Tomography synthetic aperture radar (TomoSAR) is a cutting-edge radar observation technique that has the ability to produce three-dimensional images and can effectively extract forest vertical structure parameters, including forest height, a key forest parameter closely related to forest biomass and carbon storage. However, the phase errors in the TomoSAR data are unavoidable due to the elements such as orbit errors, which can seriously affect the quality of tomographic imaging and thus affect the accuracy of forest parameter extraction. To address this issue, various methods have been proposed. Nevertheless, they still exhibit restrictions when addressing phase errors with complex trends. To solve such problem, a novel method was developed and implemented in this paper, which includes two steps and remove parts of the phase errors with different trends sequentially. First, a wavelet decomposition and polynomial fitting-based approach was applied to each track to remove the slowly but significantly spatially-varying part of the phase errors. Secondly, the modified autofocusing algorithm is proposed to correct the remaining phase errors, which adopted the two-dimensional image entropy as the optimization indicator, providing stronger robustness compared to traditional indicator. Furthermore, in order to overcome the initial value dependency of the traditional search method, the proposed autofocusing algorithm used the particle swarm algorithm as search engine. After the phase error correction, the forest height was extracted by identifying the upper and lower boundary of the forest from the corrected TomosAR profiles. Two P-band datasets obtained in north China are adopted to examine the proposed phase error correction method. Experimental results show that compared with traditional autofocusing algorithm, the proposed method can achieve higher quality tomographic imaging results. On the basis of TomoSAR imaging, higher precision forest height extraction is obtained based on the new method.

In this paper, we studied the multi-feature combination estimation approach of forest above ground biomass (AGB) using X-band InSAR and P-band PolInSAR data. We focus on a crucial step of the estimation process, which is selection of the optimal feature combination. Firstly, the feature pool was acquired using multi-frequency SAR data, which includes optimized features (forest height and polarimetric interferometric feature) and original features (polarimetric features, intensity features, and texture features). Then, using machine learning method to select the optimal feature combination. Finally, the forest AGB was estimated based on multiple types of the features combination. The experimental results showed that the combination of optimized features with original features has the highest accuracy in forest AGB estimation, followed by the combination using only optimized features. The accuracy of forest AGB estimation is lower for the feature combination that does not include optimized features.

This article introduces the Chinese Terrestrial Ecosystem Carbon Inventory Satellite(TECIS), the first mission with the integration of active and passive sensors for forest carbon mapping. TECIS utilizes time-synchronized multiple-beam LiDAR and multi-angle optical imagery for forest carbon monitoring. We first provide an overview of the satellite's features and discuss the observational capabilities of the LiDAR and multi-angle payload. The preliminary results for forest height estimation analysis were shown using the payloads. The Bidirectional Reflectance Distribution Function (BRDF) features such as hot/dark spot information, were calculated based on the multi-angle images. A deep learning approach for forest parameter estimation through the fusion of LiDAR and BRDF data.

Accurate tree species information is a prerequisite for forest resource management. Combining light detection and ranging (LiDAR) and image data is one main method of tree species classification. Traditional machinelearningmethods rely on expert knowledge to calculatea large number of feature parameters.Deep learning technology can directly use the original image and pointclouddata to classify tree species. However, data with different patterns require the use of different types of deeplearningmethods. In this study, a multimodal deeplearningframework (TSCMDL) that fuses 2D and 3D features was constructed and then used to combine data from multiple sources for tree species classification. This framework uses an improved version of the PointMLP model as its backbone network and uses ResNet50 and PointMLP networks to extract the image features and pointcloudfeatures, respectively. The proposed framework was tested using UAV LiDAR data and RGB orthophotos. The results showed that the accuracy of the tree species classification using the TSCMDL framework was 98.52%, which was 4.02% higher than that based on pointcloudfeatures only. In addition, when the same hyperparameters were used for training the model, the efficiency of the model training was not significantly lower than for models based on pointcloudfeatures only. The proposed multimodal deeplearningframework extracts features directly from the original data and integrates them effectively, thus avoiding manual feature screening and achieving more accurate classification. The feature extraction network used in the TSCMDL framework can be replaced by other suitable frameworks and has strong application potential.

Identifying forest canopy gaps and monitoring gap dynamics are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. However, it is still difficult to observe and measure canopy gaps extensively in both space and time using field measurements or bi-dimensional remote sensing images, particularly in large extensive boreal forests. In this study, we investigate the feasibility to map boreal canopy gaps of different sizes with airborne lidar datasets and derive the characteristics of gap dynamics. Two co-registered canopy height models (CHMs) of 1m resolution were created from lidar datasets acquired respectively in 2012 and 2016. Canopy gaps are automatically delineated using fixed-height threshold method. Further, two gap layers are compared and the changing information of canopy gaps are delineated to provide information on the area of old and new gaps, gap expansions, new random gap openings, gap closure due to disturbance and regeneration. The result shows airborne lidar is an efficient and effective tool for rapidly extracting detailed and spatially extensive short-term dynamics of canopy gaps.

Forest height is a key parameter of forest spatial structure, which is useful for forest management and ecosystem modelling. High resolution stereo images of satellite can be used to reconstruct the surface of ground objects. The objective of this study is to estimate the coniferous forest height combining stereo images of Gaofen-7 (GF-7) satellite and digital terrain model (DTM) extracted from airborne light detection and ranging (LiDAR). The study site is characterized by boreal coniferous forest located in the northern China. The dense point cloud from stereo images was normalized by subtracting the LiDAR DTM. The forest height was estimated by linear regression between the extracted features of normalized point cloud and the field measurements of stand height. The results shown that the values of estimated height were high correlated with the height percentile of 50% (R2 = 0.79, RMSE = 2.34m). The stereo images of GF-7 satellite have the great potential for regional forest monitoring.

