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Developing an individual tree diameter increment (ΔDBH) model is the basis of near-natural management of mixed, uneven-aged oak forests. This analysis used remeasurement data (2009–2014) comprising 6154 observations from 112 permanent plots in central China to develop and compare an indicator variable model (IVM) and a mixed-effect model (MEM) to estimate ΔDBH. First, a basic model was estimated using 12 potential explanatory variables. Geographical regions (GR), competition intensities (CI) and species compositions (SC) were introduced into the basal model as indicator variables or mixed effects, step by step, and then the prediction accuracy of IVM and MEM was compared. The results showed that (1) the independent variables significantly affecting ΔDBH included the reciprocal of DBH, basal area, altitude, and mean annual rainfall; (2) the introducing GR could not improve the accuracy of estimating ΔDBH, but the CI and SC could. (3) Compared with the basic model and IVM, the percentage mean absolute deviation of MEM decreased by 2.07% and 1.11%, while the root mean square error decreased by 0.06 and 0.04, respectively. The MEM including CI and SC as a random effect showed the best predictive performance and can be applied to improve the prediction of individual oak trees ΔDBH.
Shisheng Long; Zhenwei Shi; Guangxing Wang; Siqi Zeng. Developing an individual tree diameter increment model of oaks using indicator variables and mixed effects in central China. Scandinavian Journal of Forest Research 2021, 36, 297 -305.
AMA StyleShisheng Long, Zhenwei Shi, Guangxing Wang, Siqi Zeng. Developing an individual tree diameter increment model of oaks using indicator variables and mixed effects in central China. Scandinavian Journal of Forest Research. 2021; 36 (4):297-305.
Chicago/Turabian StyleShisheng Long; Zhenwei Shi; Guangxing Wang; Siqi Zeng. 2021. "Developing an individual tree diameter increment model of oaks using indicator variables and mixed effects in central China." Scandinavian Journal of Forest Research 36, no. 4: 297-305.
The need for timely, spatially, and thematically accurate information regarding forests is increasing because of the key role of forests in the global carbon balance and sustainable social, economic, ecological, and cultural development
Erkki Tomppo; Guangxing Wang; Jaan Praks; Ronald McRoberts; Lars Waser. Editorial Summary, Remote Sensing Special Issue “Advances in Remote Sensing for Global Forest Monitoring”. Remote Sensing 2021, 13, 597 .
AMA StyleErkki Tomppo, Guangxing Wang, Jaan Praks, Ronald McRoberts, Lars Waser. Editorial Summary, Remote Sensing Special Issue “Advances in Remote Sensing for Global Forest Monitoring”. Remote Sensing. 2021; 13 (4):597.
Chicago/Turabian StyleErkki Tomppo; Guangxing Wang; Jaan Praks; Ronald McRoberts; Lars Waser. 2021. "Editorial Summary, Remote Sensing Special Issue “Advances in Remote Sensing for Global Forest Monitoring”." Remote Sensing 13, no. 4: 597.
Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.
Fugen Jiang; Mykola Kutia; Arbi J. Sarkissian; Hui Lin; Jiangping Long; Hua Sun; Guangxing Wang. Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method. Sensors 2020, 20, 7248 .
AMA StyleFugen Jiang, Mykola Kutia, Arbi J. Sarkissian, Hui Lin, Jiangping Long, Hua Sun, Guangxing Wang. Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method. Sensors. 2020; 20 (24):7248.
Chicago/Turabian StyleFugen Jiang; Mykola Kutia; Arbi J. Sarkissian; Hui Lin; Jiangping Long; Hua Sun; Guangxing Wang. 2020. "Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method." Sensors 20, no. 24: 7248.
Forests play an important role in global ecosystems, but natural disasters such as drought and disease and pest-induced damages often affect growth of trees and even lead to tree death. There have been various vegetation indices (VIs) developed to detect the damages of trees using remote sensing technologies. However, developing an effective and accurate VI for detecting forest damages at an earlier stage is still challenging. In this study, a total of 32 target trees and 17 reference trees were selected from one of Chinese fir plantations that account for 24% of the afforested area in China. The barks of the target trees at 20 cm and 70 cm above the ground were respectively peeled off in August of 2016 to let the trees die. The hyperspectral data were collected from the selected branches with leaves using a high-performance spectrometer HR-1024i (Spectra Vista Corporation) with 1024 bands covering the region of wavelengths from 350 nm to 2500 nm. The spectral data collection was conducted from both the target and reference trees by non-imaging monthly from August of 2016 to February of 2017 until the target trees died. Based on statistical analysis, a Green to Red Region Spectral anGle Index (GRRSGI) was proposed and compared with other ten widely used VIs to detect the early dying process of the damaged trees. The results showed that compared with the reference trees, the damaged trees reduced the reflectance peaks of green bands and the absorption valleys of red bands and the frequency distributions of spectral reflectance from the damaged trees turned from statistically normal to non-normal in the region of 550–640 nm. This indicated that the spectral bands in the region of green to red bands were sensitive to the dying process of the target trees. Moreover, the performance of the VIs for detecting the dying process of the damaged trees varied depending on the time period after the trees damaged. The proposed GRRSGI based on the spectral bands of 550–640 nm led to the performance similar to all other VIs after three months of the damages but significantly better than them in the first two months after the damages. Except for GRRSGI, the Green-Red Spectral Area Index (GRSAI) also performed better than other VIs. Compared with GRSAI, however, the proposed GRRSGI performed slight better when the spectral data of 550–640 nm were re-sampled using the spectral intervals of 5–40 nm with an increase of 5 nm and significantly better when the re-sampling of the hyperspectral data was conducted in the region of 550–670 nm. This implied the proposed GRRSGI provided the greater potential of detecting the early dying process of the damage trees.
Zhuo Zang; Guangxing Wang; Hui Lin; Peng Luo. Developing a spectral angle based vegetation index for detecting the early dying process of Chinese fir trees. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 171, 253 -265.
AMA StyleZhuo Zang, Guangxing Wang, Hui Lin, Peng Luo. Developing a spectral angle based vegetation index for detecting the early dying process of Chinese fir trees. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 171 ():253-265.
Chicago/Turabian StyleZhuo Zang; Guangxing Wang; Hui Lin; Peng Luo. 2020. "Developing a spectral angle based vegetation index for detecting the early dying process of Chinese fir trees." ISPRS Journal of Photogrammetry and Remote Sensing 171, no. : 253-265.
Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while photogrammetry tends to accurately depict the canopy surface envelope. The spatial differences between the canopy surfaces estimated by LiDAR and photogrammetry have not been investigated in depth. Thus, this study aims to assess LiDAR and photogrammetry point clouds and analyze the spatial differences in canopy heights. The study site is located in the Jigongshan National Nature Reserve of Henan Province, Central China. Six data sets, including one LiDAR data set and five photogrammetry data sets captured from an unmanned aerial vehicle (UAV), were used to estimate the forest canopy heights. Three spatial distribution descriptors, namely, the effective cell ratio (ECR), point cloud homogeneity (PCH) and point cloud redundancy (PCR), were developed to assess the LiDAR and photogrammetry point clouds in the grid. The ordinary neighbor (ON) and constrained neighbor (CN) interpolation algorithms were used to fill void cells in digital surface models (DSMs) and canopy height models (CHMs). The CN algorithm could be used to distinguish small and large holes in the CHMs. The optimal spatial resolution was analyzed according to the ECR changes of DSMs or CHMs resulting from the CN algorithms. Large negative and positive variations were observed between the LiDAR and photogrammetry canopy heights. The stratified mean difference in canopy heights increased gradually from negative to positive when the canopy heights were greater than 3 m, which means that photogrammetry tends to overestimate low canopy heights and underestimate high canopy heights. The CN interpolation algorithm achieved smaller relative root mean square errors than the ON interpolation algorithm. This article provides an operational method for the spatial assessment of point clouds and suggests that the variations between LiDAR and photogrammetry CHMs should be considered when modeling forest parameters.
Qingwang Liu; Liyong Fu; Qiao Chen; Guangxing Wang; Peng Luo; Ram Sharma; Peng He; Mei Li; Mengxi Wang; Guangshuang Duan. Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry. Remote Sensing 2020, 12, 2884 .
AMA StyleQingwang Liu, Liyong Fu, Qiao Chen, Guangxing Wang, Peng Luo, Ram Sharma, Peng He, Mei Li, Mengxi Wang, Guangshuang Duan. Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry. Remote Sensing. 2020; 12 (18):2884.
Chicago/Turabian StyleQingwang Liu; Liyong Fu; Qiao Chen; Guangxing Wang; Peng Luo; Ram Sharma; Peng He; Mei Li; Mengxi Wang; Guangshuang Duan. 2020. "Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry." Remote Sensing 12, no. 18: 2884.
Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R2 = 0.38 and RMSE = 87.97) and SVR (R2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.
Mingbang Zhu; Shanshan Liu; Ziqing Xia; Guangxing Wang; Yueming Hu; Zhenhua Liu. Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land QualityUsing GA-BPNN. Agriculture 2020, 10, 318 .
AMA StyleMingbang Zhu, Shanshan Liu, Ziqing Xia, Guangxing Wang, Yueming Hu, Zhenhua Liu. Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land QualityUsing GA-BPNN. Agriculture. 2020; 10 (8):318.
Chicago/Turabian StyleMingbang Zhu; Shanshan Liu; Ziqing Xia; Guangxing Wang; Yueming Hu; Zhenhua Liu. 2020. "Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land QualityUsing GA-BPNN." Agriculture 10, no. 8: 318.
Increasing the area of planted forests is rather important for compensation the loss of natural forests and slowing down the global warming. Forest growing stem volume (GSV) is a key indicator for monitoring and evaluating the quality of planted forest. To improve the accuracy of planted forest GSV located in south China, four L-band ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images were acquired from June to September with short intervals. Polarimetric characteristics (un-fused and fused) derived by the Yamaguchi decomposition from time series SAR images with different intervals were considered as independent variables for the GSV estimation. Then, the general linear model (GLM) obeyed the exponential distribution were proposed to retrieve the stand-level GSV in plantation. The results show that the un-fused power of double bounce scatters and four fused variables derived from single SAR image is highly sensitive to the GSV, and these polarimeric characteristics derived from the time series images more significantly contribute to improved estimation of GSV. Moreover, compared with the estimated GSV using the semi-exponential model, the employed GLM model with less limitations and simple algorithm has a higher saturation level (nearly to 300 m3/ha) and higher sensitivity to high forest GSV values than the semi-exponential model. Furthermore, by reducing the external disturbance with the help of time average, the accuracy of estimated GSV is improved using fused polarimeric characteristics, and the estimation accuracy of forest GSV was improved as the images increase. Using the fused polarimetric characteristics (Dbl×Vol/Odd) and the GLM, the minimum RRMSE was reduced from 33.87% from single SAR image to 24.42% from the time series SAR images. It is implied that the GLM is more suitable for polarimetric characteristics derived from the time series SAR images and has more potential to improve the planted forest GSV.
Jiangping Long; Hui Lin; Guangxing Wang; Hua Sun; Enping Yan. Estimating the Growing Stem Volume of the Planted Forest Using the General Linear Model and Time Series Quad-Polarimetric SAR Images. Sensors 2020, 20, 3957 .
AMA StyleJiangping Long, Hui Lin, Guangxing Wang, Hua Sun, Enping Yan. Estimating the Growing Stem Volume of the Planted Forest Using the General Linear Model and Time Series Quad-Polarimetric SAR Images. Sensors. 2020; 20 (14):3957.
Chicago/Turabian StyleJiangping Long; Hui Lin; Guangxing Wang; Hua Sun; Enping Yan. 2020. "Estimating the Growing Stem Volume of the Planted Forest Using the General Linear Model and Time Series Quad-Polarimetric SAR Images." Sensors 20, no. 14: 3957.
The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species.
Zhaohui Yang; Qingwang Liu; Peng Luo; Qiaolin Ye; Guangshuang Duan; Ram Sharma; Huiru Zhang; Guangxing Wang; Liyong Fu. Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data. Remote Sensing 2020, 12, 2238 .
AMA StyleZhaohui Yang, Qingwang Liu, Peng Luo, Qiaolin Ye, Guangshuang Duan, Ram Sharma, Huiru Zhang, Guangxing Wang, Liyong Fu. Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data. Remote Sensing. 2020; 12 (14):2238.
Chicago/Turabian StyleZhaohui Yang; Qingwang Liu; Peng Luo; Qiaolin Ye; Guangshuang Duan; Ram Sharma; Huiru Zhang; Guangxing Wang; Liyong Fu. 2020. "Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data." Remote Sensing 12, no. 14: 2238.
As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.
Fugen Jiang; Andrew R. Smith; Mykola Kutia; Guangxing Wang; Hua Liu; Hua Sun. A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China. Remote Sensing 2020, 12, 1 .
AMA StyleFugen Jiang, Andrew R. Smith, Mykola Kutia, Guangxing Wang, Hua Liu, Hua Sun. A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China. Remote Sensing. 2020; 12 (11):1.
Chicago/Turabian StyleFugen Jiang; Andrew R. Smith; Mykola Kutia; Guangxing Wang; Hua Liu; Hua Sun. 2020. "A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China." Remote Sensing 12, no. 11: 1.
There has been substantial research for estimating and mapping soil moisture content (SMC) of large areas using remotely sensed images by developing models of soil thermal inertia (STI). However, it is still a great challenge to accurately estimate SMC because of the impact of vegetation canopies and vegetation-induced shadows in mixed pixels on the estimates. In this study, a new method was developed to increase the estimation accuracy of SMC for an irrigated area located in YingKe of Heihe, China, using ASTER data. In the method, an original model of estimating bare STI was modified by decomposing a mixed pixel into three components, bare soil, vegetated soil, and shaded soil, as well as extracting their fractions using a spectral unmixing analysis and then deriving their fluxes. Moreover, the 90 m spatial resolution thermal images were scaled down to the 15 m spatial resolution by data fusion of a discrete wavelet transform (DWT) and re-sampling using the nearest neighbor method (NNM). The modified model was compared with the original model based on the mean absolute error (MAE) and relative root mean square error (RRMSE) between the SMC estimates and observations from 30 validation soil samples. The results indicated that compared to the original model based on the parallel dual layer, the modified STI model based on the serial dual layer statistically significantly decreased the MAE and RRMSE of the SMC estimates by 63.0–63.2% and 63.0–63.5%, respectively. The 15 m spatial resolution thermal bands obtained by the DWT data fusion provided more detailed information of SMC but did not significantly improve its estimation accuracy than the 15 m spatial resolution thermal bands by re-sampling using NNM. This implied that the novel method offered insights on how to increase the accuracy of retrieving SMC estimates in vegetated areas.
Zhenhua Liu; Li Zhao; Yiping Peng; Guangxing Wang; Yueming Hu. Improving Estimation of Soil Moisture Content Using a Modified Soil Thermal Inertia Model. Remote Sensing 2020, 12, 1719 .
AMA StyleZhenhua Liu, Li Zhao, Yiping Peng, Guangxing Wang, Yueming Hu. Improving Estimation of Soil Moisture Content Using a Modified Soil Thermal Inertia Model. Remote Sensing. 2020; 12 (11):1719.
Chicago/Turabian StyleZhenhua Liu; Li Zhao; Yiping Peng; Guangxing Wang; Yueming Hu. 2020. "Improving Estimation of Soil Moisture Content Using a Modified Soil Thermal Inertia Model." Remote Sensing 12, no. 11: 1719.
Unplanned urban settlements exist worldwide. The geospatial information of these areas is critical for urban management and reconstruction planning but usually unavailable. Automatically characterizing individual buildings in the unplanned urban village using remote sensing imagery is very challenging due to complex landscapes and high-density settlements. The newly emerging deep learning method provides the potential to characterize individual buildings in a complex urban village. This study proposed an urban village mapping paradigm based on U-net deep learning architecture. The study area is located in Guangzhou City, China. The Worldview satellite image with eight pan-sharpened bands at a 0.5-m spatial resolution and building boundary vector file were used as research purposes. There are ten sites of the urban villages included in this scene of the Worldview image. The deep neural network model was trained and tested based on the selected six and four sites of the urban village, respectively. Models for building segmentation and classification were both trained and tested. The results indicated that the U-net model reached overall accuracy over 86% for building segmentation and over 83% for the classification. The F1-score ranged from 0.9 to 0.98 for the segmentation, and from 0.63 to 0.88 for the classification. The Interaction over Union reached over 90% for the segmentation and 86% for the classification. The superiority of the deep learning method has been demonstrated through comparison with Random Forest and object-based image analysis. This study fully showed the feasibility, efficiency, and potential of the deep learning in delineating individual buildings in the high-density urban village. More importantly, this study implied that through deep learning methods, mapping unplanned urban settlements could further characterize individual buildings with considerable accuracy.
