Forest Leaf Area Index Inversion Based on Landsat OLI Data in the Shangri-La City
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Abstract
Leaf Area Index (LAI) is an important index that reflects the growth status of forest vegetation and land surface processes. It is of important practical significance to quantitatively and accurately estimate Leaf Area Index. We used the Landsat-8 operational land imager single-band images, and 15 vegetation indices that were extracted from the multi-band were combined with the LAI data measured from the CI-110 canopy digital imager to establish the LAI estimation model. Through the leave-one-out cross-validation method, the accuracy of various model estimation results was verified and compared, and the optimal estimation model was obtained to generate the LAI distribution map of Shangri-La City. The results show that: (1) the multivariable model method is better than the single-variable model method when estimating LAI, and its determination coefficient is the highest (R2 = 0.7903). (2) The full-sample dataset is divided into Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine forest for analysis. The coefficient of determination of the model simulation is improved to varying degrees, and the highest R2 increased by 0.1652, 0.1040, 0.1264, and 0.0079, respectively, over the full-sample. The corresponding best models are LAI–DVI (Difference Vegetation Index), LAI–NNIR (normalized near-infrared), LAI–NMDI (Normalized Multi-band Drought Index), and LAI–RVI (Ratio Vegetation Index). (3) The LAI values in Shangri-La City ranged from 0.9654 to 5.5145 and are mainly concentrated in high vegetation coverage areas; and the higher the vegetation coverage level, the higher the LAI value.
Keywords
Leaf Area Index Vegetation index Landsat OLI Shangri-La City ForestIntroduction
Leaf Area Index (LAI) is defined as one-half of the total green leaf area (all sides) per unit of ground surface area (Chen and Black 2010). Among many surface biogeochemical parameters that can be derived from satellite spectral measurements, LAI is a vegetation structural parameter of fundamental importance for the quantitative analysis of many physical and biological processes related to vegetation dynamics and their effects on the global carbon cycle and climate (Chen et al. 2002). LAI is an important input factor for the study of the forest carbon cycle and water cycle mechanism models (Chang et al. 2016), which are vital to describe the vegetation canopy and assess plant growth conditions and their health status (Wang et al. 2018). LAI also plays an important role in the quantitative remote sensing inversion of vegetation and is widely used in the research of vegetation canopy reflectance models and climate models (Ren et al. 2015).
Forest land LAI measurement methods include direct and indirect measurements. Although the direct measurement of the LAI method has high precision, it is very laborious and destructive to the plant itself (Zhu et al. 2014). It is also time-consuming, labor-intensive or simply unattainable, as well as difficult to expand to a large area LAI measurement (Han et al. 2014). Therefore, LAI observation mostly uses indirect measurement methods. The indirect measurement method uses some measurement parameters or optical instruments to obtain the Leaf Area Index, which is convenient and quick to measure. These include the TRAC plant canopy analyzer, the AccuPAR plant canopy analyzer, the LAI-2000 plant canopy analyzer, and the CI-110 vegetation canopy digital imager. However, the results obtained by indirect methods need to be corrected. With increasing interest in LAI spatial models and the need for scientific research, the use of remote sensing data to estimate LAI has become the most attractive method because it provides the most effective method for large area LAI estimation (Li et al. 2014; He et al. 2013; Zhang et al. 2018). The quantitative inversion of LAI based on remote sensing methods usually includes an optical model method and a statistical model method. The former has a certain universality, but the inversion is time-consuming and the operation is complex, which can result in an incorrect inversion result (Wang et al. 2015; Li et al. 2011). The statistical model method aims to establish a linear or nonlinear model between the measured LAI and the vegetation index to achieve an estimation of the LAI (Han et al. 2014). The model is simple and easy to calculate and has high precision, but it requires a large amount of measured data (Wang et al. 2016).
Many scholars have conducted LAI studies based on a statistical model of vegetation index. These researchers methods include using TM data as the data source to analyze the correlation between vegetation index and measured LAI (Xu et al. 2003), using the best vegetation index model to construct the LAI estimation model of forest land using random forest (Yao et al. 2017), and using the method of inverting the corn canopy Leaf Area Index by using the vegetation index as a preliminary judgment basis for the growth status of maize (Su et al. 2018). The radiation transmission model was used to estimate the forest LAI model, and the NDVI–LAI (Normalized Difference Vegetation Index and Leaf Area Index) relationship of deciduous forest land was studied during 1996–2001 (Wang et al. 2005). These studies were focused on the assessment of a LAI model inversion approach applied to multitemporal optical data over an agricultural region that had various crop types with different crop calendars (González-Sanpedro et al. 2008). The relationship between LAI and NDVI was quantified using empirical relationships between plant community-specific LAI and daily scale accumulation (with a 0 °C threshold) (Juutinen et al. 2017).
