ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia
Abstract
:1. Introduction
2. Methodology
2.1. Study Area
2.2. Data
2.2.1. ICESat/GLAS Data
2.2.2. Airborne LiDAR Data
2.2.3. SRTM Data
2.2.4. MODIS Data
2.2.5. Field Inventory Data
2.3. Data Analysis
2.3.1. Airborne LiDAR Data Processing and Correlation with Field Inventory Data
2.3.2. ICESat/GLAS Data Processing and Analysis
2.3.3. Comparison ICESat/GLAS and Airborne LiDAR Data
2.3.4. Development of Above Ground Biomass Prediction Models from ICESat/GLAS Data
2.3.5. Conceptual Overview
3. Results
3.1. Comparison ICESat/GLAS, SRTM Data, and SRTM 3D Peatland Elevation Models
3.2. Comparison ICESat/GLAS and Airborne LiDAR Data
Airborne LiDAR statistics | ICESat/GLAS elevations | ||||||
---|---|---|---|---|---|---|---|
n | Signal begin | Nearest Gaussian peak | Waveform centroid | Last highest Gaussian peak | Last Gaussian peak | Signal end | |
Minimum z | 103 a | 0.50 | 0.48 | 0.61 | 0.63 | 0.68 | 0.66 |
Maximum z | 103 b | 0.86 | 0.76 | 0.81 | 0.48 | 0.43 | 0.42 |
Mean z | 104 | 0.84 | 0.77 | 0.91 | 0.60 | 0.60 | 0.59 |
Minimum DTM | 104 | 0.57 | 0.54 | 0.67 | 0.63 | 0.71 | 0.67 |
Maximum DTM | 104 | 0.57 | 0.54 | 0.67 | 0.62 | 0.70 | 0.67 |
Mean DTM | 104 | 0.57 | 0.54 | 0.67 | 0.62 | 0.71 | 0.67 |
Airborne LiDAR statistics | ICESat/GLAS heights metrics | |||||||
---|---|---|---|---|---|---|---|---|
n | H1 | H2 | H3 | H4 | H5 | H6 | H7 | |
Minimum | 102 a | 0.01 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 |
Maximum | 103 b | 0.40 | 0.46 | 0.54 | 0.42 | 0.49 | 0.33 | 0.28 |
Mean | 104 | 0.26 | 0.22 | 0.36 | 0.30 | 0.34 | 0.25 | 0.29 |
5% | 104 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 |
10% | 104 | 0.00 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
15% | 104 | 0.00 | 0.01 | 0.02 | 0.01 | 0.00 | 0.00 | 0.00 |
20% | 104 | 0.00 | 0.01 | 0.03 | 0.02 | 0.01 | 0.01 | 0.01 |
25% | 104 | 0.01 | 0.02 | 0.05 | 0.04 | 0.04 | 0.02 | 0.04 |
30% | 104 | 0.03 | 0.03 | 0.09 | 0.07 | 0.07 | 0.04 | 0.08 |
35% | 104 | 0.09 | 0.06 | 0.16 | 0.13 | 0.15 | 0.10 | 0.17 |
40% | 104 | 0.14 | 0.09 | 0.22 | 0.17 | 0.22 | 0.15 | 0.25 |
45% | 104 | 0.18 | 0.11 | 0.25 | 0.19 | 0.24 | 0.16 | 0.26 |
50% | 104 | 0.21 | 0.13 | 0.28 | 0.22 | 0.28 | 0.18 | 0.29 |
55% | 104 | 0.26 | 0.17 | 0.34 | 0.26 | 0.34 | 0.23 | 0.34 |
60% | 104 | 0.29 | 0.20 | 0.38 | 0.30 | 0.38 | 0.26 | 0.36 |
65% | 104 | 0.33 | 0.24 | 0.43 | 0.36 | 0.42 | 0.31 | 0.39 |
70% | 104 | 0.37 | 0.29 | 0.48 | 0.40 | 0.47 | 0.36 | 0.41 |
75% | 104 | 0.40 | 0.33 | 0.51 | 0.44 | 0.51 | 0.40 | 0.42 |
80% | 104 | 0.42 | 0.36 | 0.53 | 0.47 | 0.53 | 0.43 | 0.43 |
85% | 104 | 0.43 | 0.39 | 0.53 | 0.48 | 0.54 | 0.44 | 0.41 |
90% | 104 | 0.42 | 0.41 | 0.53 | 0.49 | 0.53 | 0.44 | 0.38 |
95% | 104 | 0.45 | 0.47 | 0.57 | 0.50 | 0.56 | 0.45 | 0.37 |
100% | 103 b | 0.40 | 0.46 | 0.54 | 0.42 | 0.49 | 0.33 | 0.28 |
QMCH | 102 c | 0.27 | 0.23 | 0.40 | 0.35 | 0.39 | 0.34 | 0.31 |
CL | 104 | 0.28 | 0.21 | 0.38 | 0.32 | 0.37 | 0.35 | 0.28 |
3.3. Above Ground Biomass Prediction Models from Airborne LiDAR Data and ICESat/GLAS Data
Average LiDAR point densitiy per square m | n | ICESat/GLAS height metrics | ||||||
---|---|---|---|---|---|---|---|---|
H1 | H2 | H3 | H4 | H5 | H6 | H7 | ||
all | 104 | 0.32 | 0.25 | 0.43 | 0.37 | 0.44 | 0.33 | 0.40 |
≥0.1 | 93 | 0.40 | 0.31 | 0.51 | 0.43 | 0.52 | 0.40 | 0.46 |
≥0.2 | 72 | 0.45 | 0.34 | 0.54 | 0.49 | 0.59 | 0.53 | 0.56 |
≥0.3 | 54 | 0.55 | 0.45 | 0.63 | 0.60 | 0.67 | 0.62 | 0.63 |
≥0.4 | 47 | 0.65 | 0.55 | 0.69 | 0.66 | 0.70 | 0.63 | 0.67 |
≥0.5 | 46 | 0.68 | 0.57 | 0.73 | 0.68 | 0.75 | 0.65 | 0.71 |
≥0.6 | 43 | 0.70 | 0.60 | 0.74 | 0.69 | 0.75 | 0.67 | 0.70 |
≥0.7 | 41 | 0.72 | 0.62 | 0.75 | 0.71 | 0.77 | 0.70 | 0.70 |
≥0.8 | 39 | 0.72 | 0.62 | 0.74 | 0.71 | 0.77 | 0.71 | 0.72 |
≥0.9 | 35 | 0.70 | 0.61 | 0.75 | 0.73 | 0.76 | 0.71 | 0.70 |
≥1 | 32 | 0.73 | 0.63 | 0.76 | 0.74 | 0.76 | 0.70 | 0.68 |
4. Discussion and Conclusions
Acknowledgments
References
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Ballhorn, U.; Jubanski, J.; Siegert, F. ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia. Remote Sens. 2011, 3, 1957-1982. https://doi.org/10.3390/rs3091957
Ballhorn U, Jubanski J, Siegert F. ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia. Remote Sensing. 2011; 3(9):1957-1982. https://doi.org/10.3390/rs3091957
Chicago/Turabian StyleBallhorn, Uwe, Juilson Jubanski, and Florian Siegert. 2011. "ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia" Remote Sensing 3, no. 9: 1957-1982. https://doi.org/10.3390/rs3091957