Evaluation of single-date and multi-seasonal spatial and spectral information of Sentinel-2 imagery to assess growing stock volume of a Mediterranean forest

Chrysafis, Irene; Mallinis, Giorgos; Tsakiri, Maria; Πατιάς, Πέτρος

doi:10.1016/j.jag.2018.12.004

Cited by 45 publications

(38 citation statements)

References 63 publications

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“…The multi-spectral images of high quality and availability have been used to predict regional forest volume, prior to the use of other remote sensing technologies [8,32,33]. Sentinel-2 data have emerged as one of the popular data sources for stock volume mapping, due to its higher resolution and the unique red-edge band sensitive to vegetations since the launch of the Sentinel-2A satellite in 2015 [34,35]. Optical remote sensing still has some drawbacks: it is prone to yielding spectral saturation, leading to an underestimation of the forest structure parameters in the dense forest due to its insufficient penetration through the canopy; meanwhile, the impact of the weather could result in the lack of data in cloudy and rainy days.…”

Section: Introductionmentioning

confidence: 99%

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Xie

Cao

et al. 2020

Remote Sensing

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Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

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Section: Introductionmentioning

confidence: 99%

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Xie

Cao

et al. 2020

Remote Sensing

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“…We also found the NIR and the red edge bands as good features in modeling which is due to their sensitivity to the chlorophyll and pigments of tree leaf. Relevant works of Chrysafis et al (2017) [8], Mura et al (2018) [9], and Chrysafis et al (2019) [62] also described these features as influential features for the GSV estimation in their research. Basically, the vegetation indices that use the NIR and red-edge spectral bands have an effective contribution in the GSV estimation.…”

Section: Discussionmentioning

confidence: 95%

Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests

Ataee

Maghsoudi

Latifi

et al. 2019

Forests

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Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1’s polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R2 and RMSE in a suggested GA fitness function. We calculated R2, RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model’s stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.

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“…Vegetation indexes were computed from ESA Sentinel-2 satellite mission, a constellation of two twin heliosynchronous satellites which provide freely available multispectral bands for the entire globe with a spatial resolution ranging between 10 m and 20 m (depending on the spectral band) and a revisit time of 5 days. The capabilities of Sentinel-2 spectral bands and vegetation indexes for wood volume estimation in Mediterranean forest ecosystems has been investigated by Chrysafis et al (2017); Mura et al (2018); Chrysafis et al (2019). We downloaded all the Sentinel-2 images acquired in the time window 27 May 2017 -10 June 2017, obtaining a stack of 43 Top of Atmosphere (TOA) Sentinel-2 images.…”

Section: Case Studymentioning

confidence: 99%

“…To do this, the spectral value of pixels covered by clouds was recomputed as the mean of values of cloud free pixels in the selected time window. Then, seven vegetation indices (Table 1) were computed from Sentinel-2 data taking into account previous studies (Chrysafis et al 2017;Puletti et al 2018;Chrysafis et al 2019). The Pearson's r correlation coefficient was used to investigate the relationships between the wood volume measured in the field and vegetation indices.…”

Section: Case Studymentioning

confidence: 99%

Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information

Marcelli¹,

Mattioli²,

Puletti³

et al. 2020

Silva Fenn.

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Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed.

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Evaluation of single-date and multi-seasonal spatial and spectral information of Sentinel-2 imagery to assess growing stock volume of a Mediterranean forest

Cited by 45 publications

References 63 publications

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests

Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information

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