Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images

et al. 2020

Remote Sensing

Self Cite

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 Remote Sens. 2020, 12, 871 2 of 24 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.

Section: Effective Methods For Improving Spectral Variable Selection contrasting

confidence: 81%

Section: Effective Methods For Improving Spectral Variable Selection mentioning

confidence: 99%

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

Liu

et al. 2020

Remote Sensing

Self Cite

“…Facing the challenge of spectral saturation, the models and remote sensing images are initially considered to increase the saturation levels [26,27]. Excepting the linear and non-linear models, several complexed machine learning and deep learning algorithms, such as support vector machine model (SVR) [28,29], random forest model (RF) [30], K-nearest neighbor method (KNN) [31], CNN and ensemble learning algorithms, are often employed to map GSV in various forest and the results showed that these models can improve the accuracy of GSV by delaying the saturation.…”

Section: Introductionmentioning

confidence: 99%

“…For the parameters approach, the saturation level was regarded as one of parameters. The semi-exponential model is traditional parameters approach for estimating the saturation levels using polarmetric SAR images, and the saturation level is one parameter of the model directly derived from solving the models [27,46]. Additionally, it has been found that the principle of the kriging model based on the semi-covariance function in geostatistics is similar to the phenomenon of spectral saturation, and a spherical model is also used to quantitatively estimate the saturation values of AGB using spectral reflectance from Landsat imagery, and the results indicated that the saturation levels of AGB highly related to forest types and slope aspects [13,14].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Saturation of Growing Stem Volume Based on ZY-3 Stereo and Multispectral Images in Planted Coniferous Forest

Zhang

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Long

et al. 2022

Self Cite

Recently, remote sensing (RS) technology are becoming increasingly important technology in estimating forest growing stem volume (GSV) and the saturation issue of spectral variables from various optical sensors severely hinders the improvement of mapping forest GSV, especially in planted forest with high GSV. Forest canopy height is widely considered as one of major factors to increase the saturation levels in mapping GSV. However, it is rather difficult to invert the forest canopy height without precisely external DEM for large regions. In this study, the canopy height model (CHM) was derived from ZY-3 stereo images with subtracting open-sourced external DEM and the response of saturation levels was analyzed by adding forest height in planted coniferous forest (Larch and Chinese pine). To further describe the relationships between the forest height and saturation levels, five datasets with five estimation models (Linear, MLR, SVR, KNN and RF) and three methods of variable selection (Stepwise, LASSO and Pearson) were applied to estimate the forest GSV using corrected CHM and 49 alternative variables extracted from ZY-3 multi-spectral images. Meanwhile, a spherical model was employed to quantitatively describe the saturation levels of combined variables. The results showed that values of rRMSE were decreased from 29.3% to 25% for Larch and from 26.5% to 22.2% for Chinese pine after adding the corrected CHM, respectively. Meanwhile, the saturation level of each combined variable set was successful determined by the spherical model. The results illustrated that the saturation levels of GSV were significantly increased by adding corrected CHM from open-sourced external DEM. Specially, the averaged saturation levels were increased from 220 m 3 /ha to nearly 300m 3 /ha for Chinese pine and from 150 m 3 /ha to 220 m 3 /ha for Larch, respectively. It is proved that ZY-3 stereo and multi-spectral images have great potential for accurate estimation of forest GSV by delaying the saturation levels using extracted CHM with open-sourced external DEM.

“…The forest growing stock Remote Sens. 2021, 13, 4631 2 of 18 volume (GSV) is a crucial indicator of the quality of planted forests [6][7][8]. Typically, fieldmeasured methods are considered the most accurate way to estimate GSV, but the process is time-consuming and labor-intensive [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest

Liu

et al. 2021

Remote Sensing

Self Cite

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce the uncertainty caused by variables and models, this paper proposes a combined strategy involving improved variable selection with the collinearity test and the secondary ensemble algorithm to obtain the optimally combined variables and extract a reliable GSV from several base models. Our study extracted four types of alternative variables from the Sentinel-1A and Sentinel-2A image datasets, including vegetation indices, spectral reflectance variables, backscattering coefficients, and texture features. Then, an improved variable selection criterion with the collinearity test was developed and evaluated based on machine learning algorithms (classification and regression trees (CART), k-nearest neighbors (KNN), support vector regression (SVR), and artificial neural network (ANN)) considering the correlation between variables and GSV (with random forest (RF), distance correlation coefficient (DC), maximal information coefficient (MIC), and Pearson correlation coefficient (PCC) as evaluation metrics), and the collinearity among the variables. Additionally, we proposed a secondary ensemble with an improved weighted average approach (IWA) to estimate the reliable forest GSV using the first ensemble models constructed by Bagging and AdaBoost. The experimental results demonstrated that the proposed variable selection criterion efficiently obtained the optimal combined variable set without affecting the forest GSV mapping accuracy. Specifically, considering the first ensemble, the relative root mean square error (rRMSE) values ranged from 21.91% to 30.28% for Bagging and 23.33% to 31.49% for AdaBoost, respectively. After the secondary ensemble involving the IWA, the rRMSE values ranged from 18.89% to 21.34%. Furthermore, the variance of the GSV mapped by the secondary ensemble with various ranking methods was significantly reduced. The results prove that the proposed combined strategy has great potential to reduce the GSV mapping uncertainty imposed by current variable selection approaches and algorithms.