China’s larch stock volume estimation using Sentinel-2 and LiDAR data

Yu, Tao; Pang, Yong; Liang, Xiaojun; Jia, Wen; Bai, Yu; Fan, Yilin; Chen, Dongsheng; Liu, Xianzhao; Deng, Guang; Li, Chonggui; Sun, Xiangnan; Zhang, Zhidong; Jia, Weiwei; Zhao, Zhonglong; Wang, Xiao

doi:10.1080/10095020.2022.2105754

Cited by 7 publications

(4 citation statements)

References 61 publications

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“…Moreover, many authors have observed that canopy data obtained by optical and radar sensors tend to be saturated in excessively dense forests (approximately at 250 m 3 ha −1 or 150 Mg ha −1 ), which greatly reduces the accuracy of estimation (e.g., [48,59,78]), although this phenomenon was generally not observed in our cross-validation results from 100 runs, for two possible reasons: (i) the lack or scarcity of mature forest stands and (ii) the use of indexes related to red-edge and texture variables as predictors. Some authors have reported that red-edge indexes (e.g., [79,80]) and texture variables [81] can overcome the saturation problem and increase the accuracy of estimation of forest yield variables, suggesting that inclusion of these variables may contribute to increasing the upper limit of saturation reported in the bibliography.…”

Section: Model Accuracy and Role Of Different Groups Of Predictor Var...mentioning

confidence: 99%

Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data

Novo-Fernández,

López-Sánchez,

Cámara-Obregón

et al. 2024

Forests

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In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained.

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Section: Model Accuracy and Role Of Different Groups Of Predictor Var...mentioning

confidence: 99%

Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data

Novo-Fernández,

López-Sánchez,

Cámara-Obregón

et al. 2024

Forests

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“…In this paper, the pure pixels were selected based on the coefficients of variation in each GLASS pixels. The coefficients of variation were defined as the ratio of the standard deviation to the mean value [40] of Landsat NDVI in each GLASS pixels. The minimum 30 % coefficient of variation of the pixels for each forest type (ENF, DBF, MF, DNF) were selected as the pure pixels.…”

Section: ) Downscaling Of Glass Gpp/nppmentioning

confidence: 99%

Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

Pang

Sun

et al. 2022

IEEE Access

Self Cite

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Accurately estimating vegetation productivity in the forest areas is important for studying the terrestrial ecosystem and carbon cycles. Global LAnd Surface Satellite (GLASS) vegetation production datasets provide new long-term basic products of gross primary production (GPP) and net primary production (NPP) for monitoring the issues related with carbon exchange and carbon storage. But the coarse spatial resolution of the GLASS GPP/NPP products have limited their application in ecosystem service assessment in regional scales. In this paper, a spatial downscaling method based on GLASS vegetation production datasets and four typical deep learning methods (deep neural network, convolutional neural network, back propagation neural network and recurrent neural network) was proposed to generate high resolution GPP/NPP in the forest areas in the upper Luanhe River basin in the north of Hebei Province in China. Then the downscaled GPP/NPP were validated with ground measurement data and reference high resolution GPP/NPP data, and the accuracy of downscaled GPP/NPP from different deep learning methods were compared. Results of this paper indicated the applicability and feasibility of deep learning methods in downscaling GPP/NPP. Direct validation and cross validation demonstrated that downscaled GPP/NPP using convolutional neural network obtained the highest accuracy.

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“…Yu proposed a method that combined multi-spectral satellite images and airborne laser scanning data to estimate the forest stock of larch in China, and used the random forest model for estimation, which proved that the method had certain applicability and high accuracy. In addition, the fusion of hyperspectral remote sensing and other data sources has also achieved some results in forest stock estimation [32]. Gao combined airborne point clouds and hyperspectral data, adopted a random forest screening method and constructed multiple stepwise regression to estimate forest above-ground biomass, and the results showed that the fusion of multi-source data could significantly improve the estimation accuracy of the model [33].…”

Section: Introductionmentioning

confidence: 99%

Satellite Image Fusion Airborne LiDAR Point-Clouds-Driven Machine Learning Modeling to Predict the Carbon Stock of Typical Subtropical Plantation in China

Fan,

Zhang,

Zhou

et al. 2024

Forests

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In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation in forest emission reduction project evaluation. In this paper, we propose a data-driven machine learning framework and method for predicting plantation stock based on airborne LiDAR + satellite remote sensing, and carried out experimental verification at the site of the National Forest emission reduction project in Southern China. We used step-up regression and random forest (RF) to screen LiDAR and Landsat 8 OLI multispectral indicators suitable for the prediction of plantation stock, and constructed a plantation stock model based on machine learning (support vector machine regression, RF regression). Our method is compared with traditional statistical methods (stepwise regression and partial least squares regression). Through the verification of 57 plantation field survey data, the accuracy of the stand estimation model constructed using the RF method is generally better (ΔR2 = 0.01~0.27, ΔRMSE = 1.88~13.77 m3·hm−2, ΔMAE = 1.17~13.57 m3·hm−2). The model evaluation accuracy based on machine learning is higher than that of the traditional statistical method, and the fitting R2 is greater than 0.91, while the fitting R2 of the traditional statistical method is 0.85. The best fitting models were all support vector regression models. The combination of UAV point clouds and satellite multi-spectral images has the best modeling effect, followed by LiDAR point clouds and Landsat 8. At present, this method is only applicable to artificial forests; further verification is needed for natural forests. In the future, the density and quality of higher clouds could be increased. The validity and accuracy of the method were further verified. This paper provides a method for predicting the accumulation of typical Chinese plantations at the forest farm scale based on the “airborne LiDAR + satellite remote sensing” data-driven machine learning modeling, which has potential application value for the current carbon neutrality goal of the southern plantation forest emission reduction project.

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China’s larch stock volume estimation using Sentinel-2 and LiDAR data

Cited by 7 publications

References 61 publications

Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data

Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data

Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

Satellite Image Fusion Airborne LiDAR Point-Clouds-Driven Machine Learning Modeling to Predict the Carbon Stock of Typical Subtropical Plantation in China

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