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Leaf area index (LAI) is an important forest structural parameter in the process of photosynthesis. Most studies have investigated the estimation of forest LAI using the spectral information of optical remote sensing images or the height information from LiDAR data. This paper explored the estimation of forest LAI using canopy cover and forest height information extracted from stereo imagery acquired by cameras onboard an unmanned aerial vehicle (UAV). UAV remote sensing has gradually become practical in recent years. Two alternative methods were proposed to extract forest height information. The height indices method extracted forest height indices within each forest plot based on the vertical histogram of the canopy height model derived from stereo imagery, while the segmentation method characterized forest plots using tree numbers and average tree heights based on the individual tree segmentation. The results showed that canopy cover and forest height are complementary in the estimation of forest LAI no matter what method was used. The combined use of canopy cover and forest height information extracted by the segmentation method had a better estimation accuracy of forest LAI with R 2 = 0.833 and RMSE = 0.288. This paper demonstrated a new approach to predict forest LAI using UAV optical images.

Waveform broadening effects of large-footprint lidar caused by terrain slopes are still a great challenge limiting the estimation accuracy of forest aboveground biomass (AGB) over mountainous areas. Slope-adaptive metrics of waveforms were proposed in our previous studies. However, its validation was limited by the unavailability of enough reference data. This study made full validation of slope-adaptive metrics using data acquired by the Global Ecosystem Dynamics Investigation (GEDI) mission, meanwhile exploring GEDI waveforms on estimations of forest AGB. Three types of waveform metrics were employed, including slope-adaptive metrics (RHT), typical height metrics relative to ground peaks (RH), and waveform parameters (WP). In addition to terrain slopes, two other factors were also explored including the geolocation issue and signal start and ending points of waveforms. Results showed that footprint geolocations in the first version GEDI data products were shifted to the left forward of nominal geolocations with a distance of about 24 m~30 m and were substantially corrected in the second version; the fourth and fifth groups of signal start and ending points of waveforms had worse performance than the rest of the four groups because they used the maximum and minimum signal thresholds, respectively. Taking airborne laser scanner (ALS) data as reference, the root mean square error (RMSE) of terrain slopes extracted from the digital elevation model of the shuttle radar topography mission (SRTM DEM) was about 3°. The coefficients of determination ( R 2 ) of estimation models of forest AGB based on RH metrics were improved from 0.48 to 0.68 with RMSE decreased from 19.7 Mg/ha to 15.4 Mg/ha by the second version geolocations. The RHT and WP metrics gave the best and the worst estimation accuracy, respectively. RHT further improved R 2 to 0.77 and decreased RMSE to 13.0 Mg/ha using terrain slopes extracted from SRTM DEM with a resolution of 1 arc second. R 2 of estimation models based on RHT was finally improved to 0.8 with RMSE decreased to 11.7 Mg/ha using exact terrain slopes from ALS data. This study demonstrated the great potential of slope-adaptive metrics of GEDI waveforms on estimations of forest aboveground biomass over mountainous areas.

Sentinel-1 provides an extraordinary opportunity to explore the temporal behavior of backscattering of C-band synthetic aperture radar (SAR) due to its unique capability of successive observations every 12 days. This study reported new findings on the critical temporal features of Sentinel-1 backscattering over mountainous forested areas in northeast China and their application in regional forest mapping. Two interesting phenomena were discovered through the analysis of 450 scenes of images acquired by Sentinel-1A or Sentinel-1B over an area of 318,898.62 km2. The first phenomenon was that the dates of the largest drops of backscattering coefficients over forest and non-forest plots were different during the transition from autumn to winter. The largest drop of non-forest plots occurred around the date of the minimum temperature decreasing below 0 °C, while that of forest plots occurred around the date of the maximum temperature decreasing below 0 °C. The second phenomenon was that at the dates where these two drops occurred, the magnitude of the drop was negatively correlated with the forest canopy coverage for the first date and positively correlated for the second date. Based on these two phenomena, two methods for the forest mapping, referred to as the direct method and the indirect method, were proposed using only three dates of Sentinel-1 images, i.e., Date1: before the minimum temperature decreased below 0 °C, Date2: after the minimum temperature decreased below 0 °C but before the maximum temperature decreased below 0 °C, and Date3: after the maximum temperature decreased below 0 °C. The results showed that the overall accuracy of the forest map produced by the direct method was 93.60%, while that by the indirect method was 93.80%. Their accuracies were comparable with those of forest maps derived from publicly released land cover maps, which was approximately 94.42% for the best one. This study proposed a new way to do large-scale forest mapping in annually frozen regions using as few Sentinel-1 SAR images as possible.

Directly mapping impervious surface area (ISA) at national and global scales using nighttime light data is a challenge due to the complexity of land surface components and the impacts of unbalanced...

Forest biomass is an important for evaluating forest resources and optimizing efficiency in the forest industry. To improve our ability to estimate the structure parameter in the forest based on canopy-independent structure metrics, we used a suite of structural metrics that relate to three aspects of the forest biomass: DBH、tree height、forest density, and analyzed the relationships between structural metrics derived from airborne lidar scanner data and field measure data. The regression relationship between each structural metrics and mean diameter at breast height (DBH) was calculated for sites located at New York central park. The tree height had the weak correlations with mean DBH (R2=0.482), and the two canopy-independent structure metrics (rumple index, canopy volume) had the stronger correlations with mean DBH than tree height, R2 values were 0.516, 0.532 respectively. However, the correlations were significantly improved when the two canopy-independent metrics were introduced into regression. The canopy and trunk volume had the strongest correlations with mean DBH (R2=0.898), which included information such as tree height, canopy structure and forest density. Our results demonstrate that canopy-independent variables are useful explanatory variables for predicting forest biomass even if tree height can not be obtained.

International audience; Quantifying ecosystem carbon fluxes and stocks is essential for better understanding the global carbon cycle and improving projections of the carbon-climate feedbacks. Remote sensing has played a vital role in this endeavor during the last five decades by quantifying carbon fluxes and stocks. The availability of satellite observations of the land surface since the 1970s, particularly the early 1980s, has made it feasible to quantify ecosystem carbon fluxes and stocks at regional to global scales. Here we provide a review of the advances in remote sensing of the terrestrial carbon cycle from the early 1970s to present. First, we present an overview of the terrestrial carbon cycle and remote sensing of carbon fluxes and stocks. Remote sensing data acquired in a broad wavelength range (visible, infrared, and microwave) of the electromagnetic spectrum have been used to estimate carbon fluxes and/or stocks. Second, we provide a historical overview of the key milestones in remote sensing of the terrestrial carbon cycle. Third, we review the platforms/sensors, methods, findings, and challenges in remote sensing of carbon fluxes. The remote sensing data and techniques used to quantify carbon fluxes include vegetation indices, light use efficiency models, terrestrial biosphere models, data-driven (or machine learning) approaches, solar-induced chlorophyll fluorescence (SIF), land surface temperature, and atmospheric inversions. Fourth, we review the platforms/sensors, methods, findings, and challenges in passive optical, microwave, and lidar remote sensing of biomass carbon stocks as well as remote sensing of soil organic carbon. Fifth, we review the progresses in remote sensing of disturbance impacts on the carbon cycle. Sixth, we also discuss the uncertainty and validation of the resulting carbon flux and stock estimates. Finally, we offer a forward-looking perspective and insights for future research and directions in remote sensing of the terrestrial carbon cycle. Remote sensing is anticipated to play an increasingly important role in carbon cycling studies in the future. This comprehensive and insightful review on 50?years of remote sensing of the terrestrial carbon cycle is timely and valuable and can benefit scientists in various research communities (e.g., carbon cycle, remote sensing, climate change, ecology) and inform ecosystem and carbon management, carbon-climate projections, and climate policymaking.

As the largest among terrestrial ecosystems, forests are vital to maintaining ecosystem services and regulating regional climate. The area and spatial distribution of trees in densely forested areas have been focused on in the past few decades, while sparse forests in agricultural zones, so-called agroforests or trees outside forests (TOF), have usually been ignored or missed in existing forest mapping efforts, despite their important role in regulating agricultural ecosystems. We combined Landsat and PALSAR data to map forests in a typical agricultural zone in the North China Plain. The resultant map, based on PALSAR and Landsat (PL) data, was also compared with five existing medium resolution (30–100 m) forest maps from PALSAR (JAXA forest map) and Landsat: NLCD-China, GlobeLand30, ChinaCover, and FROM-GLC. The results show that the PL-based forest map has the highest accuracy (overall accuracy of 95 ± 1% with a 95% confidence interval, and Kappa coefficient of 0.86) compared to those forest maps based on single Landsat or PALSAR data in the North China Plain (overall accuracy ranging from 85 ± 2% to 92 ± 1%). All forest maps revealed higher accuracy in densely forested mountainous areas, while the PL-based and JAXA forest maps showed higher accuracy in the plain, as the higher omission errors existed in only the Landsat-based forest maps. Moreover, we found that the PL-based forest map can capture more patched forest information in low forest density areas. This means that the radar data have advantages in capturing forests in the typical agricultural zones, which tend to be missing in published Landsat-based only forest maps. Given the significance of agroforests in regulating ecosystem services of the agricultural ecosystem and improving carbon stock estimation, this study implies that the integration of PALSAR and Landsat data can provide promising agroforest estimates in future forest inventory efforts, targeting a comprehensive understanding of ecosystem services of agroforests and a more accurate carbon budget inventory.

