Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods

Rahimizadeh, Naimeh; Kafaky, Sasan Babaie; Sahebi, Mahmod Reza; Mataji, A A

doi:10.1007/s10661-019-8015-x

Cited by 14 publications

(13 citation statements)

References 56 publications

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“…First, the received reflectance values from green vegetation surfaces (here mangrove) in optical images varied with wavelength (bands) [76]. With the SPOT-7 multispectral bands of Blue (0.455 µm-0.525 µm), Green (0.530 µm-0.590 µm), Red (0.625 µm-0.695 µm), and Near-Infrared (0.760 µm-0.890 µm), the mangrove could be discriminated from other land uses [77], even considering different ages and growth stages [78]. However, minor differences in reflectance between mangrove species could be found at the 0.760 µm-0.890 µm (NIR) [79].…”

Section: Discussionmentioning

confidence: 99%

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

et al. 2020

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This research investigated the performance of four different machine learning supervised image classifiers: artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM) using SPOT-7 and Sentinel-1 images to classify mangrove age and species in 2019 in a Red River estuary, typical of others found in northern Viet Nam. The four classifiers were chosen because they are considered to have high accuracy, however, their use in mangrove age and species classifications has thus far been limited. A time-series of Landsat images from 1975 to 2019 was used to map mangrove extent changes using the unsupervised classification method of iterative self-organizing data analysis technique (ISODATA) and a comparison with accuracy of K-means classification, which found that mangrove extent has increased, despite a fall in the 1980s, indicating the success of mangrove plantation and forest protection efforts by local people in the study area. To evaluate the supervised image classifiers, 183 in situ training plots were assessed, 70% of them were used to train the supervised algorithms, with 30% of them employed to validate the results. In order to improve mangrove species separations, Gram–Schmidt and principal component analysis image fusion techniques were applied to generate better quality images. All supervised and unsupervised (2019) results of mangrove age, species, and extent were mapped and accuracy was evaluated. Confusion matrices were calculated showing that the classified layers agreed with the ground-truth data where most producer and user accuracies were greater than 80%. The overall accuracy and Kappa coefficients (around 0.9) indicated that the image classifications were very good. The test showed that SVM was the most accurate, followed by DT, ANN, and RF in this case study. The changes in mangrove extent identified in this study and the methods tested for using remotely sensed data will be valuable to monitoring and evaluation assessments of mangrove plantation projects.

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Section: Discussionmentioning

confidence: 99%

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

et al. 2020

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“…Of course, it is necessary to pay attention to the fact that in areas similar to the studied forests, more images with low resolution and relatively high have been used to prepare the canopy cover of trees, the results of which also show there has been insu cient accuracy of these images (Boyaci et al, 2017). However, in a study conducted in forests with high vegetation cover, Banerjee et al (2014) and Rahimizadeh et al (2020) presented the canopy cover classi cation of trees with high accuracy using a direct-method using the Landsat-TM and Spot-7 satellite images, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…This classi cation uses membership functions to assign each object to the appropriate class (Quynh Trang et al 2016). In this regard, Rahimizadeh et al (2020) used the object-based method to study the canopy cover of trees using the Spot-7 satellite image in the high dense forests of Hyrcanian forests (northern Iran) and could improve the results compare to pixel-based classi cation methods. Also, Wang et al (2004) used the object-based classi cation method on the high-resolution Ikonos imagery to study the mangrove forest cover in the Caribbean coast of Panama on the high dense canopy-covered area.…”

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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“…Alternative lower cost methods for estimating forest characteristics include traditional field based sample plots and radar or optical remote sensing instruments (Hyyppä et al, 2000;Ginzler and Hobi, 2015;Rahimizadeh et al, 2020;Waser et al, 2015). The most accessible of these alternative methods is satellite based optical imagery, which can be used to create a forest land cover classification, to determine the extent of the forested area (Bühler et al, 2013), and can be combined with field plot observations of specific forest characteristics to create a predictive model based on the spectral and textural characteristics of the imagery (Dash et al, 2016;Rahimizadeh et al, 2020).…”

Section: Avalanches In Forested Terrainmentioning

confidence: 99%

Automated snow avalanche release area delineation in data sparse, remote, and forested regions

Sykes

Haegeli

Bühler³

2021

Preprint

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Abstract. Potential avalanche release area (PRA) modelling is critical for generating automated avalanche terrain maps which provide low-cost large scale spatial representations of snow avalanche hazard for both infrastructure planning and recreational applications. Current methods are not applicable in mountainous terrain where high-resolution elevation models are unavailable and do not include an efficient method to account for avalanche release in forested terrain. This research focuses on expanding an existing PRA model to better incorporate forested terrain using satellite imagery and presents a novel approach for validating the model using local expertise, thereby broadening its application to numerous mountain ranges worldwide. The study area of this research is a remote portion of the Columbia Mountains in southeastern British Columbia, Canada which has no pre-existing high-resolution spatial data sets. Our research documents an open source workflow to generate high-resolution DEM and forest land cover data sets using optical satellite data processing. We validate the PRA model by collecting a polygon dataset of observed potential release areas from local guides, using a method which accounts for the uncertainty of human recollection and variability of avalanche release. The validation dataset allows us to perform a quantitative analysis of the PRA model accuracy and optimize the PRA model input parameters to the snowpack and terrain characteristics of our study area. Compared to the original PRA model our implementation of forested terrain and local optimization improved the percentage of validation polygons accurately modelled by 11.7 percentage points and reduced the number of validation polygons that were underestimated by 14.8 percentage points. Our methods demonstrate substantial improvement in the performance of the PRA model in forested terrain and provide means to generate the requisite input datasets and validation data to apply and evaluate the PRA model in vastly more mountainous regions worldwide than was previously possible.

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Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods

Cited by 14 publications

References 56 publications

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

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

Automated snow avalanche release area delineation in data sparse, remote, and forested regions

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