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Soil moisture (Mv) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this paper employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A de-orientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index (RVI) derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multi-temporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization respectively over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.
Tengfei Xiao; Minfeng Xing; Binbin He; Jinfei Wang; Jiali Shang; Xiaodong Huang; Xiliang Ni. Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1132 -1145.
AMA StyleTengfei Xiao, Minfeng Xing, Binbin He, Jinfei Wang, Jiali Shang, Xiaodong Huang, Xiliang Ni. Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):1132-1145.
Chicago/Turabian StyleTengfei Xiao; Minfeng Xing; Binbin He; Jinfei Wang; Jiali Shang; Xiaodong Huang; Xiliang Ni. 2020. "Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 1132-1145.
Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.
Minfeng Xing; Binbin He; Xiliang Ni; Jinfei Wang; Gangqiang An; Jiali Shang; Xiaodong Huang. Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data. Remote Sensing 2019, 11, 1956 .
AMA StyleMinfeng Xing, Binbin He, Xiliang Ni, Jinfei Wang, Gangqiang An, Jiali Shang, Xiaodong Huang. Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data. Remote Sensing. 2019; 11 (16):1956.
Chicago/Turabian StyleMinfeng Xing; Binbin He; Xiliang Ni; Jinfei Wang; Gangqiang An; Jiali Shang; Xiaodong Huang. 2019. "Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data." Remote Sensing 11, no. 16: 1956.
In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.
Mehdi Zamani Joharestani; Chunxiang Cao; Xiliang Ni; Barjeece Bashir; Somayeh Talebiesfandarani. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere 2019, 10, 373 .
AMA StyleMehdi Zamani Joharestani, Chunxiang Cao, Xiliang Ni, Barjeece Bashir, Somayeh Talebiesfandarani. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere. 2019; 10 (7):373.
Chicago/Turabian StyleMehdi Zamani Joharestani; Chunxiang Cao; Xiliang Ni; Barjeece Bashir; Somayeh Talebiesfandarani. 2019. "PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data." Atmosphere 10, no. 7: 373.
Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m.
Mingbo Liu; Chunxiang Cao; Yongfeng Dang; Xiliang Ni. Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data. Forests 2019, 10, 105 .
AMA StyleMingbo Liu, Chunxiang Cao, Yongfeng Dang, Xiliang Ni. Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data. Forests. 2019; 10 (2):105.
Chicago/Turabian StyleMingbo Liu; Chunxiang Cao; Yongfeng Dang; Xiliang Ni. 2019. "Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data." Forests 10, no. 2: 105.
Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.
Sornkitja Boonprong; Chunxiang Cao; Wei Chen; Xiliang Ni; Min Xu; Bipin Kumar Acharya. The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy. ISPRS International Journal of Geo-Information 2018, 7, 274 .
AMA StyleSornkitja Boonprong, Chunxiang Cao, Wei Chen, Xiliang Ni, Min Xu, Bipin Kumar Acharya. The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy. ISPRS International Journal of Geo-Information. 2018; 7 (7):274.
Chicago/Turabian StyleSornkitja Boonprong; Chunxiang Cao; Wei Chen; Xiliang Ni; Min Xu; Bipin Kumar Acharya. 2018. "The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy." ISPRS International Journal of Geo-Information 7, no. 7: 274.
With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014–December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy.
Xiliang Ni; Chunxiang Cao; Yuke Zhou; Xianghui Cui; Ramesh P. Singh. Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network. Atmosphere 2018, 9, 105 .
AMA StyleXiliang Ni, Chunxiang Cao, Yuke Zhou, Xianghui Cui, Ramesh P. Singh. Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network. Atmosphere. 2018; 9 (3):105.
Chicago/Turabian StyleXiliang Ni; Chunxiang Cao; Yuke Zhou; Xianghui Cui; Ramesh P. Singh. 2018. "Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network." Atmosphere 9, no. 3: 105.
