Projo Danoedoro

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.

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.

Comparison of scoring, matching, SMCE and geographically weighted regression In malaria vulnerability spatial modelling using satellite imagery: an Indonesian example

Malaria is one of deadly infectious diseases commonly found in tropical countries, and until now its preventive efforts are still going on. From a spatial-analytical perspective, the preventive efforts can be done by developing malaria vulnerability maps, which can be used as a basis for risk management. Remotely sensed imagery is a powerful source for collecting relevant spatial data for that purpose. Among various models, there are four analysis methods for generating such maps, i.e. scoring, matching, spatial multi-criteria evaluation (SMCE) and geographically weighted regression (GWR), which have been compared according to their effectiveness and accuracies. The authors tested those methods in Purworejo Regency, Central Java, Indonesia, which has been recognized as a malaria endemic area. This study used Landsat-8 OLI imagery as a basis for deriving spatial parameters closely related to malaria vulnerability . Each vulnerability spatial model’s accuracy was then evaluated by calculating the number of cases found in the field, with respect to each vulnerability class, and then compiling all values using cross tabulation. It was found that, among other methods, the SMCE-based malaria vulnerability map statistically delivered the best result.

Model of Peatland Vegetation Species using HyMap Image and Machine Learning

Species Tumih / Parepat (Combretocarpus-rotundatus Mig. Dancer) family Anisophylleaceae and Meranti (Shorea Belangerang, Shorea Teysmanniana Dyer ex Brandis) family Dipterocarpaceae is a group of vegetation species distribution model. Species pioneer is predicted as an indicator of the succession of ecosystem restoration of tropical peatland characteristics and extremely fragile (unique) in the endemic hot spot of Sundaland. Climate change projections and conservation planning are hot topics of current discussion, analysis of alternative approaches and the development of combinations of species projection modelling algorithms through geospatial information systems technology. Approach model to find out the research problem of vegetation level based on the machine learning hybrid method, wavelet and artificial neural networks. Field data are used as a reference collection of natural resource field sample objects and biodiversity assessment. The testing and training ANN data set iterations times 28, achieve a performance value of 0.0867 MSE value is smaller than the ANN training data, above 50%, and spectral accuracy 82.1 %. Identify the location of the sample point position of the Tumih / Parepat vegetation species using HyMap Image is good enough, at least the modelling, design of the species distribution can reach the target in this study. The computation validation rate above 90% proves the calculation can be considered.

Linear Spectral Mixture Analysis of SPOT-7 for Tea Yield Estimation in Pagilaran Estate, Batang Central Java

Remote sensing has been utilized especially for agriculture yield estimation. Tea yield is effected by biology characteristic including crown density. The challenge of tea yield estimation uses multispectral remote sensing data is the presence of object beside tea. This mixed pixel problem can disturb spectrally to recognize tea tree, so it is necessary to use pixel approach. The aims of this research are (1) to determine fraction of tea and non-tea; (2) to estimate crown density percentage based on tea Normalized Difference Vegetation Index (NDVI); (3) to estimate tea yield based on crown density. SPOT-7 was utilized for this application. Linear Spectral Mixture Analysis (LSMA) has applied to determination fraction percentage each pixel. Each pure endmember was read the NDVI value. NDVI of tea tree has sensitivity with crown density. Counting tea NDVI was applied for NDVI mixed pixel. Linear regression analysis has applied for estimating crown density and tea yield. The results of this research are SPOT -7 which can recognize tea, tree shade, impervious and soil each pixel with accuracy 99,84%. Although it produced high accuracy, it has overestimate at certain tea estate because of the attendance of impervious. Regression analysis of crown density and NDVI showed coeffisien determination 52%. This model result 4-100% crown density percentage, where crown density 4-55% were located beside tea tree or pruned-tea block. Regression analysis of crown density and tea yield relation showed coeffisien determination 45%. This model produced 161,34-1296,8 kg/ha. Each this model resulted Root Mean Square Error (RMSE) 14,27% and 551,52 kg/ha.

Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)

Developments of analysis remote sensing have same way with development of technology especially in sensor and plane. Now, a lot of image have high spatial and radiometric resolution, thats why a lot information. Vegetation object analysis such floristic composition got a lot advantage of that development. Floristic composition can be interpreted using a lot of method such pixel based classification and object based classification. The problems for pixel based method on high spatial resolution image are salt and paper who appear in result of classification. The purpose of this research are compare effectiveness between pixel based classification and object based classification for composition vegetation mapping on high resolution image Worldview-2. The results show that pixel based classification using majority 5×5 kernel windows give the highest accuracy between another classifications. The highest accuracy is 73.32% from image Worldview-2 are being radiometric corrected level surface reflectance, but for overall accuracy in every class, object based are the best between another methods. Reviewed from effectiveness aspect, pixel based are more effective then object based for vegetation composition mapping in Tidar forest.