Qingwang Liu

LiCHy: The CAF’s LiDAR, CCD and Hyperspectral Integrated Airborne Observation System

We describe the design, implementation and performance of a novel airborne system, which integrates commercial waveform LiDAR, CCD (Charge-Coupled Device) camera and hyperspectral sensors into a common platform system. CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System is a unique system that permits simultaneous measurements of vegetation vertical structure, horizontal pattern, and foliar spectra from different view angles at very high spatial resolution (~1 m) on a wide range of airborne platforms. The horizontal geo-location accuracy of LiDAR and CCD is about 0.5 m, with LiDAR vertical resolution and accuracy 0.15 m and 0.3 m, respectively. The geo-location accuracy of hyperspectral image is within 2 pixels for nadir view observations and 5–7 pixels for large off-nadir observations of 55° with multi-angle modular when comparing to LiDAR product. The complementary nature of LiCHy’s sensors makes it an effective and comprehensive system for forest inventory, change detection, biodiversity monitoring, carbon accounting and ecosystem service evaluation. The LiCHy system has acquired more than 8000 km2 of data over typical forests across China. These data are being used to investigate potential LiDAR and optical remote sensing applications in forest management, forest carbon accounting, biodiversity evaluation, and to aid in the development of similar satellite configurations. This paper describes the integration of the LiCHy system, the instrument performance and data processing workflow. We also demonstrate LiCHy’s data characteristics, current coverage, and potential vegetation applications.

The complicate observations and multi-parameter land information constructions on allied telemetry experiment (COMPLICATE)

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.

Development of a System of Compatible Individual Tree Diameter and Aboveground Biomass Prediction Models Using Error-In-Variable Regression and Airborne LiDAR Data

Estimating individual tree diameters at breast height (DBH) from delineated crowns and tree heights on the basis of airborne light detection and ranging (LiDAR) data provides a good opportunity for large-scale forest inventory. Generally, ground-based measurements are more accurate, but LiDAR data and derived DBH values can be obtained over larger areas for a relatively smaller cost if a right procedure is developed. A nonlinear least squares (NLS) regression is not an appropriate approach to predict the aboveground biomass (AGB) of individual trees from the estimated DBH because both the response variable and the regressor are subject to measurement errors. In this study, a system of compatible individual tree DBH and AGB error-in-variable models was developed using error-in-variable regression techniques based on both airborne LiDAR and field-measured datasets of individual Picea crassifolia Kom. trees, collected in northwestern China. Two parameter estimation algorithms, i.e., the two-stage error-in-variable model (TSEM) and the nonlinear seemingly unrelated regression (NSUR), were proposed for estimating the parameters in the developed system of compatible individual tree DBH and AGB error-in-variable models. Moreover, two model structures were applied to estimate AGB for comparison purposes: NLS with AGB estimation depending on DBH (NLS&DD) and NLS with AGB estimation not depending on DBH (NLS&NDD). The results showed that both TSEM and NSUR led to almost the same parameter estimates for the developed system. Moreover, the developed system effectively accounted for the inherent correlation between DBH and AGB as well as for the effects of measurement errors in the DBH on the predictions of AGB, whereas NLS&DD and NLS&NDD did not. A leave-one-out cross-validation indicated that the prediction accuracy of the developed system of compatible individual tree DBH and AGB error-in-variable models with NSUR was the highest among those estimated by the four methods evaluated, but, statistically, the accuracy improvement was not significantly different from zero. The main reason might be that, except for the measurement errors, other source errors were ignored in the modeling. This study implies that, overall, the proposed method provides the potential to expand the estimations of both DBH and AGB from individual trees to stands by combining the error-in-variable modeling and LiDAR data and improve their estimation accuracies, but its application needs to be further validated by conducting a systematical uncertainty analysis of various source errors in a future study.

Forest height estimation based on uav lidar simulated waveform

The accurate estimation of forest height is very important for understanding forest biomass and forest vertical structures. To investigate the potentials of forest height mapping for future Chinese satellite mission concepts with a waveform Lidar system onboard, a field campaign was designed and implemented in Weihe forest farm, Northeastern China in August of 2016. The method we proposed in this paper is that firstly we generate simulated waveforms from Unmanned Aerial Vehicles (UAV) Lidar data, then we use random forest (RF) to get the most relevant variables from 18 waveform parameters driven from our simulated results and 11 terrain parameters from ASTER-DEM. Finally, we used Cubist machine learning algorithm to establish the relationships between 4 different forest heights and the selected variables. Initial results demonstrated that the simulated waveforms could estimate forest height very well.