This study develops a modeling framework for utilizing the 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, meteorological data, and forest measurements for monitoring stocks of total biomass (including aboveground biomass and root biomass). The forest tree height models were separately used according to the artificial neural network (ANN) and the allometric scaling and resource limitation (ASRL) tree height models which can both combine the climate data and satellite data to predict forest tree heights. Based on the allometric approach, the forest aboveground biomass model was developed from the field measured aboveground biomass data and the tree heights derived from two tree height models. Then, the root biomass should scale with the aboveground biomass. To investigate whether this approach is efficient for estimating forest total biomass, we used Northeast China as the object of study. Our results generally proved that the method proposed in this study could be meaningful for forest total biomass estimation (R2 = 0.699, RMSE = 55.86).
Xiliang Ni; Chunxiang Cao; Yuke Zhou; Lin Ding; Sungho Choi; Yuli Shi; Taejin Park; Xiao Fu; Hong Hu; Xuejun Wang. Estimation of Forest Biomass Patterns across Northeast China Based on Allometric Scale Relationship. Forests 2017, 8, 288 .
AMA StyleXiliang Ni, Chunxiang Cao, Yuke Zhou, Lin Ding, Sungho Choi, Yuli Shi, Taejin Park, Xiao Fu, Hong Hu, Xuejun Wang. Estimation of Forest Biomass Patterns across Northeast China Based on Allometric Scale Relationship. Forests. 2017; 8 (8):288.
Chicago/Turabian StyleXiliang Ni; Chunxiang Cao; Yuke Zhou; Lin Ding; Sungho Choi; Yuli Shi; Taejin Park; Xiao Fu; Hong Hu; Xuejun Wang. 2017. "Estimation of Forest Biomass Patterns across Northeast China Based on Allometric Scale Relationship." Forests 8, no. 8: 288.
In recent ten years, a perception exists that the agricultural management and crop cultivars have been improved obviously. But the crop yield variation trend due to above reason remain unknown yet. To evaluate the main food crop (maize, soybean and rice) yield trend from 2007 to 2016, the MODIS product (MCD12Q2) was used to extract the mature date of different crops. A two-band variant of the enhanced vegetation index at mature date was applied to establish empirical yield estimation model, coupling with statistical crop yield data. The validation show the estimated yield had accuracy of 90.9%, 91.7% and 83.3%, respectively. The average maize and soybean yield in study area presented increasing trend, but rice yield presented declining. However, maize yield in 22 cities and soybean yield in 19 cities show decreasing trend actually. Through statistical analysis, the crop yield distribution pattern was proved to be almost fixed. Most cities occupies approximate position on the ranking of relevant crop yield. It was demonstrated that some cities, for example Chifeng city, was suitable to develop specific agriculture economy. This paper can be used to give suggestion for agriculture planning and management.
Shanning Bao; Chunxiang Cao; Xiliang Ni; Min Xu; Hongrun Ju; Qisheng He; Si Zhou. Crop yield variation trend and distribution pattern in recent ten years. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 6150 -6153.
AMA StyleShanning Bao, Chunxiang Cao, Xiliang Ni, Min Xu, Hongrun Ju, Qisheng He, Si Zhou. Crop yield variation trend and distribution pattern in recent ten years. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():6150-6153.
Chicago/Turabian StyleShanning Bao; Chunxiang Cao; Xiliang Ni; Min Xu; Hongrun Ju; Qisheng He; Si Zhou. 2017. "Crop yield variation trend and distribution pattern in recent ten years." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 6150-6153.
