Accurately estimating forest component above-ground biomass is crucial for understanding and analyzing the growth process of vegetation ecosystems, interpreting the ecosystem carbon cycle correctly, and improving the effectiveness of forest management. It also provides ideas for the bottleneck problem of low saturation point in forest aboveground biomass (AGB) estimation using remote sensing techniques. This paper extracts a large number of remote sensing features from optical GF-1 and GF-3 SAR data. A feature optimization inversion model named KNN-FIFS (a fast iterative procedure embedded in K-nearest neighbor) was applied here for component and total forest AGB inversion. Four forest types including Yunnan pine, Simao pine, broadleaf forest, and mixed conifer and broadleaf forest were involved in this study. The results indicate that the summation of the estimated each component AGB for total forest AGB estimation showed higher inversion accuracy than the total AGB estimated directly by remote sensing features. The relative RMSE difference between them was about 3%. The results also revealed that through forest component estimation for later total forest AGB estimation can help improve the saturation points in forest AGB estimation using remote sensing datasets.

In this paper, we took GF-2 and GF-3 as datasets, introduced an image fusion method named HIS(Intensity-Hue-Saturation)-NSST (Nonsubsampled Shearlet Transform) +PCNN (Pulse Coupled Neural Network) method to fuse the different band combination of the GF-2 and 7 features extracted from GF-3 images, respectively. Then we use a fast iterative procedure embedded in K-nearest neighbor (KNN-FIFS) to estimate the forest AGB by the features extracted from original GF-2, GF-3, combination of GF-2 and GF-3, and fused images of GF-2 and GF-3. Their performance on forest AGB estimation were analyzed and compared.

Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Currently, polarisation information shows great potential for reducing saturation problems and improving estimation accuracy. 137 SAR features including backscatter coefficients, texture characteristics and features extracted from H/A/a decomposition and so on 9 decomposition methods were extracted for L-band airborne PolSAR data at two test sites, respectively for forest L-band scattering mechanisms analysis and AGB estimation. A multiple linear stepwise regression (MSLR) model and a fast iterative feature selection for K-nearest neighbor (KNN-FIFS) method are used to estimate the forest AGB at the two test sites. In the present study, there was evident site dependence of the L-band forest scattering mechanisms, while KNN-FIFS performed better in the estimation of forest AGB. The best AGB estimation was acquired at the Hainan test site with RMSE = 28.88 t/ha and rRMSE = 18.46%.

Forest biomass plays an essential role in forest carbon reservoirs studies, biodiversity protection, forest manage-ment, and climate change mitigation actions. Parameters extracted from Light Detection and Ranging (LiDAR) and X-band Synthetic Aperture Radar (SAR) data were used in separately and in combination to estimate total forest above-ground biomass (AGB), but rarely used in components AGB estimation. In this paper, we extracted intensity, density, and height parameters from LiDAR data, coherence coefficients from Interferometric SAR (InSAR) data, backscatter coeffi-cients and polarimetric decomposition parameters from Po-larimetric SAR (PoISAR) to estimate forest total and components AGB. The results showed that PolSAR parameters have a unique advantage to estimate leaf biomass, with the highest R2 of 0.773. And for total, bark and branch AGB, LiDAR, InSAR and PolSAR parameter combination have better accuracy, with R2 of 0.818, 0.834, and 0.842, respec-tively. The study revealed that LiDAR and SAR used in combination can effectively estimation the forest total and components AGB.

Quantifying the uncertainty of forest height estimation is crucial for accurate global carbon computation. Forest height have been estimated by random volume over ground (RVoG) model using polarimetric interferometry synthetic aperture radar (PolInSAR) data in recent two decades. By far, RVoG derived forest height uncertainty estimation has been limited to comparison against “true” validation data, which then lead to the impossible use of uncertainties analysis at a national, continental or global landscape. In this paper, Bayesian theorem were introduced to RVoG forest height estimation procedure and used to estimated the uncertainties of the estimated forest height. The results revealed the feasibility of Bayesian method in forest height uncertainties estimation.