Forest canopy cover analysis using UAS lidar

Laser pulses of LiDAR are able to penetrate forest canopy and characterize the vertical structure distribution. Forest canopy cover (CC) can be estimated from normalized point cloud (NPC) and canopy height model (CHM). Unmanned aerial system (UAS) LiDAR usually transmits laser pulses with wider scan angles, which would decrease the probability of penetrating through canopy and lead to overestimate forest CC. The paper analyzes the variation of estimated CC from NPC to determine the optimal scan angle. The raw and interpolated CHMs are also used to estimate forest CCs considering the effects of larger scan angles. The result indicates that forest CC from NPC constrained by scan angle can obviously decease uncertainty of overestimation than other models. NPC-based models are more consistent with field measurements of CCs than CHM-based models. Reasonable constrains should be considered for estimating forest CC using different sampling density of point clouds.

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

In this work, we present a strategy for obtaining the dynamics of forest above-ground biomass (AGB) at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering only one of the terms since the cumulative carbon flux should be consistent with AGB increments. We estimated forest AGB dynamics by combining two types of models driven by field, remote sensing, and auxiliary data. The strategy was successfully applied to the Qilian Mountains, a cold arid region located in northwest China. In the first step, we improved the efficiency of existing non-parametric methods for estimating forest AGB. We applied the Random Forest (RF) model in order to pre-select the most relevant remotely sensed features in Landsat Thematic Mapper 5 (TM) and ASTER GDEM V2 products (GDEM). These features were further used to construct an optimal configuration for the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the optimal k-NN configuration yielded satisfactory performance (R2 = 0.70 and RMSE = 24.52 tones ha−1). Hence, the k-NN configuration was used to generate a regional forest AGB basis map for 2009. In the second step, we obtained one seasonal cycles (2011) of carbon fluxes using the MODIS MOD_17 GPP (MOD_17) model that was driven by meteorological fields of a numerical weather prediction model (WRF) and calibrated to Eddy Covariance (EC) flux tower data. The calibrated model for 2010_well predicted GPP for 2011 (R2 = 0.88 and RMSE = 5.02 gC m−2 8d−1). In the third step, we calibrated the ecological process model (Biome-BioGeochemical Cycles (Biome-BGC)) to above GPP estimates (for 2011) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. The Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R2 = 0.75 and RMSE = 1. 27 gC m−2 d−1 for GPP, and R2 = 0.61 and RMSE = 1.17 gC m−2 d−1 for NEE). We used Biome-BGC to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments, were compared to dendrochronological measurements (R2 = 0.73 and RMSE = 46.65 g m−2 year−1). The calibrated Biome-BGC model provided estimates of forest carbon fluxes that were converted into interannual AGB increments according to site-calibrated coefficients. With combination of these increments with the AGB map of 2009, the modeling of forest AGB dynamics was accomplished.

Building height extraction from overlapping airborne images in urban environment using computer vision approach

Estimating and measuring building height has become one of the significant factors in urban planning, legal and illegal construction inspection, urban disaster warning and assessing, as well as providing initial mapping data for creating three dimensional (3D) digital city models. In this paper we examine the feasibility of extracting building height information using computer vision algorithms with Structure from Motion procedures (SfM) from overlapping airborne images in urban environment. 3D land surface models can be generated from airborne images, and then DTM is subtracted from the DSM to form the nDSM layer. Using object-based image analysis, building height can be differentiated from bush, trees, and others by rulesets of spectral features, geometric features and contextual information. The accuracy of the building height based on orthorectified images and nDSM from airborne imagery was at a similar level to those based on airborne LiDAR data from the same study area.

ICESat vegetation product evaluation with airborne lidar data in China

Spaceborne large footprint lidar (ICESat GLAS) has acquired over 250 million lidar observations over forest regions globally, an unprecedented dataset of vegetation heights. The ICESat Vegetation Product (IVP) was developed aimed at a global forest height dataset. Because of its high vertical resolution, large spatial extent and 70 m footprint characteristics, it is difficult to validate this product with other available remote sensing product or field measurements. To evaluation the IVP product in China, airborne waveform data was collected along several ICESat GLAS obits in the Southwest and West of China. The preliminary results show that the R2 is 0.41 and RMSE is 2.69 m between the vegetation height from airborne data and ICESat Vegetation Product at forest stand level in most cases. For those cases where the GLAS shots and airborne datasets gave very difference results, the airborne lidar data were synthesis to GLAS waveform and compared with the GLAS waveform. Those waveforms from fluctuated terrain also have several peaks and large waveform extent, which cased error in forest height estimation.