The above research shows that using the empirical relationship between vegetation index and measured LAI, the LAI inversion model provides an effective technical means for LAI estimation research in areas with complex terrain and difficult conditions, such as hard to reach artificial measurement areas. However, the research divides the samples into modeling sets and inspection sets and adopts a sample retention test method, which greatly reduces the sample utilization rate. This makes it more difficult to perform work for Leaf Area Index acquisition in large regions and in difficult environments. The leave-one-out cross-validation (LOOCV) method can be used for the study of fewer sample points.
In this study, therefore, our objective is to use the single-band data of remote sensing data and multiple vegetation indices calculated through multiple bands to conduct regression analysis with the land surface measured forestland LAI data. Using this method, an optimal estimation model for LAI remote sensing inversion of forest land (including forest types) is established. The accuracy of the remote sensing inversion model is verified by the LOOCV method. This provides a reference for the inversion of LAI in the woodland of Shangri-La City using empirical statistical models. Landsat OLI was used as the data source for remotely sensed data, and the LAI data were measured using the CI-110 vegetation canopy digital imager as a field acquisition tool to collect the LAI of each forest land from July–September 2016 and July–September 2017.
Data and Methods
Study Area
Location of Shangri-La City, Yunnan Province, China
Data Sources
Landsat 8 OLI Image
Shangri-La City has distinct wet and dry seasons; the summer and autumn seasons (June to October each year) are dominated by rainy days; therefore, remote sensing images have a greater cloud cover. Thus, the dry season remote sensing image (December 2016 Landsat OLI image) was selected as the data source, and the data acquisition dates were December 6, 2016 (strip number 131, line number 41) and December 13, 2016 (strip number 132, line numbers 40–41), when the cloud volume was below 1.18%. These data include 7 multispectral bands with 30 m resolution (wavelength range of 0.43–2.29 μm), one panchromatic band with 15 m resolution (wavelength range of 0.50–0.68 μm), and 2 thermal infrared bands with 100 m resolution (wavelength range of 10.6–12.5 μm). The images were downloaded from the USGS (https://glovis.usgs.gov/).
LAI Measured Data
Results of LAI for descriptive statistics
Types | Sample numbers | Minimum | Maximum | Average | SD | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|
Alpine Pine | 9 | 1.01 | 3.21 | 1.73 | 0.64 | 1.14 | 1.07 |
Oak | 5 | 1.40 | 2.35 | 1.81 | 0.34 | 0.46 | − 0.85 |
Spruce–fir | 7 | 0.56 | 2.14 | 1.51 | 0.52 | − 0.61 | − 0.21 |
Yunnan Pine | 13 | 1.05 | 2.96 | 1.76 | 0.46 | 1.13 | 2.44 |
Full-sample | 34 | 0.56 | 3.21 | 1.73 | 0.53 | 0.65 | 1.30 |
Auxiliary Data
Auxiliary data include Shangri-La City vector zoning data and land use classification maps. The land use data are from the Chinese land use data for 2015 from the Chinese Academy of Sciences’ Resource and Environment Science Data Center. The data update is based on the 2010 data that is based on Landsat 8 remote sensing images and is generated through manual interpretation. The land use types include 6 first-level types and 25 second-level types of cultivated land, forest land, grassland, water areas, residential areas, and unused land. China’s land use remote sensing monitoring database is China’s highest accuracy database of land use remote sensing monitoring data products, which has played an important role in the national land resources survey, hydrology, and ecological research. Therefore, the land use pattern is used to further analyze the spatial distribution of LAI.
Methods
Based on the Landsat OLI data calculated radiation, atmospheric correction, and other preprocessing, the multi-band vegetation index was calculated. The field data obtained were used for the elimination of invalid data and the classification of sample types. The 34 measured samples were separated from the single-band and multi-band regression analysis that was performed to establish the LAI model. Using the LOOCV method to evaluate the accuracy of the model, the best sample models for the full-sample and each sample were selected. Finally, the LAI was inverted, and the LAI status of the Shangri-La City forest land was analyzed in combination with land use types and vegetation coverage.
Univariate Model Method
Seven bands of Landsat OLI multispectral data were used to perform the quadratic polynomial regression analysis with the measured LAI, and a regression model for the single-band gray value and LAI was established.