In this article, a multilevel model (MLM) for forest height inversion is introduced and investigated using X-band single-pass interferometric synthetic aperture radar (InSAR) coherence data. Compared with the two-level model (TLM), as a generalized model, the MLM is more in line with the characteristics of forest structure and the scattering mechanism for X-band data. Based on the MLM model, three simplified MLM models were derived: TLMm, SINC, and MTND. An improved method of calculating coherence considering the assumptions of the MLM model is proposed. At two test sites, airborne (CASMSAR) and spaceborne (TanDEM-X) X-band single-pass InSAR data and LiDAR H100 data were used to verify the proposed new approach. The results showed that the MTND model can obtain more reliable and accurate inversion results compared to the SINC model and the TLMm. With airborne InSAR data, the MTND model’s highest accuracy was 82.56%, and the root mean square error (RMSE) was 2.4 m. With spaceborne InSAR data, the highest inversion accuracy of the MTND model was 73.55%, with an RMSE of 4.92 m. The inversion accuracy of forest height can be effectively improved by ensuring that the theoretical models and calculations of coherence obey the same assumptions.

Forest canopy cover (CC) directly and indirectly influences various processes of forest ecosystems. Airborne light detection and ranging (LiDAR) can be used to characterize forest spatial structures and further obtain estimates of forest CC. However, nonreturn laser pulses from targets of interest impact the estimation accuracy of forest CC. The objective of this article was to develop a novel method of estimating the nonreturn laser pulses to improve the estimation accuracy of forest CC using LiDAR data. The improved models for estimating forest CC were developed by introducing the loss ratio of laser pulses and a CC coefficient into the original models. The forest CC reference data were collected and used to validate the forest CC estimates. The results show that the loss ratio for forested areas was much higher than that for open ground areas. The range between the sensor and a target was a crucial factor that caused the loss of returns. The relationship between the range and the loss ratio was nonlinear in both open ground and forested areas. Compared with the original models, the improved models combining the loss ratio and the CC coefficient statistically significantly increased the estimation accuracy of the forest CC. Moreover, the forest CC estimates from the canopy height model (CHM) were more accurate than those from the height normalized point cloud (NPC) data. In addition, the simplified models were more generalized than the other models. This article is novel and has great potential to improve mapping of forest CC.

Accurate estimation of forest effective leaf area index (LAIe) is of great significance for regional carbon sequestration studies, forest management and monitoring. In this study, an advanced method was developed to predict LAIe based on Li-Strahler geometric-optical model, airborne LiDAR and Landsat 7 ETM+ data. More specifically, based on the Li-Strahler geometric-optical model, a reliable method was developed to solve the mixed pixel problem, and further to realize the prediction of regional LAIe from airborne LiDAR and multispectral data. First, based on the airborne LiDAR-derived canopy height product, the LAIe was estimated over airborne LiDAR coverage. Second, the sunlit background component was calculated based on the simplified relationship with canopy gap, and LAIe. Then, the reflectance of sunlit background was calculated based on the linear decomposition model. Finally, the forest LAIe was estimated by using Li-Strahler geometric-optical model over the study area. Results showed that the retrieval method proposed in this study could be used effectively in the inversion of regional LAIe, with the significant coefficient of determination (R2) was 0.81 and root mean square error (RMSE) was 0.23, as compared with field measurements.

Forest stand height is one of the most important tree measurement metrics in forest inventory. Airborne LiDAR is likely to be a preferred technology for acquiring high-quality forest stand height to support forest survey and management, but the cost is much higher if being used to monitor the changes of forest stand height in time series. In this study, we use ZIYUAN-3 (ZY-3) tri-stereo imagery to create photogrammetric digital surface models (DSM) by dense image matching methods and LiDAR data to derive the digital terrain models (DTM). Forest stand height can be derived from ZY3-lidar CHM which is produced by subtracting the lidar DTM from the ZY-3 photogrammetric DSM. The forest stand height map is validated by the systematic LiDAR CHM plot. The result shows that ZY-3 tri-stereo imagery can be used for forest stand height mapping.

The accurate mapping of forest AGB using remote sensing dataset is hindered by the saturation problem and the terrain effects. Direct measurement of forest spatial structures and terrains should be the solutions of these problems. However, the information of forest vertical structure and ground surface terrain are always mixed together in remote sensing datasets which can directly measure the elevations of ground objects. One potential way is to separate them is to synthesize InSAR, stereo imagery and lidar waveform. Theoretical model is needed for this effort. In this study, a unified model was presented, which can be used to simulate InSAR, stereo imagery and lidar waveforms over mountainous forest landscapes.

Due to the cloud coverage in optical remote sensing data, it is challenging to have a cloud-free image over large region. These inevitable cloud/cloud shadow pixels bring invalid patches in the derived thematic maps, which usually labeled as cloud and cloud shadow types. We use a pixel-level image composition method to generate cloud-free image. For each invalid patch in the thematic map, an enlarged area is extracted in the thematic map and composited image. Then the training data are generated automatically using the thematic map surrounded the invalid patch. The Random Forest (RF) method is used to classify the composited image. The new classification result is used to fill the invalid patch in the thematic map. This method is tested and evaluated in forest cover map in selected sites of the Greater Mekong Subregion (GMS). The result shows it is an efficient way to produce a cloud-free forest cover map for further analysis and applications.

Forest canopy closure (FCC) is an important factor to assess the quality of forest resources, and to understand the characteristics of forest change, which supports forest ecosystem management. The Chinese high-resolution satellite-2 (Gaofen-2, GF-2) image covering the Genhe Forest Reserve located at the Great Khingan of Inner Mongolia was firstly segmented by object-oriented technology and then the local FCC was estimated by the support vector machine (SVM) based on the GF-2's spectral, normalized vegetation index (NDVI), texture and other auxiliary information. The FCC estimates from the airborne LiDAR point cloud data with high density were used for the cross-validation. The result showed that the coefficient of determination (R2) between LiDAR and GF2 results was up to 0.65 and the root mean square error (RMSE) is 0.12. It indicated that it is feasible to estimate FCC by using the GF-2 images based on the object-oriented classification method.

