Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution

Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy of forest-type classification. Several classification methods are used to map urban and peri-urban forests and to identify healthy and non-healthy ones. Some of these methods have shown success in the classification of forests where others failed. The successful methods used specific remote sensing data technology, such as hyper-spectral and very high spatial resolution (VHR) images. However, both VHR and hyper-spectral sensors are very expensive, and hyper-spectral sensors are not widely available on satellite platforms, unlike multi-spectral sensors. Moreover, aerial images are limited in use, very expensive, and hard to arrange and manage. To solve the aforementioned problems, an advanced method, self-organizing–deep learning (SO-UNet), was created to classify forests in the urban and peri-urban environment using multi-spectral, multi-temporal, and medium spatial resolution Sentinel-2 images. SO-UNet is a combination of two different machine learning technologies: artificial neural network unsupervised self-organizing maps and deep learning UNet. Many experiments have been conducted, and the results showed that SO-UNet overwhelms UNet significantly. The experiments encompassed different settings for the parameters that control the algorithms.

Download Full-text

Genetic algorithm-based method for forest type classification using multi-temporal NDVI from Landsat TM imagery

Annals of GIS ◽

10.1080/19475683.2018.1552621 ◽

2018 ◽

Vol 25 (1) ◽

pp. 33-43 ◽

Cited By ~ 6

Author(s):

Hong Tao ◽

Manqi Li ◽

Ming Wang ◽

Guonian Lü

Keyword(s):

Genetic Algorithm ◽

Forest Type ◽

Landsat Tm ◽

Multi Temporal ◽

Landsat Tm Imagery ◽

Type Classification

Download Full-text

Utility of Very High Resolution Imagery for Forest Type Classification and Stand Structure Estimation

Journal of Forest Planning ◽

10.20659/jfp.18.1_53 ◽

2012 ◽

Vol 18 (1) ◽

pp. 53-62

Author(s):

Tetsuji Ota ◽

Nobuya Mizoue ◽

Shigejiro Yoshida

Keyword(s):

High Resolution ◽

Stand Structure ◽

Forest Type ◽

Structure Estimation ◽

High Resolution Imagery ◽

Very High Resolution Imagery ◽

Type Classification ◽

Very High

Download Full-text

Forest Type Classification in Poyang Lake Basin Based on Multi-source Data Fusion

Terrestrial Environmental Sciences - Chinese Water Systems ◽

10.1007/978-3-319-97725-6_15 ◽

2018 ◽

pp. 221-248

Author(s):

Lu Ming

Keyword(s):

Data Fusion ◽

Poyang Lake ◽

Forest Type ◽

Lake Basin ◽

Poyang Lake Basin ◽

Source Data ◽

Type Classification

Download Full-text

A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness

Forests ◽

10.3390/f11060605 ◽

2020 ◽

Vol 11 (6) ◽

pp. 605 ◽

Cited By ~ 1

Author(s):

Jianyu Gu ◽

Heather Grybas ◽

Russell G. Congalton

Keyword(s):

Spectral Data ◽

Spatial Resolution ◽

Forest Type ◽

Canopy Height ◽

Aerial Imagery ◽

Tree Level ◽

Tree Crown ◽

Remotely Sensed Data ◽

Individual Tree ◽

Tree Crowns

Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation.

Download Full-text

Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

International Journal of Forestry Research ◽

10.1155/2020/8896310 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Hung Nguyen Trong ◽

The Dung Nguyen ◽

Martin Kappas

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Forest Type ◽

Vegetation Indices ◽

Landsat 8 ◽

Tropical Lowland ◽

Central Vietnam ◽

Lowland Forests ◽

Type Classification ◽

Sentinel 2

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

Download Full-text