Multivariate Modeling Method
Vegetation indices and expressions
Vegetation index | Expression |
---|---|
Ratio Vegetation Index (Jordan 1969) | RVI = nir/r |
Normalized near-infrared (Sripada et al. 2005) | NNIR = nir/(nir + r + green) |
Soil Adjusted Vegetation Index (Huete 1988) | SAVI = 1.5 × (nir − r)/(nir + r + 0.5) |
Normalized Difference Vegetation Index | NDVI = (nir − r)/(nir + r) |
Wide Dynamic Range Vegetation Index (Huang et al. 2017) | WDRVI = (0.2 × nir − r)/(0.2 × nir + r) |
Normalized red band (Sripada et al. 2005) | NR = r/(nir + r + green) |
Green Normalized Difference Vegetation Index (Gitelson et al. 1996) | GNDVI = (nir − green)/(nir + green) |
Difference Vegetation Index (Tucker 1979) | DVI = nir − r |
Renormalized Difference Vegetation Index (Roujean and Breon 1995) | \( {\text{RDVI}} = \sqrt {\left( {{\text{nir}} - r} \right)^{2} /\left( {{\text{nir}} + r} \right)} \) |
Normalized Multi-band Drought Index (Wang and Qu 2007) | NMDI = (nir − (swir1 − swir2))/(nir − (swir1 + swir2)) |
Structure Insensitive Pigment Index (Penuelas et al. 2010) | SIPI = (nir − blue)/(nir + blue) |
Normalized green-band (Sripada et al. 2005) | NG = green/(nir + r + green) |
Green Ratio Vegetation Index (Gitelson et al. 1996) | GRVI = nir/green − 1 |
Modified Soil Adjusted Vegetation Index (Qi et al. 1994) | \( {\text{MSAVI}} = 2 \times {\text{nir}} + 1 - \sqrt {\left( {2 \times {\text{nir}} + 1} \right)^{2} - 8 \times \left( {{\text{nir}} - r} \right)/2} \) |
Plant Senescence Reflectance Index (Merzlyak et al. 2010) | PSRI = (r − blue)/nir |
Estimation of Vegetation Coverage
In the vegetation coverage estimation model based on the dimidiate pixel, the extreme values of NDVI are not necessarily \( {\text{NDVI}}_{\hbox{max} } \) and NDVImin due to the inevitable noise in the image. Therefore, in this paper, we take the maximum and minimum values within the confidence interval of 5–95%.
Results
Estimating LAI by the Univariate Model Method
Quadratic polynomial regression relationship between full-sample LAI and single-band reflectivity
Band | Blue | Green | Red | Near-infrared | Short wave infrared 1 | Short wave Infrared 2 |
---|---|---|---|---|---|---|
R 2 | 0.0677 | 0.1114 | 0.0981 | 0.1211 | 0.0642 | 0.0961 |
Estimating LAI by the Multivariate Model Method
The relationship between 4 well-fitting vegetation indices and measured LAI in the full-sample
Linear regression relationship between LAI and the vegetation index of various samples
VI | Full-sample | Alpine Pine forest | Oak forest | Spruce–fir forest | Yunnan Pine forest |
---|---|---|---|---|---|
RVI | 0.6251 | 0.7033 | 0.6429 | 0.6266 | 0.633 |
NNIR | 0.5913 | 0.7102 | 0.7291 | 0.7331 | 0.5484 |
SAVI | 0.5803 | 0.7464 | 0.6772 | 0.6214 | 0.5296 |
NDVI | 0.5736 | 0.7161 | 0.6586 | 0.6256 | 0.5232 |
WDRVI | 0.5082 | 0.6406 | 0.7439 | 0.6305 | 0.4931 |
NR | 0.5063 | 0.6828 | 0.4103 | 0.4703 | 0.502 |
GNDVI | 0.5013 | 0.629 | 0.6876 | 0.6557 | 0.4622 |
DVI | 0.4487 | 0.7903 | 0.5984 | 0.2397 | 0.474 |
RDVI | 0.4148 | 0.6581 | 0.6633 | 0.2435 | 0.4173 |
NMDI | 0.3915 | 0.4371 | 0.0092 | 0.7515 | 0.3294 |
SIPI | 0.2745 | 0.5811 | 0.5708 | 0.072 | 0.23 |
NG | 0.26 | 0.3669 | 0.636 | 0.0968 | 0.2408 |
GRVI | 0.11 | 0.1064 | 0.4438 | 0.218 | 0.2824 |
MSAVI | 0.0703 | 0.1642 | 0.3747 | 0.0003 | 0.0429 |
PSRI | 0.0018 | 0.0043 | 0.4047 | 0.0169 | 0.0293 |
Inversion Accuracy Evaluation
The correlation between the analog value of each sample model and measured values
Full-sample model | LAI–RVI | LAI–NDVI | LAI–SAVI | LAI–NNIR |
---|---|---|---|---|
R | 0.7501 | 0.7088 | 0.7138 | 0.7251 |
RMSEP | 0.3436 | 0.3669 | 0.3642 | 0.3578 |
Alpine Pine model | LAI–RVI | LAI–DVI | LAI–SAVI | LAI–NNIR |
---|---|---|---|---|
R | 0.6169 | 0.8104 | 0.6971 | 0.6484 |
RMSEP | 0.5399 | 0.3778 | 0.4787 | 0.5106 |
Oak model | LAI-GNDVI | LAI-WDRVI | LAI–SAVI | LAI–NNIR |
---|---|---|---|---|
R | 0.6188 | 0.6574 | 0.6068 | 0.6499 |
RMSEP | 0.5776 | 0.4331 | 0.3264 | 0.4263 |
Spruce–fir model | LAI-GNDVI | LAI-WDRVI | LAI–NMDI | LAI–NNIR |
---|---|---|---|---|
R | 0.6101 | 0.5613 | 0.7448 | 0.6820 |
RMSEP | 0.4246 | 0.4479 | 0.3529 | 0.3919 |