Zhuokun Pan; Jiashu Xu; Yubin Guo; Yueming Hu; Guangxing Wang. Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net. Remote Sensing 2020, 12, 1574 .
AMA StyleZhuokun Pan, Jiashu Xu, Yubin Guo, Yueming Hu, Guangxing Wang. Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net. Remote Sensing. 2020; 12 (10):1574.
Chicago/Turabian StyleZhuokun Pan; Jiashu Xu; Yubin Guo; Yueming Hu; Guangxing Wang. 2020. "Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net." Remote Sensing 12, no. 10: 1574.
Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model’s prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.
Liyong Fu; Guangshuang Duan; Qiaolin Ye; Xiang Meng; Peng Luo; Ram P. Sharma; Hua Sun; Guangxing Wang; Qingwang Liu. Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data. Remote Sensing 2020, 12, 1066 .
AMA StyleLiyong Fu, Guangshuang Duan, Qiaolin Ye, Xiang Meng, Peng Luo, Ram P. Sharma, Hua Sun, Guangxing Wang, Qingwang Liu. Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data. Remote Sensing. 2020; 12 (7):1066.
Chicago/Turabian StyleLiyong Fu; Guangshuang Duan; Qiaolin Ye; Xiang Meng; Peng Luo; Ram P. Sharma; Hua Sun; Guangxing Wang; Qingwang Liu. 2020. "Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data." Remote Sensing 12, no. 7: 1066.
Accurately estimating growing stem volume (GSV) is very important for forest resource management. The GSV estimation is affected by remote sensing images, variable selection methods, and estimation algorithms. Optical images have been widely used for modeling key attributes of forest stands, including GSV and aboveground biomass (AGB), because of their easy availability, large coverage and related mature data processing and analysis technologies. However, the low data saturation level and the difficulty of selecting feature variables from optical images often impede the improvement of estimation accuracy. In this research, two GaoFen-2 (GF-2) images, a Landsat 8 image, and fused images created by integrating GF-2 bands with the Landsat multispectral image using the Gram–Schmidt method were first used to derive various feature variables and obtain various datasets or data scenarios. A DC-FSCK approach that integrates feature variable screening and a combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (kNN) algorithm was proposed and compared with the stepwise regression analysis (SRA) and random forest (RF) for feature variable selection. The DC-FSCK considers the self-correlation and combination effect among feature variables so that the selected variables can improve the accuracy and saturation level of GSV estimation. To validate the proposed approach, six estimation algorithms were examined and compared, including Multiple Linear Regression (MLR), kNN, Support Vector Regression (SVR), RF, eXtreme Gradient Boosting (XGBoost) and Stacking. The results showed that compared with GF-2 and Landsat 8 images, overall, the fused image (Red_Landsat) of GF-2 red band with Landsat 8 multispectral image improved the GSV estimation accuracy of Chinese pine and larch plantations. The Red_Landsat image also performed better than other fused images (Pan_Landsat, Blue_Landsat, Green_Landsat and Nir_Landsat). For most of the combinations of the datasets and estimation models, the proposed variable selection method DC-FSCK led to more accurate GSV estimates compared with SRA and RF. In addition, in most of the combinations obtained by the datasets and variable selection methods, the Stacking algorithm performed better than other estimation models. More importantly, the combination of the fused image Red_Landsat with the DC-FSCK and Stacking algorithm led to the best performance of GSV estimation with the greatest adjusted coefficients of determination, 0.8127 and 0.6047, and the smallest relative root mean square errors of 17.1% and 20.7% for Chinese pine and larch, respectively. This study provided new insights on how to choose suitable optical images, variable selection methods and optimal modeling algorithms for the GSV estimation of Chinese pine and larch plantations.