Accurate understanding and detecting of vegetation growth change is essential for providing suitable management strategies for ecosystems. Several studies using satellite based vegetation indices have demonstrated changes of vegetation growth and phenology. Temperature is considered a major determinant of vegetation phenology. To accurately detect the response of vegetation to climate variations, this study investigated the vegetation phenology in the northeast (NE) region of China by using in-situ temperature observations and satellite-based leaf area index estimates (LAI3g) for the period 1982–2011. Firstly, a spatial distribution of the averaged phenology over the 30 years was obtained. This distribution showed that a tendency for an early start of the growing season (SoS) and late end of the growing season (EoS) was observed towards of the southeastern part of NE China, with the late SoS and early EoS occurring at higher latitudes. Secondly, the temperature-based and satellite-based phenological trends were analyzed. Then the significant advanced trend (SAT), significant delayed trend (SDT), and nonsignificant trend (NT) of SOS and EOS in NE region of China were detected by using the Mann-Kendall trend test approach. Finally, changes in phenological trends were investigated by using the temperature-based and satellite-based phenology method. A comparison of the phenological trend shows that there are some significant advanced trends of SOS and significant delayed trends of EOS in the NE region of China over 30 years. The results of this study can provide important support of the view that a lengthening of growing season duration occurred at the northern high latitudes in recent decades.
Xiliang Ni; Jianfeng Xie; Yuke Zhou; Xizhang Gao; Lin Ding. Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data. Climate 2017, 5, 37 .
AMA StyleXiliang Ni, Jianfeng Xie, Yuke Zhou, Xizhang Gao, Lin Ding. Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data. Climate. 2017; 5 (2):37.
Chicago/Turabian StyleXiliang Ni; Jianfeng Xie; Yuke Zhou; Xizhang Gao; Lin Ding. 2017. "Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data." Climate 5, no. 2: 37.
Although leaf area index (LAI) is one of the essential parameters employed to monitor global vegetation, no global LAI products which use a fine spatial resolution yet exist. To remedy this, we herein outline an adapted LAI retrieval algorithm which employs HuanJing-1 charge-coupled device (HJ-1 CCD) data, which was originally determined using Landsat thematic mapper (TM) data. Validation of this adapted LAI retrieval algorithm via field measurements demonstrates that errors for ∼72% of the sample sites were within a range of 0.2 for the Bashang Grassland in Hebei Province, and within 1.0 for the Taihe region in Jiangxi Province. In addition, the correlation coefficients (R) of HJ-1 LAI and ground-measured LAI data were similar to those of MODIS LAI and ground-measured LAI datasets for both regions. These results demonstrate the potential for transposing a mature Landsat LAI-retrieval algorithm to HJ-1 data. Furthermore, we generated 30 m HJ-1 LAI datasets for China for the summer of 2012. An analytical comparison between retrieved HJ-1 LAI and MODIS LAI data showed the coherence of the LAI distribution. Finally, a specific post-forest fire area was mapped using HJ-1 LAI datasets.
Xiaojie Zhao; Chunxiang Cao; Xiliang Ni; Wei Chen. Retrieval and application of leaf area index over China using HJ-1 data. Geomatics, Natural Hazards and Risk 2016, 8, 478 -495.
AMA StyleXiaojie Zhao, Chunxiang Cao, Xiliang Ni, Wei Chen. Retrieval and application of leaf area index over China using HJ-1 data. Geomatics, Natural Hazards and Risk. 2016; 8 (2):478-495.
Chicago/Turabian StyleXiaojie Zhao; Chunxiang Cao; Xiliang Ni; Wei Chen. 2016. "Retrieval and application of leaf area index over China using HJ-1 data." Geomatics, Natural Hazards and Risk 8, no. 2: 478-495.
