ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia

Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R2 = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R2 = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R2 = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R2 = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests

Model Effects on GLAS-Based Regional Estimates of Forest Biomass and Carbon

Ice, Cloud, and land Elevation Satellite (ICESat) / Geosciences Laser Altimeter System (GLAS) waveform data are used to estimate biomass and carbon on a 1.27 X 10(exp 6) square km study area in the Province of Quebec, Canada, below the tree line. The same input datasets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include non-stratified and stratified versions of a multiple linear model where either biomass or (biomass)(exp 0.5) serves as the dependent variable. The use of different models in Quebec introduces differences in Provincial dry biomass estimates of up to 0.35 G, with a range of 4.94 +/- 0.28 Gt to 5.29 +/-0.36 Gt. The differences among model estimates are statistically non-significant, however, and the results demonstrate the degree to which carbon estimates vary strictly as a function of the model used to estimate regional biomass. Results also indicate that GLAS measurements become problematic with respect to height and biomass retrievals in the boreal forest when biomass values fall below 20 t/ha and when GLAS 75th percentile heights fall below 7 m

Multi-Sensor Remote Sensing of Forest Dynamics in Central Siberia

The forested regions of Siberia, Russia are vast and contain about a quarter of the world's forests that have not experienced harvesting. However, many Siberian forests are facing twin pressures of rapidly changing climate and increasing timber harvest activity. Monitoring the dynamics and mapping the structural parameters of the forest is important for understanding the causes and consequences of changes observed in these areas. Because of the inaccessibility and large extent of this forest, remote sensing data can play an important role for observing forest state and change. In Central Siberia, multi-sensor remote sensing data have been used to monitor forest disturbances and to map above-ground biomass from the Sayan Mountains in the south to the taiga-tundra boundaries in the north. Radar images from the Shuttle Imaging Radar-C (SIR-C)/XSAR mission were used for forest biomass estimation in the Sayan Mountains. Radar images from the Japanese Earth Resources Satellite-1 (JERS-1), European Remote Sensing Satellite-1 (ERS-1) and Canada's RADARSAT-1, and data from ETM+ on-board Landsat-7 were used to characterize forest disturbances from logging, fire, and insect damage in Boguchany and Priangare areas

Comparison of IKONOS Derived Vegetation Index and LiDAR Derived Canopy Height Model for Grassland Management.

Forest encroachment is understood to be the main reason for prairie grassland decline across the United States. In Texas and Oklahoma, juniper has been highlighted as particularly opportunistic. This study assesses the usefulness of three remote sensing techniques to aid in locating the areas of juniper encroachment for the LBJ Grasslands in Decatur, Texas. An object based classification was performed in eCognition and final accuracy assessments placed the overall accuracy at 94%, a significant improvement over traditional pixel based methods. Image biomass was estimated using normalized difference vegetation index (NDVI) for 1 meter resolution IKONOS winter images. A high correlation between the sum of NDVI for tree objects and field tree biomass was determined where R = 0.72, suggesting NDVI sum of a tree area is plausible. However, issues with NDVI saturation and regression produced unrealistically high biomass estimates for large NDVI. Canopy height model (CHM) derived from 3-5m LiDAR data did not perform as well. LiDAR typically used for digital elevation model (DEM) production was acquired for the CHM and produced correlations of R = 0.26. This suggests an inability for this particular dataset to identify juniper trees. When points that registered a tree height where correlated with field values, an R = 0.5 was found, suggesting denser point spacing would be necessary for this type of LiDAR data. Further refining of the methods used in this study could yield such information as the amount of juniper tree for a given location, fuel loads for prescribed burns and better information for the best approach to remove the juniper and ultimately management juniper encroachment into grasslands

Using MODIS and GLAS Data to Develop Timber Volume Estimates in Central Siberia

The boreal forest is the Earth's largest terrestrial biome, covering some 12 million km2 and accounting for about one third of this planet's total forest area. Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. Ground based forest inventories, have much uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse and/or lacking. In addition, many of the forest inventories that do exist for Siberia are now a decade or more old. Thus, available forest inventories fail to capture the current conditions. Changes in forest structure in a particular forest-type and region can change significantly due to changing environment conditions, and natural and anthropogenic disturbance. Remote sensing methods can potentially overcome these problems. Multispectral sensors can be used to provide vegetation cover maps that show a timely and accurate geographic distribution of vegetation types rather than decade old ground based maps. Lidar sensors can be used to directly obtain measurements that can be used to derive critical forest structure information (e.g., height, density, and volume). These in turn can used to estimate biomass components using allometric equations without having to use out dated forest inventory. Finally, remote sensing data is ideally suited to provide a sampling basis for a rigorous statistical estimate of the variance and error bound on forest structure measures. In this study, new remote sensing methods were applied to develop estimates timber volume using NASA's MODerate resolution Imaging Spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) for a 10 deg x 10 deg area in central Siberia. Using MODIS and GLAS data, maps were produced for cover type and timber volume for 2003, and a realistic variance (error bound) for timber volume was calculated for the study area. In this 'study we used only GLAS footprints that had a slope value of less than 10 deg. This was done to avoid large errors due to the effect of slope on the GLAS models. The method requires the integration of new remote sensing methods with available ground studies of forest timber volume conducted in Russian forests. The results were compared to traditional ground forest inventory methods reported in the literature and to ground truth collected in the study area

Optical remote sensing for biomass estimation in the tropics: the case study of Uganda

This study investigates the capabilities and limitations of freely available optical satellite data at medium resolution to estimate aboveground biomass density of vegetation at national scales in the tropics, and compares this approach with existing methodologies to understand and quantify the sources of variability in the estimations. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset. As a result of this thesis, aboveground woody biomass for the year circa-2000 was mapped at national scale in Uganda at 30-m spatial resolution on the basis of Landsat ETM+ images, a national land cover dataset and field data using an object-oriented approach. A regression tree-based model (Random Forest) produced good results (cross-validated R² 0.81, RMSE 13 Mg/ha) when trained with a sufficient number of field plots representative of the vegetation variability. This study demonstrated that in certain contexts Landsat data can effectively spatialize field biomass measurements and produce accurate and detailed estimates of biomass distribution at national scale. This approach tended to provide conservative biomass estimates and its limitations were mainly related to the saturation of the optical signal at high biomass density and to the cloud cover. When compared with the Uganda national biomass dataset, the map produced in this study presented higher agreement than other five regional/global biomass maps. The comparative analysis showed strong disagreement between the products, with estimates of total biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change default values, and global land cover datasets strongly overestimated biomass stocks, while maps based on satellite data provided conservative estimates. The comparison of the maps predictions with field data confirmed the above findings