results showed that sensor, method, and environmental limitation contribute to the low accuracy of seagrass LAI mapping.

Improving the accuracy of Multispectral-based benthic habitats mapping using image rotations: the application of Principle Component Analysis and Independent Component Analysis

Abstract The low number of water penetration bands in multispectral images limits the maximum descriptive resolution and the accuracy of the resulting benthic habitats maps, especially at higher levels of benthic habitats scheme complexities. This research aimed at improving the accuracy of benthic habitats mapping by exploiting the spectral performance of multispectral images using image rotation techniques, which is very beneficial for fast, accurate, and repeatable mapping. Kemujan Island as part of the Karimunjawa archipelago in Indonesia is selected as the study area. Principle Component Analysis (PCA) and Independent Component Analysis (ICA) were applied on Worldview-2 prior to image classification. The inputs for PCA and ICA are deglint bands and water column-corrected bands. Field benthic data collected from photo-transect technique were used to train the rotated datasets in the classification process and to assess the accuracy of the resulting benthic habitat maps. Three levels of benthic habitats classification schemes were constructed based on the variation of benthic habitats insitu, which covers the variations of coral reefs, seagrass, macro algae, and bare substratum. The results show that the application of image rotations on Worldview-2 improves the overall accuracy of benthic habitats mapping and become more effective as the classification scheme complexities increase. In the absence of water column correction, PCA and ICA become the best option to assist benthic habitats mapping.

Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image

Mangrove forest is an important ecosystem located in coastal area that provides various important ecological and economical services. One of the services provided by mangrove forest is the ability to act as carbon sink by sequestering CO2 from atmosphere through photosynthesis and carbon burial on the sediment. The carbon buried on mangrove sediment may persist for millennia before return to the atmosphere, and thus act as an effective long-term carbon sink. Therefore, it is important to understand the distribution of carbon stored within mangrove forest in a spatial and temporal context. In this paper, an effort to map carbon stocks in mangrove forest is presented using remote sensing technology to overcome the handicap encountered by field survey. In mangrove carbon stock mapping, the use of medium spatial resolution Landsat 7 ETM+ is emphasized. Landsat 7 ETM+ images are relatively cheap, widely available and have large area coverage, and thus provide a cost and time effective way of mapping mangrove carbon stocks. Using field data, two image processing techniques namely Vegetation Index and Linear Spectral Unmixing (LSU) were evaluated to find the best method to explain the variation in mangrove carbon stocks using remote sensing data. In addition, we also tried to estimate mangrove carbon sequestration rate via multitemporal analysis. Finally, the technique which produces significantly better result was used to produce a map of mangrove forest carbon stocks, which is spatially extensive and temporally repetitive.

Mangrove above-ground carbon stock mapping of multi-resolution passive remote-sensing systems

ABSTRACT This research address several issues related to mangrove above-ground carbon stock (AGC) mapping using the integration of remote-sensing images and field data. These issues are (1) remote-sensing image availability for specific mangrove AGC mapping scale and precision, (2) the impact on mangrove AGC modelling due to the difference between images spatial resolution and plot size of field mangrove AGC measurement, which follow the standardized procedure and not specially developed to be integrated with remote-sensing data, and (3) the accuracy of performing mangrove AGC mapping using image at different spatial resolutions using similar field size mangrove AGC data. Four multispectral data sets, namely Worldview-2, Advanced Land Observation System Advanced Visible and Near-Infrared Radiometer-2 (ALOS AVNIR-2), Advanced Spectral and Thermal Radiometer (ASTER) Visible Near-Infrared (VNIR) and Landsat 8 Operational Land Imager (OLI), and a Hyperion hyperspectral image were tested for their performance for mangrove AGC mapping. These images represent various spatial, spectral, and radiometric resolutions of remote-sensing data available to date. The mapping was performed using their original spatial resolution, and for Worldview-2 the mapping was also conducted using 10 m spatial resolution. Image radiometric corrections, vegetation indices, principal component analysis and minimum noise fraction were applied to each image. These were used as input in the empirical modelling of mangrove AGC. The results indicate that (1) it is not possible to perform empirical modelling of mangrove AGC using image with sub-canopy spatial resolution, (2) decreasing the spatial resolution may be beneficial to obtaining a significant correlation with mangrove AGC, and (3) it is possible to perform empirical modelling of mangrove AGC mapping using field data not specially intended to be integrated with remote-sensing data, along with some adjustments. This opens up the possibility of utilizing the available field mangrove AGC data collected by stakeholders, that is, government institutions, NGOs, academics, private sector, to assist mangrove AGC mapping across the nation.

Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment

ABSTRACT This paper presents the first assessment of PlanetScope image for benthic habitat and seagrass species mapping in optically shallow water. PlanetScope image is equipped with ideal resolutions for benthic habitat and seagrass mapping including high-spatial resolution (3 m), high radiometric resolution (12-bit), sufficient water penetration bands (Visible-Near-infrared) and very high temporal resolution (almost daily), which distinguishes it from other high spatial resolution images. It is necessary to assess the accuracy of this ideal system in a real-world benthic habitat and seagrass species mapping application. The optically shallow water of Karimunjawa Islands was selected as the study area. Two PlanetScope images acquired on 17 May 2017 and 15 August 2017 were tested as a control for the consistency of PlanetScope image accuracy. Several treatments were applied to both PlanetScope images including atmospheric correction, sunglint correction, Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and Linear Spectral Unmixing (LSU). Per-pixel classification algorithms (including Maximum Likelihood – ML, Support Vector Machine – SVM, and Classification Tree Analysis – CTA) and Object-based Image Analysis (OBIA) were used to perform benthic habitat and seagrass species classification. Spectra-based classifications were also applied to classify seagrass species using seagrass species spectra as input endmember. The results indicated that PlanetScope images produced 47.13–50.00% overall accuracy (OA) for benthic habitat mapping consist of five classes (coral reefs, macroalgae, seagrass, bare substratum, dead coral) and 74.03–74.31% OA for seagrass species mapping consist of five seagrass species classes. The accuracy of PlanetScope images for benthic habitat and seagrass species mapping was comparable to other high spatial resolution images. The performance of PlanetScope images was also consistent, shown by the similar accuracy obtained from May and August image. The concern regarding PlanetScope image was the low Signal-to-Noise Ratio (SNR) over homogeneous areas such as optically deep water, which led to the failure of performing sunglint correction and obtaining higher accuracy. To conclude, with the very high temporal resolution, PlanetScope image is promising for monitoring the dynamics and changes of benthic habitat and seagrass species composition, and rapid assessment of extreme events impacts, especially in coastal areas with limited accessibility.