Xinyu Li; Zhaohua Liu; Hui Lin; Guangxing Wang; Hua Sun; Jiangping Long; Meng Zhang. Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm. Remote Sensing 2020, 12, 871 .
AMA StyleXinyu Li, Zhaohua Liu, Hui Lin, Guangxing Wang, Hua Sun, Jiangping Long, Meng Zhang. Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm. Remote Sensing. 2020; 12 (5):871.
Chicago/Turabian StyleXinyu Li; Zhaohua Liu; Hui Lin; Guangxing Wang; Hua Sun; Jiangping Long; Meng Zhang. 2020. "Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm." Remote Sensing 12, no. 5: 871.
Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.
Feiyang Zhang; Guangxing Wang; Yueming Hu; Liancheng Chen; A-Xing Zhu. Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality. Sensors 2020, 20, 336 .
AMA StyleFeiyang Zhang, Guangxing Wang, Yueming Hu, Liancheng Chen, A-Xing Zhu. Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality. Sensors. 2020; 20 (2):336.
Chicago/Turabian StyleFeiyang Zhang; Guangxing Wang; Yueming Hu; Liancheng Chen; A-Xing Zhu. 2020. "Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality." Sensors 20, no. 2: 336.
Accurately estimating and mapping vegetation cover for monitoring land degradation and desertification of arid and semiarid areas using remotely sensed images is promising but challenging in remote, sparsely vegetated and large areas. In this study, a novel method – geographically weighted logistic regression (GWLR – integrating geographically weighted regression (GWR) and a logistic model) was proposed to improve vegetation cover mapping of Kangbao County, Hebei of China using Landsat 8 image and field data. Additionally, a new method to determine the bandwidth of GWLR is presented. Using cross-validation, GWLR was compared with a globally linear stepwise regression (LSR), a local linear modelling method GWR and a nonparametric method, k-nearest neighbours (kNN) with varying numbers of nearest plots. Results demonstrated (1) the red and near infrared relevant band ratios and vegetation indices significantly improved mapping; (2) the GWLR, GWR and kNN methods led to more accurate predictions than LSR; (3) GWLR reduced overestimations and underestimations compared with LSR, kNN and GWR, and also eliminated negative and very large estimates caused by GWR and LSR; and (4) The maximum distance of spatial autocorrelation could be used to determine the bandwidth for GWLR. Overall, GWLR proved more promising for mapping vegetation cover of arid and semiarid areas.
H. Sun; Q. Wang; G. X. Wang; P. Luo; F. G. Jiang. Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images. The Rangeland Journal 2020, 42, 161 -169.
AMA StyleH. Sun, Q. Wang, G. X. Wang, P. Luo, F. G. Jiang. Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images. The Rangeland Journal. 2020; 42 (3):161-169.
Chicago/Turabian StyleH. Sun; Q. Wang; G. X. Wang; P. Luo; F. G. Jiang. 2020. "Improvement of mapping vegetation cover for arid and semiarid areas using a local nonlinear modelling method and landsat images." The Rangeland Journal 42, no. 3: 161-169.
China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.
Yunlei Cui; Hua Sun; Guangxing Wang; Chengjie Li; Xiaoyu Xu. A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas. Remote Sensing 2019, 11, 3038 .
AMA StyleYunlei Cui, Hua Sun, Guangxing Wang, Chengjie Li, Xiaoyu Xu. A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas. Remote Sensing. 2019; 11 (24):3038.
Chicago/Turabian StyleYunlei Cui; Hua Sun; Guangxing Wang; Chengjie Li; Xiaoyu Xu. 2019. "A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas." Remote Sensing 11, no. 24: 3038.
Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.
Shanshan Liu; Yiping Peng; Ziqing Xia; Yueming Hu; Guangxing Wang; A-Xing Zhu; Zhenhua Liu; Liu; Peng; Xia; Hu; Wang; Zhu. The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data. Sensors 2019, 19, 5127 .