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 biomass plays an important role in restraining global warming and protecting ecosystem. With the development of ALOS SAR satellite and the improvement of its sensor performance, it plays a more and more important role in quantitative retrieval of forest biomass. In this paper, we used KNN-FIFS, KNN, SVR and Multiple Regression Models to invert the typical forest biomass of Genhe in Inner Mongolia and Yiliang in Yunnan based on ALOS1 PALSAR1 and ALOS2 PALSAR2. The results show that: 1) ALOS-2 PALSAR-2 has better inversion effect than ALOS-1 PALSAR-1;2) KNN-FIFS has better inversion accuracy than KNN, SVR and multiple regression function.

With the continuous acceleration of urbanization, the construction land around Dianchi Lake has been expanded, which may result in serious surface deformation and produce adverse environmental impacts. To explore the influence of city expansion on ground subsidence in Kunming, in this paper, we used the SBAS-InSAR technique to invert the time series deformation velocity of Kunming by sentinel-1A datasets acquired from October 2018 to October 2019. The results showed that an average subsidence velocity up to-30mm/year at the north bank of Dianchi Lake. The deformation velocities extracted from descending and ascending datasets show similar results with R2equaling to 0.9677. The results showed the potentiality of SBAS-InSAR technology and provides a more effective monitoring method for surface subsidence in mountain areas.

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.

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.

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.

This paper presents an augmenting path based online max-flow algorithm. The proposed algorithm handles graph changes in chunk manner, updating residual graph in response to edge capacity increase, decrease, edge/node adding and removal. All possible graph changes are abstracted into two key graph changes, which are capacity decrease and in- crease. For capacity decrease, we release the occupied capacity by cycle cancellation and path de-augmentation to enable the capacity decrease. For capacity increase, we augment all s-t paths newly formed to update the current max-flow model. The theoretical guarantee of our algorithm is that online max- flow is always equal to batch retraining. Experiments show the deterministic computational cost save (i.e., gain) of our algorithm w.r.t batch retraining in handling graph edge adding.

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 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.

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).

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.

With the advantages of the simpler transmitter architecture requirements, the wider swath capability and lower data rate, compact polarimetric (CP) synthetic aperture radar (SAR) was proposed in recent ten years to substitute or compensate the disadvantage of the full polarization mode SAR, especially on widening the swath width. CP mode is transmitted with one polarization and received with both of them. As there are only two channels, the decomposition methods and theories which were used for full polarization can not directly apply into it. According to the characteristics of CP mode, the decomposition theory based on partial polarized waves were developed, like the degree of polarization and phase difference (m - δ) decomposition, the degree of polarization and scattering angle (m - α), the degree polarization and the Poincare ellipticity parameter (m - χ) decomposition. Since α and χ are mutual complementary angle, the result of these two decomposition is same. Since the main purpose of decomposition is to classify the different objects, this paper focus on the classification result difference of m - δ and m - α, the interpretation of these results and how to improve the classification based on the better decomposition results. In this paper, the classification results of these two decomposition methods were compared with full polarization classification result. The classification overall accuracy of m - α is 97.17%, whereas m - δ is 88.28%. The kappa coefficient for the former is 0.8500, the latter is 0.8207. Since the volume scattering component is same, the differences were caused by surface scattering component and dihedral component. The statistics of these two components shown that α has better accumulativeness than δ, which lead to the higher classification accuracy. Both of these two method result in higher assessment of volume scattering.

Land degradation in Inner Mongolia, China is much severe. Remote sensing application on land degradation assessment can provide scientific basis for land degradation prevention in the study area. In this paper, land degradation was assessed by applying two improved relative Rain Use Efficiency (RUE) indicators based on time series MODIS NDVI data and high-resolution meteorological data from 2001 to 2010. The results show that 76.74% land of the whole study area with good or unusually good condition, it indicates that the most areas have normal or good vegetation production capacity. The unusually degraded and degraded lands account for 11.94% of the study area, especially they are less degraded lands distributing in Beijing and Tianjin sandstorm source region within the Inner Mongolia, it indicates that some ecological engineering projects implemented in this area have achieved significantly for restoration of degraded ecosystems in recent 10 years.

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.