FINE RESOLUTION ASSESSMENT OF THE CARBON FLUXES FROM CONTEMPORARY FOREST DYNAMICS

Current estimation of the Earth’s carbon budget contains large uncertainties, with the largest ones in its terrestrial components. With an overarching goal to improve the understanding of carbon budget at regional to global scales, this study aimed to: 1. Develop a grid-based carbon accounting (GCA) model for estimating carbon fluxes from forest disturbance, tested over North Carolina; 2. Develop a consistent timber product output (TPO) record for a globally important timber production region, including seven states in the southeast U.S.; and 3. Further improve the GCA model based on results from objectives 1 and 2, and use it to derive carbon source/sink estimates for all forest land in North Carolina.The results show that several inputs/parameters such as pre-disturbance carbon density, disturbance intensity, allocation of removed carbon among slash and different wood product pools, and forest growth rates could have large impact on carbon estimates. The total emission between 1986 and 2010 from logging over North Carolina was reduced by one third and two thirds, respectively, when remote sensing-based disturbance intensity and biomass data were used to replace parameter values inherited from the original bookkeeping carbon accounting (BCA) model, and was reduced by over 70% when both were used. Use of the TPO data derived in Chapter 3 to partition the removed carbon among slash and different wood product pools resulted in noticeably higher emission estimates than those derived using the partitioning ratios provided by the original BCA model. In addition, without considering legacy effect from wood products harvested before 1986, the emission value derived using the prompt release method was 50% higher than that derived using the delayed release method. This study addresses multiple sources of uncertainties related to the terrestrial carbon budget. The TPO study demonstrated an approach for deriving consistent TPO records for large timber production regions. The GCA model produced state level carbon estimates comparable to those reported by the U.S. Forest Service while providing critical spatial details needed to support carbon management and advance forest-driven climate change mitigation initiatives

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales

Epipolar Rectification with Minimum Perspective Distortion for Oblique Images

Epipolar rectification is of great importance for 3D modeling by using UAV (Unmanned Aerial Vehicle) images; however, the existing methods seldom consider the perspective distortion relative to surface planes. Therefore, an algorithm for the rectification of oblique images is proposed and implemented in detail. The basic principle is to minimize the rectified images’ perspective distortion relative to the reference planes. First, this minimization problem is formulated as a cost function that is constructed by the tangent value of angle deformation; second, it provides a great deal of flexibility on using different reference planes, such as roofs and the façades of buildings, to generate rectified images. Furthermore, a reasonable scale is acquired according to the dihedral angle between the rectified image plane and the original image plane. The low-quality regions of oblique images are cropped out according to the distortion size. Experimental results revealed that the proposed rectification method can result in improved matching precision (Semi-global dense matching). The matching precision is increased by about 30% for roofs and increased by just 1% for façades, while the façades are not parallel to the baseline. In another designed experiment, the selected façades are parallel to the baseline, the matching precision has a great improvement for façades, by an average of 22%. This fully proves our proposed algorithm that elimination of perspective distortion on rectified images can significantly improve the accuracy of dense matching

Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest

Retrieval of Aerosol Optical Properties over Land Using an Optimized Retrieval Algorithm Based on the Directional Polarimetric Camera

The Directional Polarization Camera (DPC) onboard the Chinese Gaofen-5 satellite, launched in May 2018, has similar specifications as the POLDER-3 instrument. The SRON Remote Sensing of Trace gas and Aerosol Products (RemoTAP) full retrieval algorithm is applied to DPC measurements to retrieve aerosol properties including the total Aerosol Optical Depth (AOD), the fine/coarse mode AOD and the SSA (Single Scattering Albedo). Measurements of the global ground-based AERONET network between December 2019 and April 2020 have been used for the validation of the DPC retrievals. According to the average Fine Mode Fraction (FMF) of the selected AERONET stations, the stations are divided into urban stations (FMF ≥ 0.5) and dust stations (FMF < 0.5). For the total AOD validation, DPC retrievals show better performance over urban stations than over dust stations, with average biases of 0.055 and 0.106, and RMSEs of 0.151 and 0.228, respectively. Regarding the fine mode AOD, the retrieval also performs better over urban stations. Compared with the total AOD validation, both the relatively lower bias (0.021 and 0.065) and the higher Gfrac (Fraction of Good retrieval, 63.8% and 47.3%, respectively) further indicate that DPC performs better when fine mode aerosols dominate. For the land SSA validation, most of our SSA retrievals (~71%) show differences with AERONET SSA retrievals lower than 0.05. Case studies over fire spots and dust over northern China demonstrate the encouraging application potential of DPC aerosol products. The difference between fine and coarse AOD can provide more aerosol source information compared with the total AOD alone. Since the SSA retrievals are particularly sensitive to absorbing fine particles, they can be easily used in the tracking of biomass burning aerosol