AMA StyleShanshan Liu, Yiping Peng, Ziqing Xia, Yueming Hu, Guangxing Wang, A-Xing Zhu, Zhenhua Liu, Liu, Peng, Xia, Hu, Wang, Zhu. The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data. Sensors. 2019; 19 (23):5127.
Chicago/Turabian StyleShanshan Liu; Yiping Peng; Ziqing Xia; Yueming Hu; Guangxing Wang; A-Xing Zhu; Zhenhua Liu; Liu; Peng; Xia; Hu; Wang; Zhu. 2019. "The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data." Sensors 19, no. 23: 5127.
Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran’s I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China.
Guanglong Ou; Yanyu Lv; Hui Xu; Guangxing Wang. Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images. Remote Sensing 2019, 11, 2750 .
AMA StyleGuanglong Ou, Yanyu Lv, Hui Xu, Guangxing Wang. Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images. Remote Sensing. 2019; 11 (23):2750.
Chicago/Turabian StyleGuanglong Ou; Yanyu Lv; Hui Xu; Guangxing Wang. 2019. "Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images." Remote Sensing 11, no. 23: 2750.
This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.
Ziqing Xia; Yiping Peng; Shanshan Liu; Zhenhua Liu; Guangxing Wang; A-Xing Zhu; Yueming Hu. The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images. Sensors 2019, 19, 4937 .
AMA StyleZiqing Xia, Yiping Peng, Shanshan Liu, Zhenhua Liu, Guangxing Wang, A-Xing Zhu, Yueming Hu. The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images. Sensors. 2019; 19 (22):4937.
Chicago/Turabian StyleZiqing Xia; Yiping Peng; Shanshan Liu; Zhenhua Liu; Guangxing Wang; A-Xing Zhu; Yueming Hu. 2019. "The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images." Sensors 19, no. 22: 4937.
A river watershed is a complex ecosystem, and its spatial structure and temporal dynamics are driven by various natural factors such as soil properties and topographic features, human activities, and their interactions. In this study, we explored the characteristics of the ecosystem and environment of watershed by analyzing and modeling the relationships among socio-economic indices, heavy metal elements and landscape metrics. Landsat 8 data were used to generate a land cover classification map and to derive landscape pattern indices. Governmental finance statistics yearbook data were referred to provide socio-economic indices. Moreover, 9 samples were collected from the upstream to the downstream to obtain the values of heavy metal concentrations in the water body. Then, both correlation and regression analyses were applied to analyze and model the relationships among these indices. The results of this study showed that 1) The ecological status and process (social economy, land cover, water and soil pollution) of this river watershed could be explained by analyzing the relationships among the socio-economic indices, heavy metal elements and landscape pattern indices selected based on correlation analysis; 2) The accumulated socio-economic indices were significantly correlated with Al, Fe and Ni and should be applied to the integrated assessment of the watershed ecological environment; 3) Cu, Zn and Pb were the main elements that showed significant correlations with the forest land; 4) Some landscape patterns indices such as Total Area (TA) and Effective Mesh Size (MESH) could be used to the integrated assessment of the watershed characteristics because of their strong correlations with the area (or area percentage) of important landscape types; and 5) transportation land had a close relationship with per capita Gross Domestic Product (GDP). This study implied that analyzing and modeling the relationships among the socio-economic indices, heavy metal elements and landscape pattern indices can provide a powerful tool for characterizing the ecosystem of the river watershed and useful guidelines for the watershed management and sustainable development.
Huan Yu; Bo Kong; Zheng-Wei He; Guangxing Wang; Qing Wang. The potential of integrating landscape, geochemical and economical indices to analyze watershed ecological environment. Journal of Hydrology 2019, 583, 124298 .
AMA StyleHuan Yu, Bo Kong, Zheng-Wei He, Guangxing Wang, Qing Wang. The potential of integrating landscape, geochemical and economical indices to analyze watershed ecological environment. Journal of Hydrology. 2019; 583 ():124298.
Chicago/Turabian StyleHuan Yu; Bo Kong; Zheng-Wei He; Guangxing Wang; Qing Wang. 2019. "The potential of integrating landscape, geochemical and economical indices to analyze watershed ecological environment." Journal of Hydrology 583, no. : 124298.