- freely available
Remote Sens. 2016, 8(8), 647; https://doi.org/10.3390/rs8080647
2. Study Site and Materials
2.1. Study Site
2.2. Field Sampling
2.3. Remote Sensing Imagery Acquisition and Pre-Processing
3.1. Classification for Vegetation Covered Land Area
- Terra ASTER and Envisat ASAR images were input for the classification. We used both optical and SAR images to improve the classification.
- The ROI was selected with the help of high resolution imagery in Google Earth®, containing vegetation, water and other land cover. As for each type of land cover, we selected 12 typical ROIs, where each ROI covered multiple pixels.
- The Maximum Likelihood classifier in ENVI 4.8® was used for the supervised classification. The VCLA were retrieved and the classification accuracy was examined via user’s accuracy and producer’s accuracy.
3.2. Calculations of Relevant Variables
3.2.1. VIs Calculation
3.2.2. Accuracy Assessment
3.3. Optical-Imagery-Only Models
- Simple regression models. They include the usage of both linear and nonlinear models with single independent data. We generated linear and nonlinear regression models using Curve Estimation in IBM SPSS Statistics 20® (SPSS 20). As NDVI is the most widely used vegetation index, and previous biomass models are mainly based on NDVI, it was used as the only independent variable in the curve estimation.
- Multivariable linear regression models. The independent variables include each type of optical remote sensing spectral reflectance and the derived VIs. Ordinary least square (OLS) regression method was applied to select independent variables in SPSS 20 with a p-value equal to or less than 0.05. Variance Inflation Factor (VIF) was calculated to evaluate the potential multicollinearity problem.
- BPNN models. BPNN is one of the most popular neural networks method and is an excellent nonlinear fit theory, minimizing a global error between predicted outputs and measured values and used in many remote sensing studies [56,57]. The independent variables include spectral reflectance and VIs. In this step, part of sampling data was used to generate BPNN models for estimating AGB, whereas the rest is used for testing the model accuracy.
3.4. SAR-Only Models
- Simple regression models. They include the usage of both linear and nonlinear models with single independent data. The independent data is VV or HH polarized microwave backscatter data. We also generated linear and nonlinear regression models using Curve Estimation in SPSS 20.
- Multivariable linear regression models. The independent variables include Envisat ASAR HH/VV data. OLS regression method was also applied to select independent variables in SPSS 20 with a p-value equal to or less than 0.05. VIF was calculated to evaluate the potential multicollinearity.
- BPNN models. The independent variables include microwave backscatter data (, ). Also in this step, part of sampling data is used to generate models, whereas the rest is used for testing the model accuracy.
3.5. Combination Models Using Spectral Reflectance and SAR Data
- Multivariable linear regression models. The independent variables include Envisat ASAR imagery and VIs. OLS regression method was applied to select independent variables in SPSS 20 with a p-value equal to or less than 0.05. VIF was also calculated to evaluate the potential multicollinearity.
- BPNN models. The independent variables include a combination of spectral reflectance, VIs and microwave backscatter data (, ). In this step, part of sampling data is used to generate models, whereas the rest is used for testing the model accuracy.
4.1. Classification for Vegetation Covered Areas
4.2. Models and Predictions
- In the first column, the predicted AGB results were from NDVI data and NDVI regression models. In the second column, the predicted AGB results were from multivariable linear regression models. The regression models for predicting AGB from NDVI were listed in Table 2(a).
- In the first line, the data for predicting AGB were from Terra ASTER images. In the second and the third line, the data were from Landsat ETM+ and HJ-1-B CCD images.
- In the first column of Figure 8a,b, the optical data combined with SAR data for predicting AGB was from Terra ASTER. And the optical data was from Landsat ETM+ and HJ-1-B CCD in the second and third column respectively.
- In the second line, the predicted AGB results were predicted from BPNN models.
5.1. Comparison of Models by Sensor Type
5.2. Comparison of Modeling Methods
5.3. AGB Distribution
- There exists some potential disagreement between remote sensing data and field inventory. Field sampling was carried out in March and April and early May in 2009. All the images were not acquired in this period, but mainly in March or April in the year 2009. The Envisat ASAR image was acquired in August when the vegetation was in another growing season.
- One of the potential reasons that decrease accuracy is that all optical images were resampled to 12.5 m in resolution but the original resolution was 15 m for Terra ASTER, 30 m for Landsat ETM+ and HJ-1-B CCD.
- Differences exist between predicted AGB maps derived from different modeling method and input data. The results should be compared with analysis from other studies, so as to choose better models. Currently there are no AGB studies in this area.
Conflicts of Interest
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|Sensor Type||Data Type||Pass||Acquisition Date|
|Radar||Envisat ASAR HH/VV||Ascending||17 August 2009|
|Radar||ERS-2 SAR VV||Descending||22 March 2009|
|Optical||Terra ASTER||Descending||16 March 2009|
|Optical||Landsat ETM+||Descending||11 April 2009|
|Optical||HJ-1-B CCD||Descending||21 April 2009|
|(a) Optical-Only Models|
|Input||AGB Model (kg/m2)||R2||Significance||VIF|
|Terra ASTER||NDVI||0.775||NDVI: 0.000||/|
|Spectral reflectance and VIs||0.642||NDVI: 0.000||/|
|Landsat ETM+||NDVI||0.664||NDVI: 0.000||/|
|Spectral reflectance and VIs||0.559||NDVI: 0.000||/|
|HJ-1-B CCD||NDVI||0.699||NDVI: 0.000||/|
|Spectral reflectance and VIs||0.548||NIR: 0.000||/|
|(b) SAR-only models|
|Input||AGB model (kg/m2)||R2||Significance||VIF|
|Envisat ASAR||0.874||: 0.000||/|
|,||0.823||: 0.000||: 4.868|
|: 0.000||: 4.868|
|(c) Envisat ASAR and optical models|
|Input||AGB model (kg/m2)||R2||Significance||VIF|
|Envisat ASAR||0.806||: 0.000||: 2.853|
|Terra ASTER||RDVI: 0.040||RDVI: 2.853|
|Envisat ASAR, Landsat ETM+||0.807||: 0.000||: 2.117|
|GEMI: 0.039||GEMI: 2.117|
|Envisat ASAR||0.842||: 0.000||: 2.136|
|HJ-1-B CCD||RDVI: 0.047||RDVI: 2.519|
|(d) ERS-2 and optical models|
|Input||AGB model (kg/m2)||R2||Significance||VIF|
|ERS-2||0.870||: 0.000||: 2.965|
|Terra ASTER||RDVI: 0.001||RDVI: 2.965|
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