Estimation of Semiarid Forest Canopy Cover Using Optimal Field Sampling and Satellite Data with Machine Learning Algorithms

Mahdavi, Ali; Aziz, Jalal

doi:10.1007/s12524-020-01102-x

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…On the other hand, choosing an appropriate plot area for forest canopy cover density mapping is very critical due to the spatial variation of forest canopy cover classes in the Zagros forest zone. So, some researchers have done some studies (Adeli et al, 2008;Mahdavi and Aziz, 2020) in semi-arid forest zones to nd the best plot area for eld canopy cover surveys. For instance, Adeli et al (2008) stated and suggested the different sample areas for different forest canopy cover density classes.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

Naseri

Shataee

2021

Preprint

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Background: Accurate mapping and monitoring canopy cover using remote sensing data as an alternative way for field surveys are very important for forest managers, particularly in the spare and low dense forests. Due to being area-based of canopy cover density and mixing spectral responses of tree crowns and soil in the thin and semi-dense forests, finding the high-performance method of classification is a challenge particularly on high-resolution imagery. In this study, we compared produced maps of canopy cover using direct remote sensing and indirect (RS-GIS-based) methods in two forest sites on the Quickbird and WorldView-2 images using the Artificial Neural Network (ANN) algorithm. Also, the optimal plot area was examined by different plot areas.Results: In the direct method and based on the obtained results, in the Dashte Barm using Quickbird image, the best classification was for plots of 7500 m2 with an overall accuracy of 56.57% and kappa coefficient of 0.32. In the Ilam site and on the WorldView-2 image, the best result is obtained by the plots of 5,000 m2 area with an overall accuracy of 45.71% and the kappa coefficient of 0.263. The results of accuracy assessment of maps of indirect method in the Dashte Barm site for grids with different areas showed that the best classifications obtained from sample plot areas of 10000 m2 with overall accuracy of 82.69% and Kappa coefficient of 0.744; but in the Ilam sites the best result was obtained using sample area of 1000 m2 with overall accuracy of 74.27% and the Kappa coefficient of 0.690. Conclusions: The results exposed that use of the RS-GIS based method could considerably improve the results compare to direct classification. Also, the results showed concerning the conditions of canopy cover density of forest stands, plots with different areas can be used to map of forest canopy cover density; however, for direct classification the use of plots with areas of 5000 m2 and more are suitable in sparse forests. For RS-GIS based method, the plot areas of 1000 m2 are optimal due to time and cost saving.

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Section: Introductionmentioning

confidence: 99%

Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

Naseri

Shataee

2021

Preprint

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“…Heterogeneous landscape can be better represented by plotting the coverage percentage of the land elements on a continuous scale [22,23]. Currently, several parameters can represent forest cover information on a continuous scale, such as forest canopy cover is defined as the proportion of the forest floor covered by the vertical projection of the tree crowns [24]. The canopy closure, also known as crown closure, is an integrated measure of the canopy "over a segment of the sky hemisphere above one point on the ground" [25].…”

Section: Introductionmentioning

confidence: 99%

Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

Liu

Liang

et al. 2021

Remote Sensing

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The accurate monitoring of forest cover and its changes are essential for environmental change research, but current satellite products for forest coverage carry many uncertainties. This study used 30-m Landsat-8 data, and aggregated 1-m GaoFen-2 (GF-2) satellite images to construct the training samples and used multiple machine learning algorithms (MLAs) to estimate the fractional forest cover (FFC) in China’s Three North Region (TNR). In this study, multiple MLAs were merged to construct stacked generalization (SG) models based on the idea of SG, and the performances of the MLAs in the FFC estimation were evaluated. The results of the 10-fold cross-validation showed that all non-linear algorithms had a good performance, with an R2 value of greater than 0.8 and a root-mean square error (RMSE) of less than 0.05. In the bagging ensemble, the random forest (RF) (R2 = 0.993, RMSE = 0.020) model performed the best and in the boosting ensemble, the light gradient boosted machine (LGBM) (R2 = 0.992, RMSE = 0.022) performed the best. Although the evaluation index of the RF is slightly better than that of the LGBM, the independent validation results show that the two models have similar performances. The model evaluation results of the independent datasets showed that, in the SG model, the performance of the SG(LGBM) (R2 = 0.991, RMSE = 0.034) was better than that of the single or non-ensemble model. Comparing the FFC estimates of our model with those of existing datasets showed that our model exhibited more forest spatial distribution details and higher accuracy in complex landscapes. Overall, in this study, the method of using high-resolution remote sensing (RS) images to extract samples for FFC estimation is feasible. Our results demonstrate the potential of the ensemble MLAs to map the FFC. The research results also show that among many MALs, the RF algorithm is the most suitable algorithm for estimating FFC, which provides a reference for future research.

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Improvement of forest canopy density mapping of sparse forests using RS/GIS-based classification approach

Naseri,

Shataee Jouibary

2023

Arab J Geosci

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Estimation of Semiarid Forest Canopy Cover Using Optimal Field Sampling and Satellite Data with Machine Learning Algorithms

Cited by 7 publications

References 25 publications

Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

Improvement of forest canopy density mapping of sparse forests using RS/GIS-based classification approach

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