Global carbon cycle research and climate change mitigation measures require a means for large-scale monitoring of forest aboveground biomass (AGB). L-band synthetic aperture radar (SAR) is one of promising means, although its signal is saturated at 75–150 Mg ha−1 of AGB, making it difficult to monitor high biomass forest. The Advanced Land Observing Satellite-2/Phased Array L-band SAR-2 (PALSAR-2) conducts ScanSAR mode observations that cover the whole tropical region about nine times a year. We investigated whether such time-series data is effective in overcoming the signal saturation issue. In Borneo, we adopted a spaceborne Light Detection and Ranging (LiDAR) Ice, Cloud, and Land Elevation/Geoscience Laser Altimeter System (GLAS)-derived AGB data for training and validation data, then developed an AGB estimation model using the Random Forest algorithm. As a result, we improved the saturation issue, and estimated the AGB up to 280 Mg ha−1 with a root mean square error (RMSE) of 62.8 Mg ha−1. Such an AGB range covers 83% of Borneo's forests. The developed model was applied to create an AGB map of Borneo with a 250-m resolution as of 2016. Total AGB of Borneo was calculated at 12.8 Gt with an average of 173.3 Mg ha−1. This article showed the PALSAR-2 time-series data to be highly useful in the AGB mapping of high carbon stock forests. However, we needed to correct the difference in observation years between GLAS and PALSAR-2 using a simple biomass growth model, but the accuracy will be improved by using future LiDAR and SAR sensors.

Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R² = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha⁻¹, mean absolute error (MAE) = 40.98 Mg ha⁻¹, and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R² = 0.37, RMSE = 98.63 Mg ha⁻¹, MAE = 76.97 Mg ha⁻¹, and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha⁻¹ and a standard deviation of 44.37 Mg ha⁻¹. The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha⁻¹ and a standard deviation of 78.48 Mg ha⁻¹. We also found a strong linear relationship with between the model estimates and Japanese government data, with R² = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.

Floorplanning of modules has been a significant role in VLSI design automation and it can be formulated as the "Rectangle Packing Problem." Ising model-based computers (or annealing machines) are the type of a non-von Neumann computer and recently expected to solve combinatorial optimization problems efficiently. In this paper, we propose a mapping of "Rectangle Packing Problem" for solving it by the annealing machines. In our proposed mapping, a sequence-pair is represented on an Ising model. Our proposed approach maps a "Rectangle Packing Problem" with N rectangles onto a 3N ³ -spin logical Ising model. Experimental results demonstrate that through the proposed approach we can solve the problem with 18 rectangles at the maximum on a fully-connected annealing machine and the problem with three rectangles at the maximum on 20k-spin CMOS annealing machine.

Highly detailed and accurate forest maps are important for various applications including forest monitoring, forestry policy, climate change, and biodiversity loss. This study demonstrates a comprehensive and geographically transferable approach to produce a 12 category high-resolution land use/land cover (LULC) map over mainland Vietnam in 2016 by remote sensing data. The map included several natural forest categories (evergreen broadleaf, deciduous (mostly deciduous broadleaf), and coniferous (mostly evergreen coniferous)) and one category representing all popular plantation forests in Vietnam such as acacia (Acacia mangium, Acacia auriculiformis, Acacia hybrid), eucalyptus (Eucalyptus globulus), rubber (Hevea brasiliensis), and others. The approach combined the advantages of various sensor data by integrating their posterior probabilities resulting from applying a probabilistic classifier (comprised of kernel density estimation and Bayesian inference) to each datum individually. By using different synthetic aperture radar (SAR) images (PALSAR-2/ScanSAR, PALSAR-2 mosaic, Sentinel-1), optical images (Sentinel-2, Landsat-8) and topography data (AW3D30), the resultant map achieved 85.6% for the overall accuracy. The major forest classes including evergreen broadleaf forests and plantation forests had a user’s accuracy and producer’s accuracy ranging from 86.0% to 95.3%. Our map identified 9.55 × 106 ha (±0.16 × 106 ha) of natural forests and 3.89 × 106 ha (±0.11 × 106 ha) of plantation forests over mainland Vietnam, which were close to the Vietnamese government’s statistics (with differences of less than 8%). This study’s result provides a reliable input/reference to support forestry policy and land sciences in Vietnam.

Background: We aimed to clarify the potential of spaceborne light detection and ranging (LiDAR) to meet the increased demand for large-scale monitoring of forest resources. Results: We developed empirical models to estimate aboveground biomass (AGB) and canopy height in Borneo from Ice, Cloud, and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) data, and obtained root-mean-square errors of 38.7 Mg ha^–1 and 4.0 m, respectively. GLAS-estimated AGB averaged 191.8 Mg ha^–1. From 2004 to 2007, AGB decreased by an average of 33.1 Mg ha^–1, and the rate of forest loss was 2.4% year^–1. The total AGB in Borneo was estimated as 10.34 Gt. Conclusions: The results demonstrate the potential of spaceborne LiDAR for monitoring forest resources, and its potential to play an important role in REDD+ implementations.

Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015–2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam’s forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale.