196,172 research outputs found
Dr P. Erhard Schlund, O. F. M., Erlosüng, 1926 ; Vorantwortung, ibid
No full textRivière Jean. Dr P. Erhard Schlund, O. F. M., Erlosüng, 1926 ; Vorantwortung, ibid. In: Revue des Sciences Religieuses, tome 8, fascicule 4, 1928. pp. 635-636
Dr P. Erhard Schlund, O. F. M., Erlosüng, 1926 ; Vorantwortung, ibid
No full textRivière Jean. Dr P. Erhard Schlund, O. F. M., Erlosüng, 1926 ; Vorantwortung, ibid. In: Revue des Sciences Religieuses, tome 8, fascicule 4, 1928. pp. 635-636
Digital Terrain Models & elevation metrics from acquisition in 2022
No full textDigital Terrain Models (DTMs), Slope, Aspect, Topographic Position Index (TPI), curvature in profile and plan direction and their average, and flow accumulation derived from airborne LiDAR data acquired over the Bukit Duabelas, Batanghari regency and Harapan rainforest protected area, in Jambi, Sumatra, Indonesia. Slope, aspect, curvatures, flow accumulation and TPI were calculated following a two-step approach where first the DTM was resampled to 10 m pixel size and then the metrics were calculated and further aggregated to 50 m (see also Jucker et al. 2018 https://doi.org/10.1111/gcb.14415).
Original pixel size of DTM: 1m.
Geographic Coordinate System: EPSG 32748
Digital Terrain Models & elevation metrics
No full textDigital Terrain Models (DTMs), Slope, Aspect, Topographic Position Index (TPI), curvature in profile and plan direction and their average, and flow accumulation derived from airborne LiDAR data acquired over the PTPN 6 estate, Batanghari regency, Harapan rainforest protected area, and Sarolangun regency, in Jambi, Sumatra, Indonesia. Slope, aspect, curvatures, flow accumulation and TPI were calculated following a two-step approach where first the DTM was resampled to 10 m pixel size and then the metrics were calculated and further aggregated to 50 m (see also Jucker et al. 2018 https://doi.org/10.1111/gcb.14415).
Original pixel size of DTM: 1m.
Geographic Coordinate System: EPSG 32748
Landscape Assessment plots maps (with RGB, canopy height, NDVI and 3d point cloud)
No full textMaps of 99 Landscape Assessment plots. These maps include the Landscape Assessment plot (circular 1000 m²) and surroundings. The maps display high-resolution orthophotos as true-color RGB composites (5 cm spatial resolution), canopy height (1 m), normalized difference vegetation index (NDVI; 1 m) and 3d representation of LiDAR point cloud.
The original data can be found for
the RGB composites here: https://doi.org/10.25625/1RB4CC
the canopy height model here: https://doi.org/10.25625/CKLY7X
the NDVI here: https://doi.org/10.25625/AIDFG2
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Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data
Get PDFVegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall-to-wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM-X data, simplifications are necessary to estimate canopy height with semi-empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR).We trained the semi-empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM-X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation.Our results indicated that the empirical parameters substantially improved the estimation from a root-mean-square-error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land-use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals.We demonstrate the potential of the combined exploitation of the TanDEM-X and GEDI missions for a wall-to-wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data
A Novel Approach for Environmental Monitoring Based on the Integration of Multi-Temporal Multi-Source Earth Observation Data and Field Surveys in a Spatio-Temporal Framework
No full textAssessment of a power law relationship between P-band SAR backscatter and aboveground biomass and its implications for BIOMASS mission performance
Get PDFThis paper presents an analysis of a logarithmic relationship between P-band cross-polarized backscatter from synthetic aperture radar (SAR) and aboveground biomass (AGB) across different forest types based on multiple airborne datasets. It is found that the logarithmic function provides a statistically significant fit to the observed relationship between HV backscatter and AGB. While the coefficient of determination varies between datasets, the slopes, and intercepts of many of the models are not significantly different, especially when similar AGB ranges are assessed. Pooled boreal and pooled tropical data have slopes that are not significantly different, but they have different intercepts. Using the power law formulation of the logarithmic relation allows estimation of both the equivalent number of looks (ENL) needed to retrieve AGB with a given uncertainty and the sensitivity of the AGB inversion. The campaign data indicates that boreal forests require a larger ENL than tropical forests to achieve a specified relative accuracy. The ENL can be increased by multichannel filtering, but ascending and descending images will need to be combined to meet the performance requirements of the BIOMASS mission. The analysis also indicates that the relative change in AGB associated with a given backscatter change depends only on the magnitude of the change and the exponent of the power law, and further implies that to achieve a relative AGB accuracy of 20% or better, residual errors from radiometric distortions produced by the system and environmental effects must not exceed 0.43 dB in tropical and 0.39 dB in boreal forests
Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models
Get PDFSpaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions
Dr. Duane M. Jackson, Morehouse College, July 2011
No full textThis video is a conversation with Dr. Duane M. Jackson. Dr. Jackson talks about his paper, "Recall and the Serial Position Effect: The Role of Primacy and Recency on Accounting Students' Performance." Jackie Daniel, AUC Woodruff Library, is the interviewer
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