Feilong Ling

Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data

In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. Estimates of AGB are relevant for sustainable forest management, monitoring global change, and carbon accounting. This is particularly true for the Qilian Mountains, which are a water resource protection zone. We combined forest inventory data from 133 plots with TM images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) V2 products (GDEM) in order to analyse the influence of the sun-canopy-sensor plus C (SCS+C) topographic correction on estimations of forest AGB using the stepwise multiple linear regression (SMLR) and k-nearest neighbour (k-NN) methods. For both methods, our results indicated that the SCS+C correction was necessary for getting more reliable forest AGB estimates within this complex terrain. Remotely sensed AGB estimates were validated against forest inventory data using the leave-one-out (LOO) method. An optimized k-NN method was designed by varying both mathematical formulation of the algorithm and remote-sensing data input, which resulted in 3000 different model configurations. Following topographic correction, performance of the optimized k-NN method was compared to that of the regression method. The optimized k-NN method (R2 = 0.59, root mean square error (RMSE) = 24.92 tonnes ha–1) was found to perform much better than the regression method (R2 = 0.42, RMSE = 29.74 tonnes ha–1) for forest AGB retrieval over this montane area. Our results indicated that the optimized k-NN method is capable of operational application to forest AGB estimates in regions where few inventory data are available.

Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

We propose a long-term parameterization scheme for two critical parameters, zero-plane displacement height (d) and aerodynamic roughness length (z0m), that we further use in the Surface Energy Balance System (SEBS). A sensitivity analysis of SEBS indicated that these two parameters largely impact the estimated sensible heat and latent heat fluxes. First, we calibrated regression relationships between measured forest vertical parameters (Lorey’s height and the frontal area index (FAI)) and forest aboveground biomass (AGB). Next, we derived the interannual Lorey’s height and FAI values from our calibrated regression models and corresponding forest AGB dynamics that were converted from interannual carbon fluxes, as simulated from two incorporated ecological models and a 2009 forest basis map These dynamic forest vertical parameters, combined with refined eight-day Global LAnd Surface Satellite (GLASS) LAI products, were applied to estimate the eight-day d, z0m, and, thus, the heat roughness length (z0h). The obtained d, z0m and z0h were then used as forcing for the SEBS model in order to simulate long-term forest evapotranspiration (ET) from 2000 to 2012 within the Qilian Mountains (QMs). As compared with MODIS, MOD16 products at the eddy covariance (EC) site, ET estimates from the SEBS agreed much better with EC measurements (R2 = 0.80 and RMSE = 0.21 mm·day−1).

Rice areas mapping using ALOS PALSAR FBD data considering the Bragg scattering in L-band SAR images of rice fields

The objective of this paper is to assess the use of ALOS PALSAR FBD data to map rice growing areas. Image enhancement in backscattering in rice fields as a result of Bragg resonance scattering was found only at HH polarization since double-bounce scattering is a prerequisite to Bragg resonance scattering for radar backscatter from bunches of rice plants. A rice mapping method using HV images was developed and applied to Haian test site. Validation showed that rice mapping using L-band SAR is promising when cross-polarized data are available to cope with the Bragg resonance scattering effects.

Comparison of estimating forest above-ground biomass over montane area by two non-parametric methods

Forest biomass reflects the ecological succession and human disturbance of the forest, and can fully embody the quality of forest ecosystem environment. The Qilian Mountain forest reserve at upper reaches of the Heihe River Basin was selected for the study. Landsat Thematic Mapper 5 (TM) images were selected as the source data, which were rectified by SCS + C terrain radiometric correction. Forest above-ground biomass was estimated using k-nearest neighbor (k-NN) method and support vector regression (SVR) method, respectively. The results show that spectral information of remote sensing image was recovered by the sun-canopy-sensor plus the C (SCS+C) terrain correction which can effectively improve the estimation accuracy of the models regardless of k-NN or SVR. The optimal k-NN method (R2=0.54, RMSE=26.62ton/ha) performs better than the optimal SVR method (R2=0.51, RMSE=27.45ton/ha).

Dynamic analysis and modeling of Forest above-ground biomass

Estimating forest above-ground biomass (AGB) and monitoring its variation are relevant for sustainable forest management, monitoring global change, carbon accounting, particularly for the Qilian Mountains (QMs), a water resource protection zone. In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. An optimized k-Nearest Neighbor (k-NN) method was determined by varying both the mathematical formulation of the algorithm and remote sensing data input which resulted in 3,000 different model configurations. Following the sun-canopy-sensor plus C (SCS+C) topographic correction, performance of the optimized k-NN method was satisfied (R2=0.59, RMSE=24.92 ton/ha) which indicated that the optimized k-NN is capable of operational applications of forest AGB estimates in regions where only a few inventory data are available. Afterwards, the calibrated BIOME-BGC was applied to simulate the carbon fluxes over QMs forests with satisfactory accuracy. Finally, the dynamic analysis and modeling of forest AGB was conducted based on the remotely sensed estimation of forest AGB and the annual forest AGB increment from the ecological process model.