In this paper, we studied the nonparametric estimation approach of wheat biomass using C-band PolSAR data. We focus on a crucial step of the estimation process, which is feature combination selection. Firstly, the original feature pool was acquired using PolSAR data. Then, using genetic algorithm (GA) as searching engine we selected the feature combination which has the best performance in the estimation model. Finally, the wheat biomass was estimated based on the features combination selected by GA. The experimental results showed that the selected feature combination by GA can outperform the original feature pool and the feature combination selected based on Pearson correlation coefficient (PCC) between feature and biomass.

WCM (water cloud model) developed in 1978 was proved useful for vegetation parameters estimation. However, its no sense of polarization dependence limited its application with quad-polarimetric data. In this study, we developed polarimetric water cloud model (PWCM) and applied to quad-polarimetric GF-3 data for winter wheat biomass estimation. (HH-VV)/HV, (HH+W)/HV, HH/VV and VOL (volume scattering from Freeman Durden decomposition)/(ODD (odd scattering or surface scattering)+DBL (double bounce scattering)) were used as surface volume ratio parameters and provided in the developed PWCM. The results showed the effectiveness of developed PWCM for crop biomass inversion. Among all the parameters, (HH-VV)/HV showed best performance for winter wheat biomass inversion. The RMSE is 157.58 g/m2, the relative accuracy is 63.88.

The Ice, Cloud, and land Elevation Satellite (ICESat)-2 is the next generation of National Aeronautics and Space Administration (NASA)'s ICESat mission launched in September 2018. The new photon-counting LiDAR onboard ICESat-2 introduces new challenges to the estimation of forest parameters and their dynamics, the greatest being the abundant photon noise appearing in returns from the atmosphere and below the ground. To identify the potential forest signal photons, we propose an approach by using a local outlier factor (LOF) modified with ellipse searching area. Six test data sets from two types of photon-counting LiDAR data in the USA are used to test and evaluate the performance of our algorithm. The classification results for noise and signal photons showed that our approach has a good performance not only in lower noise rate with relatively flat terrain surface but also works even for a quite high noise rate environment in relatively rough terrain. The quantitative assessment indicates that the horizontal ellipse searching area gives the best results compared with the circle or vertical ellipse searching area. These results demonstrate our methods would be useful for ICESat-2 vegetation study.

To advance the utility of polarimetric synthetic aperture radar observations for extracting biophysical information about vegetation canopies, it is important to develop good understanding of the scattering mechanisms that give rise to the polarimetric scattering response and to apply it toward the development of effective decomposition models of the polarimetric covariance matrices. In this paper, we introduce a more rigorous approach to characterizing the volume scattering component of the three-component scattering model developed by Freeman and Durden. The improved rigor has two aspects: 1) the use of a T-matrix model for computing the polarimetric scattering by an inclined dielectric cylinder and 2) the incorporation of the change in local incidence angle as a function of the orientation of the vegetation cylinders. The improvements address some of the current limitations of the Freeman-Durden model. This paper also provides a sensitivity analysis of the various components of the covariance matrix as a function of several physical parameters including cylinder size, its dielectric constant, and the orientation distribution of cylinders. Such an analysis is a precursor to the development of improved inversion algorithms.

The future ICESat-2 is the next generation of NASA's ICESat (Ice, Cloud and land Elevation Satellite) mission scheduled to be launched in 2018. The new photon counting Lidar onboard ICESat-2 introduced new challenges to the estimation of forest parameters and its dynamics, the largest being the abundant photon noise in the atmosphere and below the ground. In order to investigate the potentials for vegetation study, this paper simulated this photon counting Lidar data using the Forest Light (FLIGHT) radiative transfer model and introduce a signal detection algorithm based on a LOFe indicator. The simulation results we produced for homogeneous and heterogeneous forest showed good similarity and the noise distribution. The result of signal detection we implemented demonstrated a higher accuracy than the circle distance search method. Future works will consider simulating more sensors combined with the field data to give a more quantitative analysis and testing the algorithm to other data with different noise levels.

In this study, a simple semi-empirical model based on random volume over ground model framework was used to estimate forest canopy height through single-pass TanDEM-X interferometric coherence data. Many studies have showed that SINC model could have good performance for forest canopy height inversion in relatively flat areas, this paper is aimed at assessing whether this method works well over complex terrain areas. The result showed that the inversion accuracy is influenced by local incidence angle and should be considered when mapping forest canopy height using SINC model.

Synthetic Aperture Radar (SAR) is able to image the Earth's surface in all weather conditions, regardless of whether it is day or night. It can provide high quality data for large-scale mapping. In recent years, the deep learning method, which has robust performance, can automatically extract hierarchic features of images with reduced manual participation, and is beneficial to the rapid implementation of large-scale mapping. Based on the theory of deep learning, we propose an end-to-end framework for the dense, pixel-wise classification of GF-3 dual-pol SAR imagery with convolutional highway unit network (U-net). U-net consists of a stack of learned convolutional highway unit that extract hierarchical contextual image features, and is a popular form of deep learning networks. A series of experiments show that the networks consider a large amount of context to provide fine-grained classification maps. The overall classification accuracy is 85.0%, Kappa is 0.803. The results are good enough to meet the needs of the mapping application.

Net primary productivity (NPP) is an important ecological indicator to evaluate ecosystem, and it is useful for land degradation assessment and monitoring. However, owing to dryland's particularity, retrieving vegetation properties from satellite remote sensing presents some significant challenges in sparse vegetation area. In this study, based on the wildly used time-series GF-1 data, the NPP estimation in sparse vegetation area was analyzed. Results showed that GF-1 data have high spatial and high temporal resolution characteristics, it is useful to distinguish land cover types in semi-arid areas based on NDVI time series data, the accuracy was 83.37% and Kappa coefficient was 0.79. Some key parameters of grassland were simulated and optimized based on CASA model. Compared with the measured data, the result was R2with 0.71, and results indicated that NPP estimation by GF-1 data based on the new parameters in semi-arid area is feasible.