It is an indisputable fact that wetlands in northern China are subject to increasing pressures from climate change and other human-mediated activities, including the wetland in the Ordos Larus relictus National Nature Reserve. Dynamic monitoring the wetland's state and changes have an irreplaceable role in its protection and management but have rarely been performed systematically. In this study, the wetland land-cover changes during 1991–2014 were analyzed using three land-cover components (vegetation, soil and water) acquired through linear spectral unmixing. Then, the temporal ecological changes were predicted for the next 5–100 years using cellular automata and Markov chain models. The results showed that, under the natural and anthropogenic effects, the wetland is suffering continuous degradation. From 2000 to 2005, a dramatic change characterized by a rapid reduction in water area and a significant increase in vegetation coverage occurred. The water area decreased to its historic minimum of 1.74 km2 in 2010. The prediction results indicated that the water area will further decrease to less than 0.1 km2 over the next 35 years. Considering the steady decline in precipitation over the past 50 years, halting destructive human activities and artificially intervening with wetland management are reasonable alternatives to prevent additional degradation.
Di Liu; Chunxiang Cao; Wei Chen; Xiliang Ni; Rong Tian; Xiaojun Xing. Monitoring and predicting the degradation of a semi-arid wetland due to climate change and water abstraction in the Ordos Larus relictus National Nature Reserve, China. Geomatics, Natural Hazards and Risk 2016, 8, 367 -383.
AMA StyleDi Liu, Chunxiang Cao, Wei Chen, Xiliang Ni, Rong Tian, Xiaojun Xing. Monitoring and predicting the degradation of a semi-arid wetland due to climate change and water abstraction in the Ordos Larus relictus National Nature Reserve, China. Geomatics, Natural Hazards and Risk. 2016; 8 (2):367-383.
Chicago/Turabian StyleDi Liu; Chunxiang Cao; Wei Chen; Xiliang Ni; Rong Tian; Xiaojun Xing. 2016. "Monitoring and predicting the degradation of a semi-arid wetland due to climate change and water abstraction in the Ordos Larus relictus National Nature Reserve, China." Geomatics, Natural Hazards and Risk 8, no. 2: 367-383.
The rain-fed agriculture dominates the agricultural production in Northeast China, which leads to the drought risk for crops growing. To assess the yields and necessity of irrigation for maize in the Three Northeast Provinces of China, the calibrated and validated WOrld FOod STudies (WOFOST) model was used to estimate maize yieldss in the regional scale after it was optimized by assimilating leaf area index retrieved from remote sensing data based on Shuffled Complex Evolution (SCE-UA) algorithm. Then the gaps between potential and water limiting yieldss (the irrigation was equal to zero) were computed and mapped to detect the area where the crop irrigation improvement was demanded. The presented result could support the decision from government on the crop management which suggested that crops in the northwest of Liaoning Province, the west of Jilin Province and the southwest of Heilongjiang Province lacked the irrigation most.
Shanning Bao; Chunxiang Cao; Jianxi Huang; Xiliang Ni; Min Xu. Research on yields estimation and yields increasing potential by irrigation of spring maize in Northeast China. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 2506 -2509.
AMA StyleShanning Bao, Chunxiang Cao, Jianxi Huang, Xiliang Ni, Min Xu. Research on yields estimation and yields increasing potential by irrigation of spring maize in Northeast China. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():2506-2509.
Chicago/Turabian StyleShanning Bao; Chunxiang Cao; Jianxi Huang; Xiliang Ni; Min Xu. 2016. "Research on yields estimation and yields increasing potential by irrigation of spring maize in Northeast China." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 2506-2509.
Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R2 = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R2 = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.
Chunxiang Cao; Xiliang Ni; Xuejun Wang; Shilei Lu; Yuxing Zhang; Yongfeng Dang; Ramesh P. Singh. Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data. International Journal of Remote Sensing 2016, 37, 1210 -1222.
AMA StyleChunxiang Cao, Xiliang Ni, Xuejun Wang, Shilei Lu, Yuxing Zhang, Yongfeng Dang, Ramesh P. Singh. Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data. International Journal of Remote Sensing. 2016; 37 (5):1210-1222.
Chicago/Turabian StyleChunxiang Cao; Xiliang Ni; Xuejun Wang; Shilei Lu; Yuxing Zhang; Yongfeng Dang; Ramesh P. Singh. 2016. "Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data." International Journal of Remote Sensing 37, no. 5: 1210-1222.
Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R2 = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass.
Xiliang Ni; Yuke Zhou; Chunxiang Cao; Xuejun Wang; Yuli Shi; Taejin Park; Sungho Choi; Ranga B. Myneni. Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data. Remote Sensing 2015, 7, 8436 -8452.
AMA StyleXiliang Ni, Yuke Zhou, Chunxiang Cao, Xuejun Wang, Yuli Shi, Taejin Park, Sungho Choi, Ranga B. Myneni. Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data. Remote Sensing. 2015; 7 (7):8436-8452.
Chicago/Turabian StyleXiliang Ni; Yuke Zhou; Chunxiang Cao; Xuejun Wang; Yuli Shi; Taejin Park; Sungho Choi; Ranga B. Myneni. 2015. "Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data." Remote Sensing 7, no. 7: 8436-8452.
This paper investigates the retrieval of forest crown closure (CC) from the Landsat Thematic Mapper (TM) data and aerial images with a linear spectral mixture analysis (SMA) method. Anshan is selected as the study area. Two endmember extraction methods were used in this paper: 1) traditional image-based method and 2) up-scaling method. (When we get the fractions of components from a coregistered 0.6-m spatial resolution image, the linear spectral mixture model is applied to unmix the TM image and obtain the required endmembers.) For both methods, four fraction images (sunlit canopy, shaded canopy, sunlit background, shaded background) were calculated by linear spectral mixture model and used to derive CC. Results showed that CC can be fitted best with sum of fractions of sunlit canopy and shaded canopy at S-shaped curve and the up-scaling endmember extraction method is better than traditional image-based endmember extraction method. Finally, the up-scaling endmember extraction method was used to map forest CC in Anshan forested region. The measured forest CC distribution map was used to validate the estimated map. Results show that the estimated CC and measured CC have little difference and the estimated CC is slightly lower. The majority of Anshan forest CC values were between 0.4 and 0.8.
Chunxiang Cao; Haijing Tian; Yuxing Zhang; Yongfeng Dang; Xiliang Ni; Yunfei Xu; Min Xu; Xiaowen Li; Haibing Xiang; Tianyu Yang. Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015, 8, 2560 -2568.
AMA StyleChunxiang Cao, Haijing Tian, Yuxing Zhang, Yongfeng Dang, Xiliang Ni, Yunfei Xu, Min Xu, Xiaowen Li, Haibing Xiang, Tianyu Yang. Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015; 8 (6):2560-2568.
Chicago/Turabian StyleChunxiang Cao; Haijing Tian; Yuxing Zhang; Yongfeng Dang; Xiliang Ni; Yunfei Xu; Min Xu; Xiaowen Li; Haibing Xiang; Tianyu Yang. 2015. "Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 6: 2560-2568.
In this study, we used 30m Landsat TM data to develop regression models that estimate the deforestation area of China. At first, the Maximum Likelihood Classification method was used to achieve the image classification based on the priori knowledge from the field measured data over China. Secondly, high resolution SPOT-5 data was used to validate the classification precision of Landsat TM data and build the deforestation area models. All built model highly accurate, and the R2 are bigger than 0.85. Finally, the deforestation area over China was estimated according to regression model developed.
Xuejun Wang; Yuxing Zhang; Enping Yan; Guosheng Huang; Chunxiang Cao; Xiliang Ni. Deforestation area estimation in China based on Landsat data. 2014 IEEE Geoscience and Remote Sensing Symposium 2014, 4254 -4256.
AMA StyleXuejun Wang, Yuxing Zhang, Enping Yan, Guosheng Huang, Chunxiang Cao, Xiliang Ni. Deforestation area estimation in China based on Landsat data. 2014 IEEE Geoscience and Remote Sensing Symposium. 2014; ():4254-4256.
