The Discrete Representation of Continuously Moving Indeterminate Objects

AbstractTo incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM (1, 1) model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object's movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects

The potential of Indonesian mangrove forests for global climate change mitigation

Mangroves provide a wide range of ecosystem services, including nutrient cycling, soil formation, wood production, fish spawning grounds, ecotourism and carbon (C) storage¹. High rates of tree and plant growth, coupled with anaerobic, water-logged soils that slow decomposition, result in large long-term C storage. Given their global significance as large sinks of C, preventing mangrove loss would be an effective climate change adaptation and mitigation strategy. It has been reported that C stocks in the Indo-Pacific region contain on average 1,023 MgC ha⁻¹ (ref. 2). Here, we estimate that Indonesian mangrove C stocks are 1,083 ± 378 MgC ha⁻¹. Scaled up to the country-level mangrove extent of 2.9 Mha (ref. 3), Indonesia’s mangroves contained on average 3.14 PgC. In three decades Indonesia has lost 40% of its mangroves⁴, mainly as a result of aquaculture development⁵. This has resulted in annual emissions of 0.07–0.21 Pg CO₂e. Annual mangrove deforestation in Indonesia is only 6% of its total forest loss⁶; however, if this were halted, total emissions would be reduced by an amount equal to 10–31% of estimated annual emissions from land-use sectors at present. Conservation of carbon-rich mangroves in the Indonesian archipelago should be a high-priority component of strategies to mitigate climate change

Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Non peer reviewe

Advanced Land Cover Mapping of Tropical Peat Swamp Forest using Discrete Return Lidar

The ability to better understand tropical peat ecosystems for restoration and climate change mitigation is often hampered by the lack of availability accurate and detailed data on vegetation cover and hydrologys, which is typically only derived from detailed and high-resolution imaging or field-based measurements. The aims of this study were to explore the potential advantage of airborne discrete-return lidar for mapping of forest cover in peat swamp forests. We used 2.8 pulse.m-1 lidar and the associated 1-m DTM derived from an airborne platform. The lidar dataset fully covered a 120 thousand hectare protection forest in Central Kalimantan. We extracted maximum vegetation heights in 5-m grid resolution to allow detailed mapping of the forest. We followed forest definition from FAO for forest and non-forest classification. We found that lidar was able to capture detail variation of canopy height in highresolution, thus provide more accurate classification. A comparison with existing maps suggested that the lidar-derived vegetation map was more consistent in defining canopy structure of the vegetation, with small standard deviations of the mean height of each class.The study was supported by the Silva Carbon Program, USA. The lidar dataset was provided by the KFCP-AusAid project and the Indonesian Ministry of Environment and Forestry. Solichin Manuri was supported by Australian Award Scholarship

Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m2 also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m2, with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m2) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems

ADVANCED LAND COVER MAPPING OF TROPICAL PEAT SWAMP ECOSYSTEM USING AIRBORNE DISCRETE RETURN LIDAR

The ability to better understand tropical peat ecosystems for restoration and climate change mitigation is often hampered by the lack of availability accurate and detailed data on vegetation cover and hydrologys, which is typically only derived from detailed and high-resolution imaging or field-based measurements. The aims of this study were to explore the potential advantage of airborne discrete-return lidar for mapping of forest cover in peat swamp forests. We used 2.8 pulse.m-1 lidar and the associated 1-m DTM derived from an airborne platform. The lidar dataset fully covered a 120 thousand hectare protection forest in Central Kalimantan. We extracted maximum vegetation heights in 5-m grid resolution to allow detailed mapping of the forest. We followed forest definition from FAO for forest and non-forest classification. We found that lidar was able to capture detail variation of canopy height in high-resolution, thus provide more accurate classification. A comparison with existing maps suggested that the lidar-derived vegetation map was more consistent in defining canopy structure of the vegetation, with small standard deviations of the mean height of each class

A review of forest and tree plantation biomass equations in Indonesia

Key message: We compiled 2,458 biomass equations from 168 destructive sampling studies in Indonesia. Unpublished academic theses contributed the largest share of the biomass equations. The availability of the biomass equations was skewed to certain regions, forest types, and species. Further research is necessary to fill the data gaps in emission factors and to enhance the implementation of climate change mitigation projects and programs. Context: Locally derived allometric equations contribute to reducing the uncertainty in the estimation of biomass, which may be useful in the implementation of climate change mitigation projects and programs in the forestry sector. Many regional and global efforts are underway to compile allometric equations. Aims: The present study compiles the available allometric equations in Indonesia and evaluates their adequacy in estimating biomass in the different types of forest across the archipelago. Methods: A systematic survey of the scientific literature was conducted to compile the biomass equations, including ISI publications, national journals, conference proceedings, scientific reports, and academic theses. The data collected were overlaid on a land use/land cover map to assess the spatial distribution with respect to different regions and land cover types. The validation of the equations for selected forest types was carried out using independent destructive sampling data. Results: A total of 2,458 biomass equations from 168 destructive sampling studies were compiled. Unpublished academic theses contributed the majority of the biomass equations. Twenty-one habitat types and 65 species were studied in detail. Diameter was the most widely used single predictor in all allometric equations. The cumulative number of individual trees cut was 5,207. The islands of Java, Kalimantan, and Sumatra were the most studied, while other regions were underexplored or unexplored. More than half of the biomass equations were for just seven species. The majority of the studies were carried out in plantation forests and secondary forests, while primary forests remain largely understudied. Validation using independent data showed that the allometric models for peat swamp forest had lower error departure, while the models for lowland dipterocarp forest had higher error departure. Conclusion: Although biomass studies are a major research activity in Indonesia due to its high forest cover, the majority of such activities are limited to certain regions, forest types, and species. More research is required to cover underrepresented regions, forest types, particular growth forms, and very large tree diameter classes

Improved allometric equations for tree aboveground biomass estimation in tropical dipterocarp forests of Kalimantan, Indonesia

Background Currently, the common and feasible way to estimate the most accurate forest biomass requires ground measurements and allometric models. Previous studies have been conducted on allometric equations development for estimating tree aboveground biomass (AGB) of tropical dipterocarp forests (TDFs) in Kalimantan (Indonesian Borneo). However, before the use of existing equations, a validation for the selection of the best allometric equation is required to assess the model bias and precision. This study aims at evaluating the validity of local and pantropical equations; developing new allometric equations for estimating tree AGB in TDFs of Kalimantan; and validating the new equations using independent datasets. Methods We used 108 tree samples from destructive sampling to develop the allometric equations, with maximum tree diameter of 175 cm and another 109 samples from previous studies for validating our equations. We performed ordinary least squares linear regression to explore the relationship between the AGB and the predictor variables in the natural logarithmic form. Results This study found that most of the existing local equations tended to be biased and imprecise, with mean relative error and mean absolute relative error more than 0.1 and 0.3, respectively. We developed new allometric equations for tree AGB estimation in the TDFs of Kalimantan. Through a validation using an independent dataset, we found that our equations were reliable in estimating tree AGB in TDF. The pantropical equation, which includes tree diameter, wood density and total height as predictor variables performed only slightly worse than our new models. Conclusions Our equations improve the precision and reduce the bias of AGB estimates of TDFs. Local models developed from small samples tend to systematically bias. A validation of existing AGB models is essential before the use of the models

Tree biomass equations for tropical peat swamp forest ecosystems in Indonesia

To assist countries to reduce emissions from deforestation and forest degradation, the United Nations has introduced the REDD+. mechanism. This performance-based incentive mechanism requires accurate quantification of carbon stock and emissions. However