Forest above-ground biomass (AGB) is an important indicator for understanding the global carbon cycle. It is hard to obtain a geographically and statistically representative AGB dataset, which is limited by unpredictable environmental conditions and high economical cost. A spatially explicit AGB reference map was produced by airborne LiDAR data and calibrated by field measurements. Three different sampling strategies were designed to sample the reference AGB, PALSAR backscatter, and texture variables. Two parametric and four nonparametric models were established and validated based on the sampled dataset. Results showed that random stratified sampling that used LiDAR-evaluated forest age as stratification knowledge performed the best in the AGB sampling. The addition of backscatter texture variables improved the parametric model performance by an R2 increase of 21% and a root-mean-square error (RMSE) decrease of 10 Mg ha−1. One of the four nonparametric models, namely, the random forest regression model, obtained comparable performance (R2=0.78, RMSE=14.95 Mg ha−1) to the parametric model. Higher estimation errors occurred in the forest stands with lower canopy cover or higher AGB levels. In conclusion, incorporating airborne LiDAR and PALSAR data was proven to be efficient in upscaling the AGB estimation to regional scale, which provides some guidance for future forest management over cold and arid areas.
The significance of laser return intensity has been widely verified in airborne light detection and ranging (LiDAR)-based forest canopy mapping, but this does not mean that all of its roles have been played. People still ask such questions as “Is it possible using this optical attribute of lasers to investigate individual tree-crown insides wherein laser intensity data are typically yielded in complicated echo-triggering modes?” To answer this question, this study examined the characteristics of the intensities of the laser points within 10 Quercus robur trees by fitting their peak amplitudes into default Gaussian distributions and then analyzing the resulting asymmetric tails. Exploratory data analyses showed that the laser points lying within the distribution tails can indicate primary tree branches in a sketchy way. This suggests that the question can be positively answered, and the traditional restriction of airborne LiDAR in canopy mapping at the crown level has been broken. Overall, this study found a unique way to detect primary tree branches in airborne LiDAR data and pointed out how to explore more ways this optical intensity attribute of airborne LiDAR data can measure tree organs at fine scales and further learn their properties.
Airborne light detection and ranging (LiDAR) system calibration is a crucial procedure for ensuring the accuracy of point data. A common practice is to use conjugate planar patches to recover systematic parameters based on coplanar constraints and to use planes with different orientations to decrease the correlations between the systematic errors. When there are not sufficient planar patches and the configuration of planar patches is not optimal, it is difficult to guarantee the reliability of the estimated system parameters. Based on the analyses of the bore-sight angle effects, we find that not only the orientations but also the distribution of planar patches play an important role in the calibration procedure. We propose an improved method for bore-sight calibration based on the principles of symmetry of coordinate offsets and low correlations between bore-sight angles. Comparisons of the experimental results of bore-sight angle calibration suggest that the proposed configuration of conjugate planar patches can decrease the correlations between bore-sight angles and improve the reliability of calibration results. The optical results obtained from four gable-roof buildings are very close to the results calculated by the RiProcess software with a deviation of about 0.001 deg.
Airborne Light Detection and Ranging (LiDAR) is an active remote sensing technology which can acquire the
topographic information efficiently. It can record the accurate 3D coordinates of the targets and also the signal intensity
(the amplitude of backscattered echoes) which represents reflectance characteristics of targets. The intensity data has
been used in land use classification, vegetation fractional cover and leaf area index (LAI) estimation. Apart from the
reflectance characteristics of the targets, the intensity data can also be influenced by many other factors, such as flying
height, incident angle, atmospheric attenuation, laser pulse power and laser beam width. It is therefore necessary to
calibrate intensity values before further applications. In this study, we analyze the factors affecting LiDAR intensity
based on radar range equation firstly, and then applying the intensity calibration method, which includes the
sensor-to-target distance and incident angle, to the laser intensity data over the study area. Finally the raw LiDAR
intensity and normalized intensity data are used for land use classification along with LiDAR elevation data respectively.
The results show that the classification accuracy from the normalized intensity data is higher than that from raw LiDAR
intensity data and also indicate that the calibration of LiDAR intensity data is necessary in the application of land use
classification.
Light Detection and Ranging (LiDAR) and Synthetic
Aperture Radar (SAR) are two competitive active
remote sensing techniques in forest above ground biomass estimation, which is important for forest
management and global climate change study. This study aims to further explore their capabilities in
temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of
variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote
sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics,
backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2
imagery were calculated. First, simple linear regression models (SLR) was established between the
field-estimated above ground biomass and the remote
sensing variables. Pearson’s correlation coefficient
(R2) was used to find which LiDAR metric showed the most significant correlation with the regression
residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression
co-kriging was conducted by choosing the regression residuals as
dependent variable and the LiDAR
metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was
estimated using SLR model and RCoKrig model, respectively. The results for these two models were
validated using the same ground points. Results showed that both of these two methods achieved
satisfactory prediction accuracy, while
regression co-kriging showed the lower estimation error. It is
proved that regression co-kriging model is feasible
and effective in mapping the spatial pattern of AGB
in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.
Data quality determines the accuracy of results associated with remote sensing data processing and applications. However, few effective studies have been carried out on quality assessment methods for the full-waveform light detecting and ranging data. Using the geoscience laser altimeter system (GLAS) waveform data as an example, a signal-to-noise ratio (SNR)-based waveform quality assessment method is proposed to analyze the relationship between the SNR and its controlling factors, i.e., laser type, laser using time, topographic relief, and land cover type, and study the impacts of these factors on the quality of the GLAS waveform data. Results show that the SNR-based data quality assessment method can quantitatively and effectively assess the GLAS waveform data quality. The SNR linearly attenuates with the laser using time, and the attenuation rate varies with laser type. The topographic relief is inversely correlated with the SNR of the GLAS data. As the land cover structure (especially the vertical structure) becomes more complex, the SNR of the GLAS data decreases. It was found that land cover types in descending order of the SNR values are desert, farmland, water body, grassland, city, and forest.
The average vegetation height can be accurately extracted from ICESat GLAS data, however, a certain spatial interval
exist in laser strips and dots reduces the mapping accuracy of average canopy height after the interpolation of the GLAS
data. The MODIS-BRDF/albedo data consist of canopy structural data, such as LAI, canopy height etc. So the
combination of ICESat GLAS and MODIS data can be obtained more accurate distribution of average canopy height and
achieve the distribution of continuous canopy height. In this paper, the GLAS / MODIS data were collected in forest-rich
three provinces in northeastern China. We firstly filtered GLAS waveform data and get the average vegetation height,
and then selected the optional MODIS-BRDF / albedo bands to retrieve the average vegetation height. An artificial
neural networks model was esTablelished by training the MODIS BRDF data, and finally obtained the average
vegetation height over the whole three provinces. The fusion method between GLAS data and optical remote sensing
image was proposed to make up for their shortages and obtained a continuous distribution of average vegetation height.
It increases the analysis dimensions of forest ecosystem and produces more accurate data for forest biomass and carbon
storage estimates.
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