Funding for Open Access provided by the UMD Libraries Open Access Publishing Fund., Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha−1 or above 50% of 150 Mg·ha−1 are detectable using cross-normalized SAR data.

Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%.

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges

The estimation of forest biomass from synthetic aperture radar (SAR) data is limited by the lack of forest structure information. Interferometric synthetic aperture radar (InSAR) provides a means for the extraction of forest structure. The crucial issue in InSAR application is to parameterize forest structure and to link the parameter with InSAR observations. Model-based analysis enables exploring the theoretical linkages between InSAR observations and forest structure free from temporal decorrelation effects. In this paper, a semicoherent model (SCSR) was first developed and verified. A series of simulations at L-band was then made for both homogeneous and heterogeneous forests generated from a forest growth model. The forest structure was parameterized by four height indices. Aside from the height of scattering phase center (HSPC), the depth of scattering phase center (DSPC) was also proposed to characterize the scattering phase center of InSAR. The results showed that the behavior of homogeneous forest on InSAR data was quite different from that of heterogeneous forest. Special care was needed when the retrieval algorithms of forest biomass developed on a homogeneous forest were applied to a heterogeneous forest. Crown size-weighted height (CWH) and Lorey's height were correlated with the HSPC at all polarizations and with the DSPC at copolarization in both cases of homogeneous and heterogeneous forests. These findings indicated that CWH could be an alternative biomass indicator of the Lorey's height for biomass estimation, which can be derived from the combination of InSAR data and the elevation of the forest canopy top from lidar or high-resolution stereo images.

LiDAR waveform data from airborne LiDAR scanners (ALS) e.g. the Land Vegetation and Ice Sensor (LVIS) havebeen successfully used for estimation of forest height and biomass at local scales and have become the preferredremote sensing dataset. However, regional and global applications are limited by the cost of the airborne LiDARdata acquisition and there are no available spaceborne LiDAR systems. Some researchers have demonstrated thepotential for mapping forest height using aerial or spaceborne stereo imagery with very high spatial resolutions.For stereo imageswith global coverage but coarse resolution newanalysis methods need to be used. Unlike mostresearch based on digital surface models, this study concentrated on analyzing the features of point cloud datagenerated from stereo imagery. The synthesizing of point cloud data from multi-view stereo imagery increasedthe point density of the data. The point cloud data over forested areas were analyzed and compared to small footprintLiDAR data and large-footprint LiDAR waveform data. The results showed that the synthesized point clouddata from ALOSPRISM triplets produce vertical distributions similar to LiDAR data and detected the verticalstructure of sparse and non-closed forests at 30mresolution. For dense forest canopies, the canopy could be capturedbut the ground surface could not be seen, so surface elevations from other sourceswould be needed to calculatethe height of the canopy. A canopy height map with 30 m pixels was produced by subtracting nationalelevation dataset (NED) fromthe averaged elevation of synthesized point clouds,which exhibited spatial featuresof roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlationwith RH50 of LVIS data with a slope of 1.04 and R2 of 0.74 indicating that the canopy height derived fromPRISM triplets can be used to estimate forest biomass at 30 m resolution.