Yunnan Pine model | LAI–RVI | LAI–NDVI | LAI–SAVI | LAI–NNIR |
---|---|---|---|---|
R | 0.6337 | 0.5295 | 0.5319 | 0.5327 |
RMSEP | 0.3612 | 0.4027 | 0.4020 | 0.4004 |
From Tables 4 and 5, the full-sample was divided into Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine, and the coefficient of determination of the model simulation is increased by 0.1652, 0.1040, 0.1264, and 0.0079, respectively, compared with the highest R2 of the full-sample. We can see that the best fitting models for the full-sample, Alpine Pine, Oak, Spruce–fir and Yunnan Pine are LAI–RVI, LAI–DVI, LAI–NNIR, LAI–NMDI, and LAI–RVI, respectively. However, due to the special vegetative conditions in the study area, it is difficult to realize the remote sensing differentiation of the 4 forest land types in Shangri-La City. Therefore, the full-sample model is used to invert the Shangri-La City LAI.
LAI Mapping
LAI distribution map
Fractional vegetation cover
LAI statistics within each vegetation coverage
VC level | LAI statistics | |||
---|---|---|---|---|
LAImin | LAImax | LAIavg | The proportion of LAI (%) | |
LVC | 0.9654 | 1.1088 | 1.0388 | 11.6327 |
MVC | 1.1088 | 1.3816 | 1.2360 | 19.6515 |
MHVC | 1.3816 | 1.7516 | 1.5644 | 20.8492 |
HVC | 1.7516 | 5.5145 | 2.3151 | 47.8666 |
Conclusions and Discussion
- 1.
The multivariate model method is more effective than the univariate model method when estimating LAI. In the multivariate model for estimating LAI, the vegetation index extracted from the remote sensing image has a good correlation with measured LAI. This is similar to the results of Tang et al. (2014), who believed that the 2 variables have a better fit to the leaf area than the univariate ones. The best fitting indices in the full-sample, Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine forest are RVI (R2 = 0.6251), DVI (R2 = 0.7903), WDRVI (R2 = 0.7439), NMDI (R2 = 0.7515), and RVI (R2 = 0.6330), respectively. All of the samples in the study area were divided into Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine forest samples. In addition to Yunnan Pine forest, the determination coefficient of samples of other forest land is higher than full-sample in the study. Zhu et al. (2014) states that classifying a sample as a single sample has a higher coefficient of determination and simulation accuracy than in full-sample studies.
- 2.
Using the leave-one-out method to verify the 4 kinds of indices with good fits for 5 samples, it is found that the best fitting models for the full-sample, Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine forest samples are LAI–RVI (y = 0.8298x + 0.1356, R = 0.7501, RMSEP = 0.3436), LAI–DVI (y = 1.2478x − 0.0375, R = 0.8104, RMSEP = 0.3778), LAI–NNIR (y = 5.6094x − 0.8462, R = 0.6499, RMSEP = 0.4263), LAI–NMDI (y = 4.5813x − 1.6694, R = 0.7448, RMSEP = 0.3529), and LAI–RVI (y = 0.6866x + 0.4341, R = 0.6337, RMSEP = 0.3612), respectively.
- 3.
In the LAI inversion, Shangri-La city LAI values are distributed from 0.9654 to 5.5145, and the higher the VC level, the higher the LAI value. LAI is mainly distributed in the HVC area.
The estimation of the Leaf Area Index is a complicated process. The research results of estimating LAI based on the correlation between vegetation index and measured LAI are only preliminary, and many problems need to be further studied. For example, due to the limitation of data, the measured data (mainly acquired in October) have a certain degree of asynchronism, which will affect the accuracy of the inversion, so later research should strive to achieve synchronization. In addition, there are only 34 effective samples of the measured LAI, which will also have some influence on the inversion accuracy.
Notes
Acknowledgements
All authors made great contributions to this study. This research was funded by the National Natural Science Foundation of China (No. 41271230) and the Reserve Personnel Training Program of Middle-aged Academic and Technology Leaders of Yunnan Province (No. 2008PY056).
Compliance with Ethical Standards
Conflict of interest
All authors declare that they have no conflict of interest.
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