Because of climatic change and human activities, sandification is becoming a serious threat to the sustainability of human habitation. The aim of this study, therefore, was to propose a method for sandy land detection based on mixed pixel decomposition; the dynamic change monitoring and assessment was then conducted. Results showed that the pixel purity index is a viable indicator for endmember extraction for sandy land detection via remote sensing by linear mixed pixel decomposition methods. Results showed that when the endmember proportion of sandy land accounted for > 50% of the total (except for the vegetation), a pixel would be detected as sandy land. The extraction accuracy was verified to be 86.42% by field data. Early-middle August was believed to be the most reasonable time to assess sandy land coverage based on vegetation coverage. The sandy land areas in 2005 and 2014 were 5524 km2and 4109 km2respectively, reduced by 25.6%. Under the governance of sandy land in the last ten years, the sandy land area declined continually, but some areas were still degraded to a worse status and need special care to protect.

In this study, we tested the performance of averaged Stokes parameters in rape biomass inversion over Radarsat-2 data. Five time series Radarsat-2 data were acquired and they covered the whole rape growth season. At each growth stage, 16 Stokes and its child parameters were extracted to analyze their sensitivity to rape biomass. When it comes to biomass inversion, random forest (RF) algorithm were chosen for the rape biomass inversion. The results showed that the total scattered intensity g0 has highest coherence with rape biomass during the whole growth cycle. R2 between them was 0.704. Other parameters including degree of polarization m, the degree of depolarization (1- m), the degree of linear polarization (Pl) and the linear polarization ratio (Uc) showed secondary higher coherence with R2 between 0.3 to 0.4. These parameters also showed higher importance for the parameter importance analysis with RF method. RF method showed potential performance for rape biomass inversion with R2 of 0.585 and RMSE of 71.86 g/m2.

In this paper, we studied the synergetic estimation approach of forest above ground biomass (AGB) based on the multidimensional SAR data (dual antenna X-band InSAR and P-band PolSAR data) that acquired by air-borne CASMSAR system of China. Firstly, the high-resolution DSM data of the experimental area was acquired from the X-InSAR data. Then, based on the filtered DSM, the terrain correction of P-PolSAR data and X-InSAR coherence was completed. Finally, forest AGB was estimated based on the characteristics of multi-dimensional SAR that after the terrain correction. The experimental results showed that the combined multi-dimensional SAR features can obtain higher estimation accuracy than the single-dimensional SAR features. Compared to only using P-PolSAR features and X-InSAR coherence feature, the accuracy of combined estimation approach was improved by 6.4% and 5.1%, respectively.

In this paper, we focus on the topographic effects of InSAR interferometric land use (ILU) images and a corresponding terrain correction method is proposed. Firstly, Effective scattering area correction and angular variation effect correction are performed for intensity information. Secondly, for the coherence information, a novel difference equation method of terrain correction based on the SINC volume decorrelation model is proposed. The Tandem-X/Terrasar-X InSAR data and SRTM DEM data are used to illustrate the method. The results showed that terrain effects not only existed in the intensity image, but also existed obviously in the coherence image and can been effectively removed by the proposed method. Finally, the interpretability of the ILU imagery treated in this manner is improved in comparison to the uncorrected case.

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-28d-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-2d-1for GPP, and R2= 0.61 and RMSE = 1.17 gC m-2d-1for 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-2year-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.

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.

Recently, multi-source remote sensing data and their derived features such as vegetation indices, texture metrics have been frequently applied to quantitatively estimate forest above-ground biomass (AGB). However, it is still challenging to efficiently select the optimal features for modeling the forest AGB. In this study, a fast, efficient and automatic method has been proposed, called as k-nearest neighbor with fast iterative features selection (KNN-FIFS). This method iteratively pre-select the optimal features which determined by the minimum root mean square error (RMSE) between the forest field data and the k-nearest neighbor (k-NN) estimates based on the leave-one-out (LOO) cross-validation. By use of KNN-FIFS and multisource data, including Landsat-8 OLI (operational land imager) and its vegetation indices, texture metrics, HV polarization of P-band Synthetic Aperture Radar (SAR) data (PHV), and forest inventory data, were applied to estimate forest AGB over Genhe forest reserve located in Inner Mongolia, China. Afterwards, the model behaviors between KNN-FIFS and stepwise multiple linear regression (SMLR) methods were compared, which showed that the KNN-FIFS method (R2= 0.77 and RMSE = 22.74 t·ha-1) was superior to the SMLR method (R2= 0.53 and RMSE = 32.37 t·ha-1).

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.

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.

This study investigated forest changes over Jinhe town of Inner Mongolia among 2001, 2004, 2008, 2013 and 2015 by use of multi-temporal Landsat Thematic Mapper-5 (TM), Chinese high resolution satellite data, Gaofen-1 (GF-1) remote sensing and forest inventory data. Vegetation fractional coverage (VFC) was extracted by the dimidiate pixel model. Forest above-ground biomass (AGB) was estimated by k-nearest neighbor (k-NN) with fast iterative features selection (KNN-FIFS). The results showed that, due to the serious forest fire disaster which taken plaihquce in 2003, the local VFC declined in the high level (60%2, accounting for 17.90% of the total area. Further analysis for the years after the fire showed that the forest recovered well, with the high level areas increasing to 2977.99 km2in 2015. The total forest AGB was increased from 1,792,900 tons in 2008 to 1,903,000 tons in 2013, with an average annual growth of 300,200 tons.

Simulation of forest carbon fluxes over Greater Khingan based on strategy of model integration and data assimilation from 2000 to 2012 was conducted in this study. Original MODIS MOD_17 GPP (MOD_17) model was optimized over Greater Khingan using downscaled meteorological data, optimized light use efficiency, and refined GLASS fPAR products. Afterwards, representative forest plots for different forest types were selected for the integration of the optimized MOD_17 model and Biome-BioGeochemical Cycles (Biome-BGC) model. Through the model integration, the process-based model (Biome-BGC) was calibrated and then was applied to simulate the forest carbon fluxes over Greater Khingan. Meanwhile, time-series refined 8-day Global LAnd Surface Satellite (GLASS) LAI were assimilated into the calibrated Biome-BGC to improve the model performances and alleviated the uncertainties. Forest carbon fluxes (GPP, NPP, NEE) were estimated over Greater Khingan from 2000 to 2012 and NPP estimates were validated using tree ring data. Lastly, spatial statistics and trend analysis of NPP were carried out, and results showed that NPP tended to slightly increase during the study years.

