The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS/PROBA Data

Multi-angle remote sensing can either be regarded as an added source of uncertainty for variable retrieval, or as a source of additional information, which enhances variable retrieval compared to traditional single-angle observation. However, the magnitude of these angular and band effects for forest structure parameters is difficult to quantify. We used the Discrete Anisotropic Radiative Transfer (DART) model and the Zelig model to simulate the forest canopy Bidirectional Reflectance Distribution Factor (BRDF) in order to build a look-up table, and eight vegetation indices were used to assess the relationship between BRDF and forest biomass in order to find the sensitive angles and bands. Further, the European Space Agency (ESA) mission, Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy (CHRIS-PROBA) and field sample measurements, were selected to test the angular and band effects on forest biomass retrieval. The results showed that the off-nadir vegetation indices could predict the forest biomass more accurately than the nadir. Additionally, we found that the viewing angle effect is more important, but the band effect could not be ignored, and the sensitive angles for extracting forest biomass are greater viewing angles, especially around the hot and dark spot directions. This work highlighted the combination of angles and bands, and found a new index based on the traditional vegetation index, Atmospherically Resistant Vegetation Index (ARVI), which is calculated by combining sensitive angles and sensitive bands, such as blue band 490 nm/−55°, green band 530 nm/55°, and the red band 697 nm/55°, and the new index was tested to improve the accuracy of forest biomass retrieval. This is a step forward in multi-angle remote sensing applications for mining the hidden relationship between BRDF and forest structure information, in order to increase the utilization efficiency of remote sensing data

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

ICESat-2 is the new generation of NASA’s ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean R 2 value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2

Effect of CSA Concentration on the Ammonia Sensing Properties of CSA-Doped PA6/PANI Composite Nanofibers

Camphor sulfonic acid (CSA)-doped polyamide 6/polyaniline (PA6/PANI) composite nanofibers were fabricated using in situ polymerization of aniline under different CSA concentrations (0.02, 0.04, 0.06, 0.08 and 0.10 M) with electrospun PA6 nanofibers as templates. The structural, morphological and ammonia sensing properties of the prepared composite nanofibers were studied using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), four-point probe techniques, X-ray diffraction (XRD) and a home-made gas sensing test system. All the results indicated that the CSA concentration had a great influence on the sensing properties of CSA-doped PA6/PANI composite nanofibers. The composite nanofibers doped with 0.02 M CSA showed the best ammonia sensing properties, with a significant sensitivity toward ammonia (NH3) at room temperature, superior to that of the composite nanofibers doped with 0.04–0.10 mol/L CSA. It was found that for high concentrations of CSA, the number of PANI–H+ reacted with NH3 would not make up a high proportion of all PANI–H+ within certain limits. As a result, within a certain range even though higher CSA-doped PA6/PANI nanofibers had better conductivity, their ammonia sensing performance would degrade

GEDI data evaluation and canopy height change analysis - a case study in the Northeast of China

1041701723National Key Research and Development ProgramNational Key Research and Development Progra

A patch filling method for thematic map refinement: a case study on forest cover mapping in the Greater Mekong Subregion and Malaysia

Accurate forest cover mapping is essential for monitoring the status of forest extent in Southeast Asia. However, tropical areas frequently experience cloud cover, resulting in invalid or missing data in thematic maps. The initial 2005 and 2010 forest cover maps produced by the collaboration of the Greater Mekong Subregion and Malaysia (GMS+) economies contain unclassified pixels in the areas affected by cloud or cloud shadow. To enhance the usability and effectiveness of the 2005 and 2010 GMS+ forest cover maps for further analysis and applications, we present a novel method for accurately mapping forest cover in the presence of cloud cover. We employed a pixel-based algorithm to create clear view composites and automatically generated land cover training labels from the existing forest cover maps. We then reclassified the invalid areas and produced updated maps. The land cover types for all previously missing pixels have been successfully reclassified. The accuracy of this method was assessed at both the pixel and region level, with an overall accuracy of 94.2% at the forest/non-forest level and 86.6% at the finer classification level by pixel level assessment across all reclassified patches, and 93.2% at the forest/non-forest level and 89.9% at the finer level by region level for the selected site. There are 2.6% of forest and 0.7% of non-forest areas in the 2005 map, as well as 2.7% of forest and 0.6% of non-forest in the 2010 map have been reclassified from invalid pixels. This approach provides a framework for filling invalid areas in the existing thematic map toward improving its spatial continuity. The updated outputs provide more accurate and reliable information than the initial maps on the status of forest extent in the GMS+, which is critical for effective forest management and sustainable use in the region

Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China

This paper proposes a method to classify individual tree species groups based on individual tree segmentation and crown-level spectrum extraction (“crown-based ITC” for abbr.) in a natural mixed forest of Northeast China, and compares with the pixel-based classification and segment summarization results (“pixel-based ITC” for abbr.). Tree species is a basic factor in forest management, and it is traditionally identified by field survey. This paper aims to explore the potential of individual tree classification in a natural, needle-leaved and broadleaved mixed forest. First, individual trees were isolated, and the spectra of individual trees were then extracted. The support vector machine (SVM) and spectrum angle mapper (SAM) classifiers were applied to classify the trees species. The pixel-based classification results from hyperspectral data and LiDAR derived individual tree isolation were compared. The results showed that the crown-based ITC classified broadleaved trees better than pixel-based ITC, while the classes distribution of the crown-based ITC was closer to the survey data. This indicated that crown-based ITC performed better than pixel-based ITC. Crown-based ITC efficiently identified the classes of the dominant and sub-dominant species. Regardless of whether SVM or SAM was used, the identification consistency relative to the field observations for the class of the dominant species was greater than 90%. In contrast, the consistencies of the classes of the sub-dominant species were approximately 60%, and the overall consistency of both the SVM and SAM was greater than 70%

Improved forest cover mapping by harmonizing multiple land cover products over China

Fine resolution land cover products are becoming increasingly more available for many regions. These products, however, may not meet the quality requirements of many applications. This study provides an approach for improving land cover mapping by leveraging existing products and clear view Landsat composites. Assessments using independent reference datasets revealed that the CAF-LC30 2020 product derived using this approach over China was more accurate than four existing land cover products. Its overall accuracy with field observations was 2.94% to 10.28% higher than those of the four existing land cover products in northeast China and was 2.10% to 8.18% better across China. It provided a more accurate representation of the land cover types in many regions where the existing land cover products had large classification errors. Forest areas calculated using the CAF-LC30 2020 for the 31 provinces and autonomous regions and municipalities (PARM) in mainland China were better correlated with those reported by the most recent National Forest Inventory (NFI) survey than areas calculated using the other four existing land cover products. Therefore, the CAF-LC30 2020 product should be a better alternative for understanding China’s forests in 2020 than the other four existing land cover products

Ammonia Sensing Performance of Polyaniline-Coated Polyamide 6 Nanofibers

To understand the properties of polyaniline (PANI), aim gas, and the interaction between them in PANI-based gas sensors and help us to design sensors with better properties, direct calculations with molecular dynamics (MD) simulations were done in this work. Polyamide 6/polyaniline (PA6/PANI) nanofiber ammonia gas sensors were studied as an example here, and the structural, morphological, and ammonia sensing properties (to 50-250 ppm ammonia) of PA6/PANI nanofibers were tested and evaluated by scanning electron microscopy, Fourier transform infrared spectroscopy, and a homemade test system. The PA6/PANI nanofibers were prepared by in situ polymerization of aniline with electrospun PA6 nanofibers as templates and hydrochloric acid (HCl) as a doping agent for PANI, and the sensors show rapid response, ideal selectivity, and acceptable repeatability. Then, complementary molecular dynamics simulations were performed to understand how ammonia molecules interact with HCl-doped PANI chains, thus providing insights into the molecular-level details of the ammonia sensing performances of this system. Results of the radial distribution functions and mean square displacement analysis of the MD simulations were consistent with the dedoping mechanism of the PANI chains

Mapping Aboveground Biomass using Texture Indices from Aerial Photos in a Temperate Forest of Northeastern China

Optical remote sensing data have been considered to display signal saturation phenomena in regions of high aboveground biomass (AGB) and multi-storied forest canopies. However, some recent studies using texture indices derived from optical remote sensing data via the Fourier-based textural ordination (FOTO) approach have provided promising results without saturation problems for some tropical forests, which tend to underestimate AGB predictions. This study was applied to the temperate mixed forest of the Liangshui National Nature Reserve in Northeastern China and demonstrated the capability of FOTO texture indices to obtain a higher prediction quality of forest AGB. Based on high spatial resolution aerial photos (1.0 m spatial resolution) acquired in September 2009, the relationship between FOTO texture indices and field-derived biomass measurements was calibrated using a support vector regression (SVR) algorithm. Ten-fold cross-validation was used to construct a robust prediction model, which avoided the over-fitting problem. By further comparison the performance of the model estimates for greater coverage, the predicted results were compared with a reference biomass map derived from LiDAR metrics. This study showed that the FOTO indices accounted for 88.3% of the variance in ground-based AGB; the root mean square error (RMSE) was 34.35 t/ha, and RMSE normalized by the mean value of the estimates was 22.31%. This novel texture-based method has great potential for forest AGB estimation in other temperate regions