Dark target effectiveness for dark-object subtraction atmospheric correction method on mangrove above-ground carbon stock mapping

One of the most effective atmospheric correction methods is dark-object subtraction (DOS) method, where the atmospheric offset can be generated from the image itself. The success of DOS strongly relies on the availability and quality of the dark target. Based on the response to the downwelling irradiances, the most effective dark target would be optically-deep water, which is not always available. It is important to assess the alternative dark targets in the absence of the ideal dark target. This research aimed at comparing the effectiveness of different dark targets for DOS method during mangrove above-ground carbon stock (AGC) mapping and comparing the accuracy with robust atmospheric correction FLAASH method. ALOS AVNIR-2 image was used as the test image, and mangrove forest of Karimunjawa and Kemujan Island was selected as the study area. The comparison covers the quality of healthy mangrove reflectance and the accuracy of vegetation indices for mangrove AGC mapping. The results of this research showed that non-ideal dark targets such as cloud-shadow pixels and the minimum value of the image can be used in the absence of ideal dark target, and DOS method is more efficient and effective than more robust atmospheric correction method.

Multispectral Resampling of Seagrass Species Spectra: WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI

Although spectrally different, seagrass species may not be able to be mapped from multispectral remote sensing images due to the limitation of their spectral resolution. Therefore, it is important to quantitatively assess the possibility of mapping seagrass species using multispectral images by resampling seagrass species spectra to multispectral bands. Seagrass species spectra were measured on harvested seagrass leaves. Spectral resolution of multispectral images used in this research was adopted from WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI. These images are widely available and can be a good representative and baseline for previous or future remote sensing images. Seagrass species considered in this research are Enhalus acoroides (Ea), Thalassodendron ciliatum (Tc), Thalassia hemprichii (Th), Cymodocea rotundata (Cr), Cymodocea serrulata (Cs), Halodule uninervis (Hu), Halodule pinifolia (Hp), Syringodum isoetifolium (Si), Halophila ovalis (Ho), and Halophila minor (Hm). Multispectral resampling analysis indicate that the resampled spectra exhibit similar shape and pattern with the original spectra but less precise, and they lose the unique absorption feature of seagrass species. Relying on spectral bands alone, multispectral image is not effective in mapping these seagrass species individually, which is shown by the poor and inconsistent result of Spectral Angle Mapper (SAM) classification technique in classifying seagrass species using seagrass species spectra as pure endmember. Only Sentinel-2A produced acceptable classification result using SAM.

Above Ground Carbon Stock Estimates of Mangrove Forest Using Worldview-2 Imagery in Teluk Benoa, Bali

Mangrove forests have a role as an absorbent and a carbon sink to a reduction CO2 in the atmosphere. Based on the previous studies found that mangrove forests have the ability to sequestering carbon through photosynthesis and carbon burial of sediment effectively. The value and distribution of carbon stock are important to understand through remote sensing technology. In this study, will estimate the carbon stock using WorldView-2 imagery with and without distinction mangrove species. Worldview-2 is a high resolution image with 2 meters spatial resolution and eight spectral bands. Worldview-2 potential to estimate carbon stock in detail. Vegetation indices such as DVI (Difference Vegetation Index), EVI (Enhanced Vegetation Index), and MRE-SR (Modified Red Edge-Simple Ratio) and field data were modeled to determine the best vegetation indices to estimate carbon stocks. Carbon stock estimated by allometric equation approach specific to each species of mangrove. Worldview-2 imagery to map mangrove species with an accuracy of 80.95%. Total carbon stock estimation results in the study area of 35.349,87 tons of dominant species Rhizophora apiculata, Rhizophora mucronata and Sonneratia alba.

Machine-learning regression for coral reef percentage cover mapping

Coral reef live percent cover (LPC) mapping has always been a challenging application for remote-sensing. The adoption of machine-learning algorithm in remote-sensing has opened-up the possibility of mapping coral reef at higher accuracy. This paper presents the application of machine-learning regression in the empirical modeling of coral reef LPC mapping. Stepwise regression, Support Vector Machine (SVM) regression, and Random Forest (RF) regression were used model the percentage of live coral cover in optically shallow water of Parang Island, Central Java, Indonesia using field photo-transect data to train the PlanetScope image. PlanetScope multispectral bands were transformed into water column corrected bands, Principle Component bands, and Cooccurrence texture analysis bands to be used as predictors in the regression process. The results indicate that the accuracy of machine learning algorithm to map coral reef LPC is relatively low due to the radiometric quality issue in the PlanetScope image (RMSE = 15.43%). We could not yet fairly justify the performance of machine learning algorithm until we applied the algorithms in other images.