In this study, 5 compact polarimetric (CP) data were simulated from 5 consecutive fully polarimetric images, which covered the whole rape growth period. Four groups including 25 CP parameters were extracted from each CP image at different rape growth stages, respectively. With the analysis of the relationships between CP parameters and LAI, g3in stokes parameter group, Ucin stokes child parameters group, σRRin backscattering parameters group and PVαin decomposition parameters group were chose to inverse rape LAI. The results showed that, among these four CP parameters, PVαperformed best for rape LAI inversion, the determination coefficient R2 between inversion results and the ground truth data is 0.917, root mean square error (RMSE) is 0.36.

Two extraction models applied in estimating forest height and above-ground biomass (AGB) were developed using the X-band Interferometric Synthetic Aperture Radar (InSAR) data, which was acquired from the China airborne SAR system in 2013 covering part of forested area in northeast China. The models using multi-passes InSAR data for the estimations of forest height and AGB were introduced respectively with detailed ground true data derived from both forest plot data and airborne Light Detection And Ranging (LiDAR) data. The estimation results indicated that it is feasible to estimate forest height using X-band InSAR data without the external DEM. Moreover, the model using multi-passes InSAR data could descript the forest structure characteristics and can be used to estimate forest AGB faithfully.

Desertification and restoration could be assessed by long time-series vegetation index at regional scale. In this study, a new approach for desertification assessment based on analysis of NPP and MNPP change derived from the time-series MERIS NDVI data was promoted. Desertification and restoration in Xilin Gol League, Inner Mongolia, China were assessed and monitored during 2003 to 2011 by using the new method. Results showed that the desertification is severe in study area, 17.2% of lands were suffered from desertification in Xilin Gol League during 2003 to 2011. The ecological engineering projects implemented in the study area have achieved significantly, especially in the Otindag Sandylands.

Forest vertical canopy cover (VCC) is an essential factor to be considered during forest resource inventory, sub-compartment division and thinning intensity. In order to monitor the forest canopy structure changes over the Greater Khingan, two temporal (2005 and 2010) forest VCC are derived by inverting the Li-Strahler geometric-optical model (GO) based on the extracted endmembers. The results show that the estimated VCC based on GO model agrees well with the measurements from the national forest inventory data with R20052=0.64, MSE2005=0.32%; R20102=0.74, MSE2010=0.25%.

Forest cover will be changed by natural disaster, deforest and other activities. Remote sensing has the ability to monitor the change of forest cover in large area. However, in order to obtain the change information of forest cover, the method for getting the change information from remote sensing images is needed. China has launched GF-1 satellite in 2013, the 16m spatial resolution multi-spectral data of it should be suitable for forest cover detection applications. In this paper, I describe a set of procedures that automate forest cover change detection using a pair of GF-1 images. The proposed method was tailored to work with high spatial resolution images acquired over forest in large area. To achieve a high level of automation, automatic optimum threshold selection algorithm were applied in the processing steps. In this study, an overall change recognition accuracy of 83% has been achieved.

Hyperspectral remote sensing data have great potential to identify ground objects and classify tree species. However, the tree species classification based on hyperspectral data in the subtropical region of hilly landscape have always been challenged by rugged topography. We conducted our research in subtropical mountainous forests in Pu'er of Yunnan province in southwestern China. This research investigated the capability of airborne AISA Eagle II hyperspectral data in tree species classification, especially in mountainous areas. Integrated Radiometric Correction (IRC) is applied as atmospheric and topographic correction technique; a classification algorithm was developed based on the topographic-corrected data. Principal Component Analysis (PCA) method was used to reduce the dimension of hyperspectral data before classifying tree species. The first three components indices combined with texture features were used for Support Vector Machine (SVM) classification. Study results demonstrated that this developed method obtained a good performance in detecting the target tree species for the overall classification accuracy is 95.14% and kappa coefficient is 0.93.

Forest is an important component of terrestrial ecosystems, so it is necessary to estimate the forest aboveground biomass (AGB) accurately in order to reduce the uncertainty of the carbon stock in forest ecosystem. In this study, a fast, efficient and automatic method has been proposed, called as Automatic KNN(AKNN). Using the Landsat 8 OLI data, airborne polmetric SAR (PolSAR) data, the AKNN has been applied to estimate the forest AGB and stem volume at two levels, the pixel and the subcompartment levels, respectively. The results showed that the power of AKNN in quantitative retrieval at two levels(R2=0.75, RMSE=23.43t/ha; R2=0.52 and RMSE=21.86m3/ha, respectively).

Forest above ground biomass (AGB) retrieval by TomoSAR technology has been preliminary studied over the last two years. Recent experiments have demonstrated that the backscattered power in HV channel at 30-m layer have strong correlation with the forest AGB, and be successfully used to build the estimation model. However, much progress remains necessary to make the best of the vertical distribution of the backscattered power. In this work, we proposed a multivariate regression equation to refine the AGB estimation model. The experiments were carried out on TropiSAR campaign data over the site of Paracou, French Guiana. As expected, the results demonstrate the relevance of the proposed method.

The goal of this paper is to investigate the influence of temporal decorrelation on InSAR / Pol-INSAR and T-SAR in boreal forest. The P-, L-band Pol-InSAR data collected in campaign BioSAR 2008 was used in our study. The impact of temporal decorrelation on InSAR / Pol-InSAR and T-SAR is reflected by coherences, phases and vertical backscattering power. Markov model is applied to describe the quantitative impact of time decorrelation, and correlation, time decorrelation constant is identified by GA. And the influence of temporal decorrelation on T-SAR is also related with coherences and phases. The backscattering power represents more ambiguous with longer time interval than that with shorter time interval for single baseline.