The differences of two digital terrain models (DTMs) derived from airborne interferometric synthetic aperture radar (InSAR) data of short and long wavelengths are utilized for the estimation of vertical forest structures. However, when the spaceborne repeat-pass InSAR data are used, atmospheric effects must be considered. A simple method for the reduction of atmospheric effects in spaceborne repeat-pass interferometry is proposed in this letter. By subtracting a simulated interferogram using the Shuttle Radar Topography Mission (SRTM) DTM from the interferogram of a pair of Phased Array Type L-Band Synthetic Aperture Radar (PALSAR) InSAR data, the remaining phase includes the phase caused by the height differences of scattering phase centers (SPC) at C- and L-bands and the phases caused by atmospheric effects and other changes during the PALSAR repeat-pass period. A low-pass spatial filtering can reveal the atmospheric effect in the phase image because of the low spatial frequency of the atmospheric effects. The proper size of the filtering window can be determined by the changes of standard deviation of filtered phase images as the window size increases. The changes of the standard deviations of the filtered phase images should be almost constant when only the atmospheric effect remains. After reducing the atmospheric effects, the difference between the SRTM-DTM and the PALSAR-DTM was reduced from 60.17 m±16.2 m to near 0 m (0.52 m±4.3 m) at bare surfaces, and the correlation (R2) between the mean forest height and the difference between the SRTM-DTM and the PALSAR-DTM was significantly increased from 0.021 to 0.608.

The digital elevation model from the Shuttle Radar Topography Mission (SRTM) and the National Elevation Dataset (NED) have been used to estimate the forest canopy height. Most of such studies have been conducted over flat areas; the method performance has not been carefully examined over mountainous areas. This study, which is conducted over two mountainous test sites located in California and New Hampshire, reveals that the co-registration of these two digital elevation models (DEMs) is crucial to ensuring the quality of the results. The image co-registration method used in interferometric SAR processing is adapted to the co-registration of two DEMs. The forest canopy height from the Laser Vegetation Imaging Sensor (LVIS) is used as the reference data. The results showed that the misregistration between SRTM and NED was very obvious at both test sites. After the co-registration, the R2 of the correlation between the height of the C-band scattering phase center derived from SRTM minus NED and the forest canopy height derived from LVIS data was improved from 0.19 to 0.51, and RMSE was reduced from 16.4 m to 6.8 m for slope up to 55° at the California test site, while the R2 was improved from 0.39 to 0.57 and RMSE was reduced from 5.4 m to 3.6 m for slopes up to 45° at the New Hampshire test site. The influences of data resolution and terrain slopes were also investigated. The results showed that reducing the data resolution by spatial averaging could not reduce the influence of DEM misregistration.

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R2 higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.

Accurate estimate of biomass and its changes at local to regional scales are important for a better understanding of ecosystem function, biodiversity and sustainability. In this study we explored the forest biomass prediction and dynamic monitoring from Light detection and ranging (LiDAR) waveform metrics at different key map scales. NASA's Laser Vegetation Imaging Sensor (LVIS) data were acquired in Penobscot County, Maine, USA, during August 2003 and 2009 airborne campaigns in the New England region. Field data were collected in 2003, and 2009 to 2011. Regression models developed at the scale of footprint were applied to all LVIS waveforms within the two study sites: Howland Forest (HF) and Penobscot Experiment Forest (PEF). The effect of forest disturbances on LVIS biomass prediction models was investigated. Two types of models, i. e. combined model without consideration of disturbances and disturbance-specific models were developed and compared. Field data from nested field plots of 0.25 ha, 0.5 ha and 1.0 ha were used to evaluate the averaged, footprint-level (~ 0.03 ha, 20 m diameter) estimates in these plots. The results demonstrate that: 1) prediction model at the scale of individual LVIS footprints is reliable when the geolocations of the measured footprints were determined by DGPS with a best accuracy of 0.5–1.0 m. 2) The differences between biomass prediction models for disturbed and undisturbed forests were statistically significant (p < 0.001) at the scale of footprint, and the disturbance-specific models performed slightly better (R2 = 0.89, RMSE = 27.9 Mg·ha− 1, and relative error of 22.6%) than the combined model (R2 = 0.86, RMSE = 31.0 Mg·ha− 1, 25.1%). 3) The evaluation using field plot data showed that the predictions of biomass were improved markedly with the increase of plot sizes from 0.25 ha to 1.0 ha and that the effect of disturbance was not strong. At 1.0 ha plot-level, both disturbance-specific and combined models agreed well with field estimates (R2 = 0.91, 23.1 Mg∙ha− 1, 16.1%; and R2 = 0.91, 22.4 Mg·ha− 1, 15.6%). 4) Sensitivity analysis on levels of variation and error to footprint density suggests that a certain density of LVIS footprints is required for biomass mapping. The errors were minimized when footprint coverage approached about 50% of the area of 1.0 ha plots (16 footprints). 5) By applying the footprint-level models developed from 2009 LVIS data to both 2009 and 2003 LVIS data, the change of biomass from 2003 to 2009 could be assessed. The average annual biomass reduction rate from forest disturbance at two sites is − 7.0 Mg·ha− 1 and − 6.2 Mg·ha− 1, the average annual biomass accumulation from regrowth is + 4.4 Mg·ha− 1 and + 5.2 Mg·ha− 1, respectively.