Chicago/Turabian StyleXuejun Wang; Yuxing Zhang; Enping Yan; Guosheng Huang; Chunxiang Cao; Xiliang Ni. 2014. "Deforestation area estimation in China based on Landsat data." 2014 IEEE Geoscience and Remote Sensing Symposium , no. : 4254-4256.
To achieve a fully automatic registration between HJ-1 CCD images and HJ-1 infrared images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives, and the local deformations within the images. In this paper, aimed at those registration issues, a fully automatic registration approach based on contour and SIFT is proposed. The registration technique performs a pre-registration process using contour feature matching algorithm that decides the overlapping region between a reference image and an input image. Once the coarse regions are obtained, it performs a fine registration process based on SIFT detector and a local adaptive matching strategy. In the fine registration process, image blocking theory is used, which not only speeds up the features extraction and matching, but also makes the matching point pairs distributed uniformly in images, and further improves the accuracy of input image rectification. Experiments with visible images and infrared images from HJ-1A/B demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote sensing images registration.
Xiliang Ni; Chunxiang Cao; Lin Ding; Tao Jiang; Hao Zhang; Huicong Jia; Guanghe Li; Jian Zhao; Wei Chen; Wei Ji; Min Xu; Mengxu Gao; Sheng Zheng; Rong Tian; Cheng Liu; Sha Li. A fully automatic registration approach based on contour and SIFT for HJ-1 images. Science China Earth Sciences 2012, 55, 1679 -1687.
AMA StyleXiliang Ni, Chunxiang Cao, Lin Ding, Tao Jiang, Hao Zhang, Huicong Jia, Guanghe Li, Jian Zhao, Wei Chen, Wei Ji, Min Xu, Mengxu Gao, Sheng Zheng, Rong Tian, Cheng Liu, Sha Li. A fully automatic registration approach based on contour and SIFT for HJ-1 images. Science China Earth Sciences. 2012; 55 (10):1679-1687.
Chicago/Turabian StyleXiliang Ni; Chunxiang Cao; Lin Ding; Tao Jiang; Hao Zhang; Huicong Jia; Guanghe Li; Jian Zhao; Wei Chen; Wei Ji; Min Xu; Mengxu Gao; Sheng Zheng; Rong Tian; Cheng Liu; Sha Li. 2012. "A fully automatic registration approach based on contour and SIFT for HJ-1 images." Science China Earth Sciences 55, no. 10: 1679-1687.
In this study, we developed the Allometric Scaling and Resource Limitations (ASRL) model by using the best GLAS tree heights to optimize the ASRL. At first, we obtained the best metric of GLAS tree heights by comparing with LVIS tree heights in six sites. Then, the best metric GLAS tree heights were separately used to optimize ASRL model and test the accuracy of prediction heights from optimized ASRL model in sites scale and country scale. Validation result showed that predicted tree heights from optimized ASRL model had high accuracy.
Xiliang Ni; Yuli Shi; Sungho Choi; Chunxiang Cao; Ranga B. Myneni. Estimation of tree heights using remote sensing data and an Allometric Scaling and Resource Limitations (ASRL) model. 2012 IEEE International Geoscience and Remote Sensing Symposium 2012, 7248 -7251.
AMA StyleXiliang Ni, Yuli Shi, Sungho Choi, Chunxiang Cao, Ranga B. Myneni. Estimation of tree heights using remote sensing data and an Allometric Scaling and Resource Limitations (ASRL) model. 2012 IEEE International Geoscience and Remote Sensing Symposium. 2012; ():7248-7251.
Chicago/Turabian StyleXiliang Ni; Yuli Shi; Sungho Choi; Chunxiang Cao; Ranga B. Myneni. 2012. "Estimation of tree heights using remote sensing data and an Allometric Scaling and Resource Limitations (ASRL) model." 2012 IEEE International Geoscience and Remote Sensing Symposium , no. : 7248-7251.
