- G.F. Ficetola (1)
- Sangram Ganguly (1)
- Mariano Garcia (1)
- G.W. Geerling (1)
- N.L.M. Gilissen (1)
- Stephen Hagen (1)
- G.B.M. Heuvelink (1)
- Dirk Hoekman (1)
- M. Labrador-Garcia (1)
- J.X. Leon (1)
- Victoria Meyer (1)
- C.A. Mücher (1)
- E. Padoa-Schioppa (1)
- S.R. Phinn (1)
- Timothy R.H. Pearson (1)
- A.M.J. Ragas (1)
- Orbita Roswintiart (1)
- Ervan Rutishauser (1)
- Sassan Saatchi (1)
- Ari Samboko (1)
- Carlos Silva (1)
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- A.J.M. Smits (1)
- Sarah Walker (1)
- Arief Wijaya (1)
- Liang Xu (1)
- Yifan Yu (1)
Carbon storage potential in degraded forests of Kalimantan, Indonesia
Ferraz, António ; Saatchi, Sassan ; Xu, Liang ; Hagen, Stephen ; Chave, Jerome ; Yu, Yifan ; Meyer, Victoria ; Garcia, Mariano ; Silva, Carlos ; Roswintiart, Orbita ; Samboko, Ari ; Sist, Plinio ; Walker, Sarah ; Pearson, Timothy R.H. ; Wijaya, Arief ; Sullivan, Franklin B. ; Rutishauser, Ervan ; Hoekman, Dirk ; Ganguly, Sangram - \ 2018
Environmental Research Letters 13 (2018)9. - ISSN 1748-9318
aboveground biomass mapping - airborne lidar - carbon - forest degradation - Indonesia - Kalimantan - peat swamp forests
The forests of Kalimantan are under severe pressure from extensive land use activities dominated by logging, palm oil plantations, and peatland fires. To implement the forest moratorium for mitigating greenhouse gas emissions, Indonesia's government requires information on the carbon stored in forests, including intact, degraded, secondary, and peat swamp forests. We developed a hybrid approach of producing a wall-to-wall map of the aboveground biomass (AGB) of intact and degraded forests of Kalimantan at 1 ha grid cells by combining field inventory plots, airborne lidar samples, and satellite radar and optical imagery. More than 110 000 ha of lidar data were acquired to systematically capture variations of forest structure and more than 104 field plots to develop lidar-biomass models. The lidar measurements were converted into biomass using models developed for 66 439 ha of drylands and 44 250 ha of wetland forests. By combining the AGB map with the national land cover map, we found that 22.3 Mha (106 ha) of forest remain on drylands ranging in biomass from 357.2 ±12.3 Mgha-1 in relatively intact forests to 134.2 ±6.1 Mgha-1 in severely degraded forests. The remaining peat swamp forests are heterogeneous in coverage and degradation level, extending over 3.62 Mha and having an average AGB of 211.8 ±12.7 Mgha-1. Emission factors calculated from aboveground biomass only suggest that the carbon storage potential of more than 15 Mha of degraded and secondary dryland forests will be about 1.1 PgC.
Incorporating DEM Uncertainty in Coastal Inundation Mapping
Leon, J.X. ; Heuvelink, G.B.M. ; Phinn, S.R. - \ 2014
PLoS ONE 9 (2014)9. - ISSN 1932-6203
sea-level rise - climate-change - spatial prediction - airborne lidar - elevation data - error - geostatistics - adaptation - topography - simulation
Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.
How many predictors in species distribution models at the landscape scale? Land use versus LiDAR-derived canopy height
Ficetola, G.F. ; Bonardi, A. ; Mücher, C.A. ; Gilissen, N.L.M. ; Padoa-Schioppa, E. - \ 2014
International Journal of Geographical Information Science 28 (2014)8. - ISSN 1365-8816 - p. 1723 - 1739.
mapping forest structure - airborne lidar - habitat - vegetation - availability - environments - biodiversity - probability - explanation - localities
At the local spatial scale, land-use variables are often employed as predictors for ecological niche models (ENMs). Remote sensing can provide additional synoptic information describing vegetation structure in detail. However, there is limited knowledge on which environmental variables and how many of them should be used to calibrate ENMs. We used an information-theoretic approach to compare the performance of ENMs using different sets of predictors: (1) a full set of land-cover variables (seven, obtained from the LGN6 Dutch National Land Use Database); (2) a reduced set of land-cover variables (three); (3) remotely sensed laser data optimized to measure vegetation structure and canopy height (LiDAR, light detection and ranging); and (4) combinations of land cover and LiDAR. ENMs were built for a set of bird species in the Veluwe Natura 2000 site (the Netherlands); for each species, 26–214 records were available from standardized monitoring. Models were built using MaxEnt, and the best performing models were identified using the Akaike’s information criterion corrected for small sample size (AICc). For 78% of the bird species analysed, LiDAR data were included in the best AICc model. The model including LiDAR only was the best performing one in most cases, followed by the model including a reduced set of land-use variables. Models including many land-use variables tended to have limited support. The number of variables included in the best model increased for species with more presence records. For all species with 33 records or less, the best model included LiDAR only. Models with many land-use variables were only selected for species with >150 records. Test area under the curve (AUC) scores ranged between 0.72 and 0.92. Remote sensing data can thus provide regional information useful for modelling at the local and landscape scale, particularly when presence records are limited. ENMs can be optimized through the selection of the number and identity of environmental predictors. Few variables can be sufficient if presence records are limited in number. Synoptic remote sensing data provide a good measure of vegetation structure and may allow a better representation of the available habitat, being extremely useful in this case. Conversely, a larger number of predictors, including land-use variables, can be useful if a large number of presence records are available.
Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data
Geerling, G.W. ; Labrador-Garcia, M. ; Clevers, J.G.P.W. ; Ragas, A.M.J. ; Smits, A.J.M. - \ 2007
International Journal of Remote Sensing 28 (2007)19. - ISSN 0143-1161 - p. 4263 - 4284.
airborne lidar - forest canopy - river - rejuvenation - biodiversity - succession - accuracy - science - imagery
To safeguard the goals of flood protection and nature development, a river manager requires detailed and up-to-date information on vegetation structures in floodplains. In this study, remote-sensing data on the vegetation of a semi-natural floodplain along the river Waal in the Netherlands were gathered by means of a Compact Airborne Spectrographic Imager (CASI; spectral information) and LiDAR (structural information). These data were used to classify the floodplain vegetation into eight and five different vegetation classes, respectively. The main objective was to fuse the CASI and LiDAR-derived datasets on a pixel level and to compare the classification results of the fused dataset with those of the non-fused datasets. The performance of the classification results was evaluated against vegetation data recorded in the field. The LiDAR data alone provided insufficient information for accurate classification. The overall accuracy amounted to 41% in the five-class set. Using CASI data only, the overall accuracy was 74% (five-class set). The combination produced the best results, raising the overall accuracy to 81% (five-class set). It is concluded that fusion of CASI and LiDAR data can improve the classification of floodplain vegetation, especially for those vegetation classes which are important to predict hydraulic roughness, i.e. bush and forest. A novel measure, the balance index, is introduced to assess the accuracy of error matrices describing an ordered sequence of classes such as vegetation structure classes that range from bare soil to forest.