In this paper a method was demonstrated that GF-1 multi-spectral and MODIS multi-temporal NDVI data were integrated for forest land cover classification. The test site is located in the central of the Xiaoxing'anling region in Heilongjiang province where covered the area of one scene of GF-1 image. The random forests algorithm was adopted to select the best features automatically which contains spectral, texture and shape features from GF-1 multi-spectral data and phenological features from multi-temporal MODIS NDVI data. A decision tree was used to supervise the classification result. Experimental results show that the overall classification accuracy and Kappa coefficient of the developed method combing multi-sources data can reach 89.46% and 0.874 respectively, with significant improvement compared with that using either GF-1 multi-spectral data or MODIS NDVI time series data alone, especially for the classification of evergreen forest.

A campaign aimed at airborne multi-angle hyperspectral data collection was designed and implemented in Daxinganling of Northeastern China in the August of 2015. The purpose of this campaign is to investigate the potentials of multi-angle optical data for forest height and biomass estimation. The observation angles of 0°, 20°, 44° and 55° were used with 400-1000 nm spectrum range. To provide comparable information, airborne Lidar data were collected simultaneously. First results showed the collected angular information captured the Bidirectional Reflectance Distribution Function (BRDF) characteristics of forest spectrum. The relationship of lidar derived vertical parameters and multi-angle optical data are under further investigation.

China has a wide variety of forest types. It is challenging to make a reliable estimation of these forest aboveground biomass (AGB) using geo-spatial technologies. We developed a Field-Airborne-Spaceborne (FAS) comprehensive observation method for AGB estimation. According to forest ecological zones of China, we carried out three FAS campaigns in the Northeast, central, and Southwest of China. Airborne LiDAR data were collected along National Forest Inventory (NFI) plots. Then the airborne LiDAR data were used to estimate AGB after been trained by NFI plots. Then these LiDAR estimated AGB were used to train satellite data for large area biomass mapping. The stratified regression tree modeling method was used in this research. The overall estimation correlation coefficient are better than 0.8.

Vegetation fraction is an important index for sandy land evaluation, but it changes with time and precipitation obviously, especially the annual ephemeral plants. How to choose the best time for vegetation coverage to evaluate the sandy land degree is an imperative question. In this paper, soil adjusted vegetation index (SAVI), pixel dichotomy model and pixel unmixed model for multi-endmumber were applied to estimate the vegetation fraction based on GF-1 multispectral image, taking Zhenglan Banner in Inner Mongolia as a study area. By validated from filed data, the pixel unmixed model for multi-endmumber was superior to the others, with the lowest RMSE. The vegetation fraction change was analyzed in growing seasons from May to October, taking the precipitation into consideration. It was shown that vegetation in sandy land grown steadily by the middle of July, but it appeared to grow much rapidly after this period due to the effect of precipitation on annual ephemeral plants. In the later period, the plants began to be restrained and became placid until August because of temperature reduction and lack of water. In order to reduce the unsteady effect of annual ephemeral plant, early-middle August was thought as the most reasonable time for vegetation coverage to evaluate the sandy land degree.

A strategy of data assimilation using the refined remote sensing product for the process-based model (Biome-BGC) in order to improve the simulated carbon fluxes was proposed. Firstly, we applied the optimized the remote-sensing-based MODIS MOD_17 GPP (MOD_17) model to calibrate the process-based Biome-BGC model. This incorporation strategy for the parameterization of Biome-BGC has been proved to be more reliable in carbon fluxes' simulations. The calibrated Biome-BGC model agreed better with the Eddy covariance (EC) measurements (R2=0.87, RMSE=1.583 gC/m2/d) than the original model (R2=0.72, RMSE=2.419 gC/m2/d). Afterwards, two years (8-day during 2003 and 2004) Global LAnd Surface Satellite (GLASS) LAI products were applied to test the data assimilation procedure for the calibrated Biome-BGC using Ensemble Kalman Filter (EnKF). The results indicated that simulated LAI through assimilation agreed better with GLASS LAI, and the carbon fluxes are hoped to improve by further adaption of the filter.

The radiometric quality of polarimetric SAR (PolSAR) image is affected by terrain undulations due to 1) the variation of effective scattering area, 2) the variation of scattering mechanisms, and 3) the variation of polarization states. This paper proposed a three-stage terrain correction method of PolSAR data for the impact of above three aspects. PALSAR-2 PolSAR data and LiDAR forest AGB data are used to illustrate the method. The results showed that terrain effects can been effectively removed and the correlation between forest biomass and backscatter coefficient are improved after three-stage terrain correction. The HV polarization has the best correlation with the forest AGB (R = 0.75) and the correlation coefficient was increased about 0.3 compared with uncorrected case.

The objective of this paper is to derive DEM and DHM in tropical forests based on the technology of multi-baseline InSAR tomography, focusing on the requirement of the minimum number of flight tracks. The experiments were carried out on P-band HH polarization airborne multi-baseline InSAR data over the site of Paracou, French Guiana, during the European Space Agency campaign TropiSAR 2009. Tomographic processing was carried out by Capon spectral estimation technique with three-track observations, subsequently two elevation values with respect to the relative maximum peaks were retrieved representing DEM and DSM respectively. As expected, the retrieved DEM and DHM are found agreements with LiDAR measurements.

The Greater Mekong Subregion (GMS) is rich in forest resources. It is important to estimate forest carbon with high accuracy methods in this region. Remote sensing is an efficient way to estimate forest parameters in large area, especially at regional scale where field data is scarce. LIDAR provides accurate information on the vertical structure of forests. In this study, the forest carbon was estimated at ICESat GLAS footprint level after being trained with field measurements and airborne lidar estimations in GMS. According to different types of ecological zones, a set of categorical regression models was built between ICESat GLAS estimates and ENVISAT MERIS spectral variables together with MODIS VCF product. Then the forest carbon map with continuous value was generated. The estimation agreed well with FAO FRA 2010 report and other published results, and the average difference was about 13.3%.