Chunxiang Cao; Wei Chen; Guanghe Li; Huicong Jia; Wei Ji; Min Xu; Mengxu Gao; Xiliang Ni; Jian Zhao; Sheng Zheng; Rong Tian; Cheng Liu; Sha Li. The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis. Canadian Journal of Remote Sensing 2011, 37, 348 -358.
AMA StyleChunxiang Cao, Wei Chen, Guanghe Li, Huicong Jia, Wei Ji, Min Xu, Mengxu Gao, Xiliang Ni, Jian Zhao, Sheng Zheng, Rong Tian, Cheng Liu, Sha Li. The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis. Canadian Journal of Remote Sensing. 2011; 37 (4):348-358.
Chicago/Turabian StyleChunxiang Cao; Wei Chen; Guanghe Li; Huicong Jia; Wei Ji; Min Xu; Mengxu Gao; Xiliang Ni; Jian Zhao; Sheng Zheng; Rong Tian; Cheng Liu; Sha Li. 2011. "The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis." Canadian Journal of Remote Sensing 37, no. 4: 348-358.
Forest biomass is an important indicator in carbon sequestration capacity and forest carbon budget evaluation, but there is less focus on shrub biomass. Multi-angle images provide the volume scattering information which could improve inversion accuracy. HJ-1 satellites are the sun synchronous recurrent frozen orbit small satellite constellation for environment and disaster monitoring and forecasting and were launched on September 6, 2008, China, with a repetition cycle of two days. Now 6 angle observations have been built up from several days of HJ-1 A/B images. Images were first calibrated both geometrically and atmospherically. A simplified Geometric-Optical(SGM) model was used for the parameters estimation. The background estimation was conducted from the LiSparse- RossThin kernel weights and the Walthall empirical model. Then, the three kernel weights of the LiSparse-RossThin model were retrieved for each location. Finally, the shrub cover and radius were retrieved from SGM model. Biomass was estimated from the shrub cover and radius. 21 plots for background estimation and verification were measured during July 2009. The R2 between simulated cover and measured cover is 0.21, but the RMSE of the model fitting is 0.09. The distribution of the shrub cover estimated from multi-angle HJ images is coincidence with fine resolution Google Earth image. So is the biomass distribution. The weak relationship may be because the higher spatial heterogeneity, the view angles' limitation, and the time variance among images. The multi-angle HJ images are low cost which is useful in far and extended sandland monitoring and evaluation. The results reported here also showed that the development of HJ satellite data for wide range of mapping application.
Jian Zhao; Chunxiang Cao; Hao Zhang; Sheng Zheng; Huicong Jia; Wei Ji; Mengxu Gao; Min Xu; Xiliang Ni; Wei Chen; Rong Tian; Cheng Liu; Xiaowen Li. Shrub biomass estimation in Mu Us Sandland using simple GO model and multi-angle observations. 2011 IEEE International Geoscience and Remote Sensing Symposium 2011, 3046 -3049.
AMA StyleJian Zhao, Chunxiang Cao, Hao Zhang, Sheng Zheng, Huicong Jia, Wei Ji, Mengxu Gao, Min Xu, Xiliang Ni, Wei Chen, Rong Tian, Cheng Liu, Xiaowen Li. Shrub biomass estimation in Mu Us Sandland using simple GO model and multi-angle observations. 2011 IEEE International Geoscience and Remote Sensing Symposium. 2011; ():3046-3049.
Chicago/Turabian StyleJian Zhao; Chunxiang Cao; Hao Zhang; Sheng Zheng; Huicong Jia; Wei Ji; Mengxu Gao; Min Xu; Xiliang Ni; Wei Chen; Rong Tian; Cheng Liu; Xiaowen Li. 2011. "Shrub biomass estimation in Mu Us Sandland using simple GO model and multi-angle observations." 2011 IEEE International Geoscience and Remote Sensing Symposium , no. : 3046-3049.