Comparison of crop classification capabilities of spaceborne multi-parameter SAR data

With the arisen spaceborne multi-parameter Synthetic Aperture Radar (SAR) systems, such as Envisat ASAR, TerraSAR-X, ALOS PALSAR, and RADARSAT-2, the interest of crop mapping has been increasing. The present study compares the capabilities of the multi-parameter SAR in discriminating the main crop types by object-based classification in Haian county of Jiangsu province, South China. Two kinds of information, SAR intensity based and SAR statistical properties based are used for Maximum Likelihood Classification (MLC) and Minimum Distance Classification (MDC) respectively. The results show that, the L-band SAR can uniquely identify mulberry from dry-land crops, such as maize and vegetable and C-band SAR has some advantages in mapping rice. Specifically, the polarimetric RADARASAT-2 data can identify the rice with accuracy about 75% ∼ 80% which is similar as the result from X-band TerraSAR-X Spotlight data but higher than that from C-band dual-polarization Envisat ASAR data. Nevertheless, both of X- and C-band can hardly separate the mulberry from the other dry-land crops.

Forest mapping with multi-temporal dual polarization ALOS PALSAR data

The objective of this study is to exploit the new features of ALOS PALSAR dual polarization mode data and to develop novel classification method for forest mapping in heterogeneous areas. A test site was selected in Fujian province in southeast of China. Traditionally, forest is detected by its low coherence, low temporal variability of the backscattering intensity and mediate backscattering intensity. However, the analyses in this paper indicate that it is not possible to discriminate forest from nonforest by any single PALSAR feature in this test site. After examination the dependences of the multitemporal backscatter intensity, the polarimetric parameters and the interferometric coherence on different land cover types, a hierarchical classification method is proposed for coastal forest and hilly forest mapping. The forest maps are validated by forest inventory data and SPOPT-5 images. The results show that multitemporal PALSAR dual polarization data can accurate maps for coastal forest in flat areas using the proposed method. The capability to map forest in hilly regions is still limited.

Uncertainty analysis of forest above-ground biomass increments in southern Qilian Mountains

This study showed the potential for using Monte Carlo method for uncertainty analyses based on multi-parameter remote sensing data or incorporated models. The Monte Carlo method, involves using random numbers and probability to solve problems, is a simple but useful tool for uncertainty analyses with complex models. In this study, the uncertainties of the inter-annual and the overall forest AGB increments in the QMs form 2000 to 2012 were analyzed based on the Monte Carlo method. The sensitivity analysis showed that the modeled NPPs (SEnpp) contributed the most uncertainty to the inter-annual forest AGB dynamics. Furthermore, multi-source data and incorporated model fusion methods should be adopted to reduce model output uncertainties.

Estimation of Gross Primary Productivity of four types of forest in China

The quantification of forest Gross Primary Productivity (GPP) has been the focus of many scientific studies (e.g. carbon cycle, climate change, etc.). Current remote sensing-based models (i.e., the MODIS MOD_17 model), rely on the accurate meteorological data, specific vegetation parameter, the applicability and explicability of remote sensing data. In this study, the original MODIS GPP products were validated and showed significant underestimation compared to the eddy covariance measurements of the four forest sites over China. Thus the strategy of simple yet accurate and quantitative simulation of carbon fluxes was improved by using Sims TG model which was termed the Temperature and Greenness (TG) model and included the land surface temperature (LST) product and enhanced vegetation index (EVI) product from MODIS. The results indicated that Sims TG model was poor adaptive to tropical and subtropical evergreen forest in China. The model precision of deciduous forest was high but the GPP of evergreen forest are underestimated in Qianyanzhou and Xishuangbanna station in summer. After the parameter was optimized in Sims TG model, the estimation accuracy of evergreen forest GPP was improved to a certain extent and the model better adapt to dynamic change of forest GPP in China.

Forest/non-forest mapping using ENVISAT ASAR data in Northeast China

Large scale forest mapping and change detection plays a significant role in the study of global change, particularly in the research of carbon source and sink. This paper presents results from forest/non-forest classification using ENVISAT-ASAR data. Both pixel-based and object-based classification method were developed for ASAR HH/HV images acquired on a single date. For the object-based classification, two different strategies were proposed: rule-set and threshold-ratio. Using as reference a land use map derived from Landsat TM images acquired in 2000, the accuracy of the forest/non-forest map from ASAR AP data has been found to meet the requirements of mapping the Northeast Chinese forests at large scale.