The forest aboveground biomass and its dynamics are essential for researches of carbon cycling. The estimation of forest aboveground biomass is an important topic in the application of remote sensing. However, the estimation accuracy is limited by the lack of forest structures. Recent progresses over the past decade in the estimation of forest biomass from remote sensing data was mainly due to the success in the extraction of related forest structure parameters[1]. For example, LiDAR data is widely used in the estimation of forest biomass at local scales because it can directly measure the vertical structure of forests [2, 3]. Polarimetric Interferometric SAR (PolInSAR) is a another promising technique for the estimation of forest height and biomass which employs the dependence of penetration depth of SAR on polarization [4]. However, spaceborne LiDAR system cannot acquire wall-to-wall data because it mainly worked by point sampling while airborne LiDAR system only worked on at local scaled due to its cost. The P-band PolInSAR system onboard European BIOMASS satellite which is suitable for retrieval of forest biomass is still under construction and scheduled to be launched around 2020 by European space agency (ESA)

Forest parameters, especially the forest aboveground biomass over large area and in higher accuracy, become more and more important for the research on global climate change and carbon cycle [1]. Synthetic Aperture Radar (SAR) was considered as an effective tool for assessing forest aboveground biomass because it could penetrate farther into forest canopies, especially at longer wavelength such as L-and P-bands. In 1990's, many researchers investigated the relationship between the forest aboveground biomass and radar backscattering coefficients under various conditions through the campaigns of airborne and space-borne SARs [2–4]. The basic conclusions from these researches were that (1) cross-polarization is more sensitive than co-polarization; (2) The saturation point increased as the wavelength becomes longer [5]. Besides of radar backscattering coefficients, another variable used for the estimation of forest aboveground biomass is coherence since 2000's [6]. Coherence was initially used to describe the quality of interferometric SAR image pairs. Some researchers found an interesting phenomenon that forest stands with higher biomass level always showed lower coherence, i.e coherence is negatively correlated with forest aboveground biomass[7].

Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas, such as hydrology, meteorology, and agriculture. With the rapid development of remote sensing techniques, remote sensing has had the capacity of monitoring many factors of the Earth’s land surface. Especially, the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow, soil moisture, and vegetation parameters with their all-weather, all-time observation capabilities and their sensitivities to the characteristics of land surface factors. Based on the electromagnetic theories and microwave radiative transfer equations, researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years. This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling, microwave inversion on soil moisture, snow, vegetation and land surface temperatures. Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques, remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems.

Studies are needed to evaluate the ability of Advanced Land Observing Satellite ALOS Phased Array type L-band Synthetic Aperture Radar PALSAR for forest aboveground biomass AGB extraction in mountainous areas. In this article, forest biomass was estimated at plot and stand levels, and different biomass grades, respectively. Light detection and ranging LiDAR data with about one hit per m2 were first used for forest biomass estimation at the plot level, with R2 of 0.77. Then the LiDAR-derived biomass, as prior knowledge, was used to investigate the relationship between ALOS PALSAR data and biomass. The results showed that at each biomass level, the range of the back-scatter coefficient in HH and HV polarization where H and V represent horizontal and vertical polarizations, respectively, and the first of the two letters refers to the transmission polarization and the second to the received polarization was very large and there was no obvious relationship between the synthetic aperture radar SAR back-scatter coefficient and biomass at plot level. At stand level and in different biomass grades, the back-scatter coefficient increased with the increase of forest biomass, and a logarithm equation can be used to describe the relationship. The main reason may be that forest structure is complex at the plot level, while the average value could partly decrease the influence of forest structure at stand level. Meanwhile, terrain radiometric correction TRC was investigated and found effective for forest biomass estimation.