In this letter, a fully automatic method to resolve range ambiguities for high-repetition-rate airborne laser scanner data is introduced. The method combines position and orientation system data with a rough digital elevation model to make adaptive predictions for the range intervals of laser pulses and is compatible with a wide range of LiDAR systems. Results show its capability in avoiding the intrinsic constraints of multiple pulse repetition rate techniques in state-of-the-art commercial ALS systems while retaining efficient computational performance and robustness under complex terrain circumstances.

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.

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).

The aim of this paper is to analyze the feasibility of hemi-boreal forest above ground biomass (AGB) estimation based on Tomo-SAR technique. Repeat-path multi-baseline P-band Pol-InSAR data collected during March and May, 2007 in Remningstorp test site is used. The result shows that the correlation coefficient (R) of P-band HH backscattering coefficient reaches 0.87 with the in-situ forest biomass. The R of P-band VV backscattering power at 5m and 10m is 0.71, 0.72 with the in-situ forest biomass, respectively.

An approach was used to incorporate the forest carbon flux for Qilian Mountains by ecological-process-based model (Biome-BGC), and remote-sensing-based model (MODIS-PSN). The calibration phase, aiming at setting the ecophysiological parameters to effectively simulate the daily GPP behavior of the Qilian Mountains, was proceeded by adjusting the 8 day GPP outputs obtained from Biome-BGC using the optimized MODIS-PSN algorithm and the observations. The results showed that the optimized MODIS-PSN could describe the GPP behavior faithfully comparing to the eddy covariance-observed GPPs, with R2=0.77, RMSE=6.219gC/m2/8d. After validation, the calibrated Biome-BGC has been proved to estimate the performances of daily GPP behavior effectively comparing to the eddy covariance-observed GPPs, especially in summer and winter (with R2= 0.76, RMSE= 1.3115gC/m2/d), which illustrated that the combination of Biome-BGC and optimized MODIS-PSN could express the carbon fluxes well over the Qilian Mountains.

In this paper we analyze the effects of polarization, environmental conditions and forest structure upon the backscatter response of forested stands. This analysis is based upon a time series of ALOS-1 PALSAR images acquired over our study site in Xunke County, Heilongjiang Province, China. Based on six scenes, we analyzed the polarization and environment conditions on the forest stands. Backscatter coefficients of HV channel had a greater dynamic range than HH channel. HV channel was less influenced by weather and wind speed conditions. Our observations found canopy density greatly influenced the forest stand backscatter. Backscatter coefficient showed weak correlations to canopy density, mean tree height and mean diameter at breast height (DBH). Correlations were much stronger when the forest stands were grouped with canopy density or mean tree height. Before grouping the highest correlation coefficient between backscatter coefficients and tree height was 0.377, the value for HV image acquired on August 07, 2007. After grouping these forest stands by canopy density, the correlation was 0.95 to tree height. We also analyzed the correlations with two different tree species, and obtained similar results.

This study explores retrieval of wall-to-wall forest above-ground biomass (AGB) over Jilin province in Northeast China, using the optimized non-parametric k-NN method, the 7thNational Forest Inventory (NFI) data, and the remote sensing data: Landsat-TM/ETM+ images. For pixel-based validation, the estimated result was compared to the NFI data by leave-one-out process and R2= 0.40 and RMSE = 54.29 tons/hm2. For county-scale validation, the result was verified by the intensive forest sub-compartment data of eight county and R2= 0.80 and RMSE = 34.26 tons/hm2.

A campaign consists of in-situ measurement, airborne Lidar data and spaceborne data fusion was designed and implemented in Daxinganling of Northeastern China. The purpose of the campaign is to collect field and multi-sensor data to produce spatial explicit map of forest aboveground biomass for the entire region. This paper presents preliminary results of ABG mapping using field data, airborne lidar, ICESat GLAS, MODIS and ENVISAT MERIS data. The AGB map from multi-sensor fusion had a good performance with 8.1% difference after comparison with the estimated AGB in the Daxinganling Ecological Research Station.

In this paper, we compared two versions of the Vegetation Change Tracker (VCT) algorithm, one based on the Integrated Forest Index (IFI) and the other on Disturbance index (DI) derived using the tasseled cap transformation. Time series Landsat ETM+ data from 1999 to 2010 were used in a tropical site of Xishuangbanna, Yunnan province of China. The results were evaluated using 376 field plots. The accuracies of IFI-VCT and DI-VCT are 82.7% and 79.9%, respectively. These two algorithms had different performances in different areas. The DI enhanced the separability between some tall agriculture and forest. Haze and seasonality affect DI result more than IFI.

Compared with traditional remote sensing, multiangular observation provides 3-D structural information of a forest through different directional observations. The MGeoSAIL model, suitable for multiangular observations, was developed based on the single-angle model GeoSAIL. The MGeoSAIL model combines the geometric-optic model with the radiation transfer model and has the advantages of both models. Thus, it is more accurate and feasible. The geometric-optic model calculates the amount of shadowed and illuminated components within a forest scene, while the radiation transfer model [Scattering by Arbitrarily Inclined Leaves (SAIL)] calculates the reflectance and transmittance of tree crowns. The uniform index is introduced to characterize the relationship quantitatively between tree distribution pattern and the bidirectional reflectance distribution function (BRDF). The simulation results show that the MGeoSAIL model could simulate the “hot” spot in red and near-infrared bands, as well as the “bowl” shape in the near-infrared band. The relationship between the uniform index and BRDF is negatively exponential. Finally, the look-up table was calculated using the MGeoSAIL model, and leaf area index (LAI) was inversed from compact high-resolution imaging spectrometry data. The results compared well with the measured LAI in Changbai Mountain area, China.