Aerosol Retrieval over Land from the Directional Polarimetric Camera Aboard on GF-5

The DPC (Directional Polarization Camera) onboard the Chinese GaoFen-5 (GF-5) satellite is the first operational aerosol monitoring instrument capable of performing multi-angle polarized measurements in China. Compared with POLDER (Polarization and Directionality of Earth’s Reflectance) which ended its mission in December 2013, DPC has similar band design, with a maximum of 12 imaging angles and a relatively higher spatial resolution of 3.3 km. The global aerosol optical depth (AOD) over land from October to December in 2018 was retrieved with multi-angle polarization measurements of DPC. Comparisons with MODIS (Moderate Resolution Imaging Spectroradiometer) AOD products show relatively good agreement over fine-aerosol-particle-dominated areas such as northern China and Huanghuai areas in eastern China, the southern foothills of the Himalayas and India. AERONET (Aerosol Robotic Network) measurements over Beijing, Xianghe and Kanpur were used to evaluate the accuracy of DPC AOD retrievals. The correlation coefficients are greater than 0.9 and the RMSE are lower than 0.08 for Beijing and Xianghe stations. For Kanpur, a relatively lower correlation of 0.772 and larger RMSE of 0.082 are found

Aerosol Retrieval over Land from the Directional Polarimetric Camera Aboard on GF-5

The DPC (Directional Polarization Camera) onboard the Chinese GaoFen-5 (GF-5) satellite is the first operational aerosol monitoring instrument capable of performing multi-angle polarized measurements in China. Compared with POLDER (Polarization and Directionality of Earth’s Reflectance) which ended its mission in December 2013, DPC has similar band design, with a maximum of 12 imaging angles and a relatively higher spatial resolution of 3.3 km. The global aerosol optical depth (AOD) over land from October to December in 2018 was retrieved with multi-angle polarization measurements of DPC. Comparisons with MODIS (Moderate Resolution Imaging Spectroradiometer) AOD products show relatively good agreement over fine-aerosol-particle-dominated areas such as northern China and Huanghuai areas in eastern China, the southern foothills of the Himalayas and India. AERONET (Aerosol Robotic Network) measurements over Beijing, Xianghe and Kanpur were used to evaluate the accuracy of DPC AOD retrievals. The correlation coefficients are greater than 0.9 and the RMSE are lower than 0.08 for Beijing and Xianghe stations. For Kanpur, a relatively lower correlation of 0.772 and larger RMSE of 0.082 are found.https://doi.org/10.3390/atmos1311188

Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud

To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS method includes both parallel architecture and a mechanism for adjusting the computational resources—the parallel geocomputing mechanism of the P-AaaS method used to execute a geospatial service and the execution algorithm of the P-AaaS based geospatial service chain, respectively. The P-AaaS based method has the following merits: (1) it inherits the advantages of the AaaS-based method (i.e., avoiding transfer of large volumes of remote sensing data or raster terrain data, agent migration, and intelligent conversion into services to improve domain expert collaboration); (2) it optimizes the low performance and the concurrent geoprocessing capability of the AaaS-based method, which is critical for special applications (e.g., highly concurrent applications and emergency response applications); and (3) it adjusts the computing resources dynamically according to the number and the performance requirements of concurrent requests, which allows the geospatial service chain to support a large number of concurrent requests by scaling up the cloud-based clusters in use and optimizes computing resources and costs by reducing the number of virtual machines (VMs) when the number of requests decreases