Retrieval of forest growing stock volume by two different methods using Landsat TM images

Zheng, Sheng; Cao, Chunxiang; Dang, Yongfeng; Xiang, Hai; Zhao, Jian; Zhang, Yuxing; Wang, Xuejun; Guo, Hongwen

doi:10.1080/01431161.2013.860567

Cited by 34 publications

(30 citation statements)

References 30 publications

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“…Lu 13 indicated that the combination of spectral and textural features of Landsat image improved the aboveground biomass estimation in Altamira, and texture was critical for mature forest biomass estimation. Texture measures were also proved useful for retrieving forest growing stock volume of oak in southern Liaoning, China, 14 and for delineating logged forests in Amazonia. 15 Most previous studies retrieved forest age using remote sensing data acquired in growing (leaf-on) season.…”

Section: Introductionmentioning

confidence: 99%

Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons

Wang

et al. 2014

J. Appl. Remote Sens

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This study investigated the ability of Landsat Enhanced Thematic Mapper Plus data acquired in leaf-on and leaf-off seasons to estimate stand age of Larix gmelinii and Betula platyphylla in northeast China. The relationships of six band reflectances, nine vegetation indices, and six texture measures with stand age were examined. Linear and multivariable regression models and multilayer perceptron neural network (MLP NN) were employed to estimate forest age based on these variables. The results indicate that reflectance in short-wave infrared bands and wetness are more significantly correlated with stand age in the leaf-on image, while reflectance in blue and green bands and greenness are more sensitive to stand age in leaf-off image. The MLP NN model can be effectively used to retrieve the stand age; the highest coefficient of determination and minimum root mean square error values of retrieved age are 0.47 and 21.3 years for Larix gmelinii, and 0.60 and 10.1 years for Betula platyphylla, respectively. The predicted age errors increased significantly when stand ages were >100 and >50 years for Larix gmelinii and Betula platyphylla, respectively. Remote sensing data acquired in the leaf-on season is more suitable for estimating forest age than that acquired in the leaf-off season over the study area.

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Section: Introductionmentioning

confidence: 99%

Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons

Wang

et al. 2014

J. Appl. Remote Sens

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“…Thirdly, extracting the feature variables that significantly contribute to the estimation improvement of forest GSV, such as texture measures, topographical, phenological and auxiliary data. Current GSV estimation models focus on the use of spectral variables from remote sensing images and only a few reports take phenological features and auxiliary data into account [17,18,33,34]. Future attention should be paid to the consideration of the variables from different data sources.…”

Section: Implication Of Methods For Improving Gsv Estimation Of Chinementioning

confidence: 99%

“…On one hand, there are a large number of features, vegetation indices and texture measures available. On the other hand, the variables may be interrelated, which leads to information redundancy and affects the improvement of estimation accuracy [15,17,18,30]. A novel method that can provide potential solutions for the challenges is needed.…”

Section: Selection Of Optimal Variable Combinationmentioning

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

Lin

et al. 2020

Remote Sensing

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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.

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“…Alternatively, the satellite-based approach aided by forest inventory can up-scale observed extent and has thus been widely used to estimate GSV or biomass for a continuous spatial distribution (Bijalwan et al, 2010;Gao et al, 2013a). Satellite optical images have been used to estimate biomass and GSV at different scales (Houghton et al, 2007;Anaya et al, 2009;Zheng et al, 2013;Gao et al, 2013b). However, passive optical data can only sense the canopy in two dimensions, thereby making it be insensitive to sub-canopy structure, such as basal area and height of tree (Almeida Filho et al, 2007;Morel et al, 2011).…”

Section: Introductionmentioning

confidence: 98%

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

Gao

Zhu

Deng

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tTimber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process-based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a "space-for-time" substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m 3 ha −1 . The total timber production of LP was estimated to be 7.27 × 10 6 m 3 , with 4.87 × 10 6 m 3 in current GSV and 2.40 × 10 6 m 3 in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from −55.2 to 56.3 m 3 ha −1 . The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service.

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Retrieval of forest growing stock volume by two different methods using Landsat TM images

Cited by 34 publications

References 30 publications

Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons

Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons

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

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

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