In order to improve the land cover classification accuracy for SAR image, Support Vector Machine (SVM), which has wide applicability is used on the land cover classification of POLSAR image in this paper. The study site is located in Tahe County, Heilongjiang Province, China, and two scenes of quad-polarization Radarsat-2 SAR images were acquired. the land cover classification of single-temporal POLSAR image by SVM, and multi-temporal POLSAR image by SVM and maximum likelihood classification (MLC) is studied separately. Then all the classification results are evaluated. Some conclusions can be got according to the analysis of all results and accuracy: Firstly, it is difficult to distinguish the different types of vegetation for the similar scattering among them in July. However, water, whose scattering characteristic is simplex, can be distinguished from others easily. Scondly, in October, the scattering characteristics among forest, shrub, grass, crop are different, therefore it is easy to distinguish vegetation because of their one from others in this period. But for water, with reduced in winter, the river width narrows, compared with it in summer, water classification accuracy is lower in this period. Thirdly, joint July and October SAR data for classification, can offset espective their own disadvantages. and improve overall accuracy. And the last one, With the characteristics that different probability density distribution, small sample, non-linear and so on, SVM shows the wide applicability.

The main objective of this study was to investigate the potential of using Support Vector Machines (SVM) and Random forest (RF) to estimate forest above ground biomass (FAGB) by using multi-source remote sensing data. To do so, we introduced a basic flow of SVM to estimate FAGB from multisource remote sensing data. RF method was adept at identifying relevant features having main effects in multisource remote sensing data. Results show that: (i) In the stage of feature selection, the Random Forest model provide better results compared to the typical F-scores method. (ii) The optimal SVM model, based on the selection of features clearly demonstrate that the estimation accuracy increased by feature selection algorithm. (iii) Compared to the optimal KNN, BPNN and RBFNN model, the optimal SVM algorithm provided more accurate and robust result on the considered case.

The objective of this study is to estimate forest above ground biomass (AGB) of the whole areas covered with forest basing on Polarization coherence tomography (PCT) and polarimetric interferometric segmentation. Two scenes of DLR E-SAR L band quad-polarization images were acquired in Traunstein test site. Results show that there is no saturation using this method even when the biomass ups to 500tons/ha and parameters extracted from PCT is easier to implement in practice.

Canopy height model (CHM) is an important model of LiDAR application in forestry. The quality of CHM directly or indirectly influences the subsequent forest parameters extraction and estimation. However, some abnormal changes of elevation exist in CHM regularly. This study tried to get crown area in CHM ignoring the negative influence from these changes using morphological crown control. The morphological crown control determined crown areas by a morphological closing operator with two thresholds. The results showed that our method could give the crown areas well. The thresholds' setting was discussed and they were mainly decided by the crown base height of the tree in object area and the CHM's resolution.

Extraction of vegetation water content and soil moisture from microwave observations requires development of a high fidelity scattering model. A number of factors associated with the vegetation canopy and with the underlying bare soil should be taken into account. In this paper, we propose an electromagnetic scattering model for a corn canopy which includes the coherent effect due to the corn structure and takes advantage of recently advanced scattering models for dielectric cylinder of finite length and for rough surface.

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.

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.

Large footprint lidar has demonstrated its great potential for accurate estimation of many forest parameters, e.g., forest height, forest biomass and vertical structure of forest canopy. In addition to the canopy vegetation, many factors such as atmosphere, underlying surface, the shape of crown, et al., influence the lidar waveform. The illuminating intensity of the laser beam across the lidar footprint is a Guassian distribution and reduces from 1.0 to e-2 from the center to the edge of the footprint. Hence the forest stand structure plays an important role in the lidar waveform. The contribution of each tree to the lidar waveform varies with its location in a forest stand. This work simulated the random, uniform and clumped tree distribution patterns in a stand. Then waveforms were simulated using a three dimensional lidar waveform model developed by Sun and Ranson. The results show that the tree distribution patterns affect the lidar waveform profiles. The area (or energy) under the waveform from vegetation (AWAV) and the height of median energy (HOME) were used to estimate the effects. Following trends have been revealed from the simulation: for AWAV and HOME, uniform > random > cluster. There is no obvious difference between regular and random. The waveform area (AWAV) varies much more than HOME. For the clumped case, the number of clusters does not have much effect on the lidar waveform.

Average height of forest stand is an important parameter related to forest ecological function and carbon cycling. Estimation of stand height using remote sensing data becomes possible due to the new technologies. Interferometry synthetic aperture radar (InSAR) technology uses the phase information of radar returns to get three-dimensional information of the earth surface. The existence of trees in a radar pixel modifies the phase information, so InSAR data has been studied for its potential of tree height estimation. In this study, the C and L-band InSAR data acquired during STR-C/X SAR Mission (1994) were used to estimate average tree height of forest stands in the Daxinganling forest region in Northeast China. The DGPS is used to locate each forest sample plot and the tree heights were measured in 1999. Tree heights in 1994 were deduced from the 1999 field measurements and the tree growth table. The results from C and L bands were compared. Following conclusions were drawn from the study: (i) Stand height information can be acquired from the digital surface model (DSM) generated from InSAR data, (ii) If the forest stand is very uniform in terms of tree height and density, the C-band InSAR data gives better result than L band. Otherwise, the results from L-band InSAR data are more stable, (iii) Multi-temporal InSAR technology may be used to monitor stand height increment.

The forest fire during May 6 - June 4, 1987 in Northeast China burned 1.14 million hectares of forests and nearly 25 million cubic meters of timber. The land cover has changed dramatically during the last 15 years. In this study, the Landsat-5 TM, Landsat-7 ETM+ data and JERS-1 SAR data were used to analyze land cover change and forest recovery from burned scar in Changqing Forest Farm of this region. The post-classification change detection method was used. The results from remote sensing data were compared with forest maps based on field inventory. Comparisons of the classification results of 1987 and 2000 Landsat images have revealed several distinctive land cover change patterns: most of the burned scars have recovered and become either forests or shrubs, but some of the scars have turned into other land use types. The NDVI of some recovered areas are even higher than unburned forests. But the timber volume inferred from JERS-1 SAR data shows that even for the best recovered area, the volume is still much less than the unburned forests. The results show that Landsat data can be used to monitor land cover change, but is not sensitive to forest timber volume. On the other hand, L-band SAR data can be used to separate different forest volume levels, but it is not easy to be used to identify different land cover types. Therefore, combined use of Landsat TM and L-band SAR data has high potential to monitor land cover changes and forest recovery after fire.