Radiative transfer (RT) models were used to simulate reflectance at TM bands and radar beckscattering coefficients at L-bands at acquisition conditions of LANDSAT and PALSAR on the acquisition dates. The forest stands aged from 5 to 250 years simulated from a forest growth model ZELIG at test sites were used to feed the RT models to build a look-up table. A simulated optical and SAR dataset was first used to test the LUT method and the results were promising. SAR data is better for biomass estimation and optical for canopy fractional cover estimation. Combined use of the data didn't improve the biomass estimation and slightly improved the fractional cover estimation. The inversion results from real TM and PALSAR data were presented and discussed.

Accurate estimates of aboveground biomass (AGB) from forest after disturbance could reduce the uncertainties in carbon budget of terrestrial ecosystem and provide critical information to related carbon policy. Yet the loss of carbon from forest disturbance and the gain from post-disturbance recovery have not been well assessed. In this study, sensitivity analysis was conducted to investigate: (1) influence of factors other than the change of AGB (i.e. distortion caused by incident angle, soil moisture) on SAR backscatter; (2) feasibility of cross-image calibration between multi-temporal and multi-sensor SAR data; and (3) possibility of applying normalized backscatter to detect the post-disturbance AGB recovery. A semi-automatic empirical model was proposed to reduce the incident angle effect. Then, a cross-image normalization procedure was performed in order to remove the radiometric distortions among multi-source SAR data. The results indicate that effect of incident angle and soil moisture on SAR backscatter could be reduced by the proposed procedure, and a detection of biomass changes is possible using multi-temporal and multi-sensor SAR data.

Forest plays a vital role in carbon, energy and water cycling of Earth System. Estimation of forest biomass has become essential to ecosystem studies. In this paper, three approaches, linear regression, Maximum Entropy and Look-up table based on model, were adopted. It was found that linear regression or Maximum Entropy met the saturation when biomass reached 170 Mg/ha, by use of either HV polarization or all polarizations. Look-up Table based on backscatter model could predict biomass well with the highest R2=0.74, and over 250Mg/ha without saturation problem.

Biomass at local to regional scales is important for carbon cycle study and monitoring the ecosystem responses to natural and human activities. This paper directly quantify biomass and its changes at 1-ha (100 m) spatial resolution from LiDAR footprint-level waveform data. A large-footprint full-waveform LiDAR (LVIS) data were acquired in Penobscot County, Maine State (USA) in August, 2003 and 2009. Field data were collected during the October 2003, and August of 2009 to 2011. The developed models using field measurements at waveform footprints were applied to all LVIS waveforms within the study site. Plots at 0.25-ha, 0.5-ha and 1-ha were used to validate the biomass averaged from footprints measured in these plots. The effect of forest disturbances on LiDAR biomass prediction models was investigated in the study. The results show that: 1) the prediction accuracy of models at footprint-level was acceptable at various plot-levels; 2) the footprint-level models could be applied for forest biomass with consideration of forest disturbance; 3) the 1-ha (100 m) was a proper scale for mapping of forest biomass and its change detection.

Former NASA's Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) satellite mission was to provide regional or global biomass carbon stock at regional, national, and global scales. Carbon Monitoring System UAVSAR and LVIS data acquired in August 2009 were used in this paper. In this paper we used Cloude's target decomposition theorem to decompose traditional polarimetric SAR data, and then to estimate biomass. By certification of LVIS derived biomass, the results show that only decomposed scattering elements are not enough for biomass retrieval, but they could improve the accuracy of biomass retrieval. The standard error for only polmethod is 66.3Mg/ha, but for both polametric and decomposed data is only 64.1Mg/ha.

Forest biomass is important information for the forest management and the studies of global changes. Researches showed that SAR data is superior in the estimation of forest biomass. However the backscattering coefficients are not the direct measurements of forest biomass. It can be affected by forest canopy spatial structures. In this study, eight scenes of PALSAR data acquired from July 10th 2007 to July 12th 2008 were collected. The annual behavior of backscattering coefficients and coherence derived from these interferometric PALSAR data were investigated. The results showed that the summer data showed high backscattering while the winter data showed low backscattering. The coherence of PALSAR InSAR data was the highest in the summer lowest in the winter. The coherence from summer data was negatively correlated with forest biomass. But the coherence from spring data showed higher coherence in the areas with higher forest biomass. The difference between SRTM elevation and digital elevation model generated by PALSAR interferometric data in the summer shows the potential for forest biomass mapping.

