Abstract Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and 5.6 m, respectively), and correctly estimate vegetation heights up to >50 m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data (i.e., 2000 km2 in Gabon and ≈5800 km2 in Switzerland), high-resolution vegetation height maps with 10 m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery.
Moreover, tree height can also be regressed from spatially explicit maps of correlated environmental parameters (many of which are in turn derived, at least in part, from satellite observations): @cite_19 use quantities like precipitation, temperature, elevation, tree cover etc. to infer canopy height, again based on GLAS samples.
@cite_19
1981263865 1566532634 2294859034 2160454972
[1] Data from spaceborne light detection and ranging (lidar) opens the possibility to map forest vertical structure globally. We present a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite). A challenge in the use of GLAS data for global vegetation studies is the sparse coverage of lidar shots (mean = 121 data points degree2 for the L3C campaign). However, GLAS-derived canopy height (RH100) values were highly correlated with other, more spatially dense, ancillary variables available globally, which allowed us to model global RH100 from forest type, tree cover, elevation, and climatology maps. The difference between the model predicted RH100 and footprint level lidar-derived RH100 values showed that error increased in closed broadleaved forests such as the Amazon, underscoring the challenges in mapping tall (>40 m) canopies. The resulting map was validated with field measurements from 66 FLUXNET sites. The modeled RH100 versus in situ canopy height error (RMSE = 6.1 m, R2 = 0.5; or, RMSE = 4.4 m, R2 = 0.7 without 7 outliers) is conservative as it also includes measurement uncertainty and sub pixel variability within the 1-km pixels. Our results were compared against a recently published canopy height map. We found our values to be in general taller and more strongly correlated with FLUXNET data. Our map reveals a global latitudinal gradient in canopy height, increasing towards the equator, as well as coarse forest disturbance patterns. [1] The value of lidar derives from its ability to map ecosystem vertical structure which can be used to estimate aboveground carbon storage. Spaceborne lidar sensors collect data along transects and gain value for the global change science community when combined with data sources that have complete horizontal coverage. Data sources and methods for this type of analysis require evaluation. In this work we use image segmentation of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) data to produce a global map of 4.4 million forest patches. Where a Geoscience Laser Altimeter System (GLAS) transect intersects a patch, its height is calculated from the GLAS observations directly. Regression analysis is then used to estimate the heights of those patches without GLAS observations. Regression goodness-of-fit statistics indicate moderately strong relationships for predicting the 90th percentile patch height, with a mean RMSE of 5.9 m and mean correlation (R2) of 0.67. Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88 for fragmented forests and 74 for urban tree-cover areas, with FPRs lower than 2 for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000‐2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82 of the variance in AGB, with a root mean square error of 50.5 Mg ha −1 for a range of biomass between 0 and 454 Mg ha −1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate that the model successfully captured the regional distribution of AGB. The results showed a strong positive correlation (R 2 = 0.90) between the GLAS height metrics and predicted AGB.