The NASA's DESDynI mission will provide global systematic lidar point-sampling data and areal coverage of L-band SAR data with polarimetric capabilities for 3-D structural studies of vegetation. The combined use of lidar's direct sampling measurements and radar's global areal mapping capabilities creates a real opportunity to map global ecosystem structures and functions that link to carbon dynamics. However, the relationship of the lidar point-sample data and corresponding areal SAR data is yet to be established. In this study the data fusion algorithms for the estimation of parameters of forest structure from Lidar and SAR Data is investigated. The results show that SAR intensity data is sensitive to forest biomass which is less than 15kg/m2 (or 150 ton/ha) and insensitive to the maximum forest height. Lidar data can be used to provide the information of forest height and the introduction of the information of forest height is helpful in the estimation of forest biomass.

Forest above ground biomass (AGB) is an important variable for evaluating ecosystem function and structure across landscape, which is necessary for studying forest productivity, carbon balance and nutrient allocation in forest ecosystem. In this study, a forest biomass estimate technique based on forest backscattering database is developed, and is used to retrieve AGB of Changbai mountain area from ALOS PALSAR dual-polarization data. The forest growth model and the 3D forest radar backscattering model were combined to build a forest multi-polarization radar backscattering database. Then forest AGB was estimated based on this database using statistic regression method and look up table (LUT) method. Two types of LUT searching methods (nearest distance and distance threshold) were used to find the accurate results. The biomass retrieved from forest inventory data was taken as ground truth to evaluate the inversion methods and the precision of the AGB estimation. The inversion results derived from PALSAR FBD data shows that both the statistical regression method and nearest distance LUT method underestimate forest aboveground biomass. The distance threshold LUT method gives the better biomass estimation compared with forest inventory data, the mean absolute error (MAE) of the whole research area is less than 10 Ton/ha.

Stand level forest canopy structure such as the size, density, and distribution of the branches and leaves may have a strong effect on radar backscatter. In this study, several broad-leaf (birch) stands and needle stands (larch) with different growing stages and different canopy structures are established using parametric and stochastic L-system. Stands with 10, 30, 50 years and with 10, 40, 100 years, which correspond to young, mid-age and mature birch and larch stands respectively, are simulated according to field measurements. To stands with the same age, the above ground biomass is almost the same. Then different 3D birch and larch crown architectures faithful to the real stand are generated using L-system, which provide realistic and detailed canopy biometric data for radar model. The radar model used here is an improved 3D forest radar backscatter model based on Radiative Transfer Theory, which considers tree crown distribution and multiple scattering from canopy during backscattering calculation In this paper, total 60 stands with 30×30m area, namely three stand ages, ten canopy structures and two species, are simulated and analyzed at C-, L-band with different polarizations. Simulation results show that the backscatter coefficient is sensitive to the canopy structure, particularly at C-band and L-band HV polarization. The discrepancy between birch and larch stands with the same tree age is distinct. The crown structure effect to the C-band is more obvious than L-band because of its short wavelength. Then the simulation results of L-HH, C-HH and C-HV polarizations are compared with JERS-1, ASAR data of Changqing forest farm located at DaXinAnLing, northeast of China, which shows good correspondence.

3D Lidar waveform and 3D radar backscatter models based on Radiative Transfer theory were used to simulate waveform and backscattering of various plots with different stand ages and structures, which were generated using forest growth model. The inversion models for estimating forest Above Ground Biomass (AGB) and Average Stand Height (ASH) were derived from the combined simulated database of large footprint Lidar waveforms and L-band polarimetric SAR backscattering using stepwise analysis method. The inversion procedures were then applied to NASA LVIS and ALOS PALSAR data to retrieve forest parameters for the study area. The study area is a 10km by 10km area located at International Paper's Northern Experiments Forest, Maine, USA, where field measurements that include stem coordinate, DBH, species and canopy position were recorded within a 200m by 150 m stand. Heights and AGB of total 7956 trees were estimated by applying species-specific allometric equations to stand measurements. AGB and height were then scaled up to the area according to the LVIS footprint size and location at 149 20m*20m plots, which were used to verify the inversion model developed using simulated database. The study concludes that Lidar waveform indices and SAR backscattering are complementary for forest parameters retrieving, which improved the limitation of signature saturation for regional biomass mapping using SAR data only. The comparison between inversed forest parameters and field measurements shows good consistency.