Estimate tropical forest stand volume using SPOT 5 Satellite Image

This paper describes the combination of multi-data in stratifying the natural evergreen broadleaved tropical forest of the Central Highlands of Vietnam. The forests were stratified using both unsupervised and supervised classification methods based on SPOT5 and field data. The forests were classified into 3 and 4 strata separably. Correlation between stratified forest classes and forest variables was analyzed in order to find out 1) how many classes is suitable to stratify for the forest in this area and 2) how closely the forest variables are related with forest classes. The correlation coefficient shows although all forest variables did have a significant correlation with the forest classes, stand volume appeared to have the strongest correlation with forest classes. These are 0.64 and 0.59 for four and three strata respectively. The results of supervised classification also show the four strata of heavily degraded forest, moderate disturbance, insignificant disturbance, and dense forest were discriminated more clearly comparing to the forest stratified into three classes. The proof is that overall accuracy of supervised classification was 86% with Kappa of 0.8 for four classes, meanwhile, these are 77% and 0.62 respectively for forest area classified into 3 classes.

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MAPPING TROPICAL FOREST FOR SUSTAINABLE MANAGEMENT USING SPOT 5 SATELLITE IMAGE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-319-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 319-323

Author(s):

Huong Thi Thanh Nguyen

Keyword(s):

Tropical Forest ◽

Sustainable Management ◽

Supervised Classification ◽

Satellite Image ◽

Classification Methods ◽

Stand Volume ◽

Dense Forest ◽

Moderate Disturbance ◽

Supervised Classification Methods ◽

Spot 5

This paper describes the combination of multi-data in stratifying the natural evergreen broadleaved tropical forest of the Central Highlands of Vietnam. The forests were stratified using both unsupervised and supervised classification methods based on SPOT5 and field data. The forests were classified into 3 and 4 strata separably. Correlation between stratified forest classes and forest variables was analyzed in order to find out 1) how many classes is suitable to stratify for the forest in this area and 2) how closely the forest variables are related with forest classes. The correlation coefficient shows although all forest variables did have a significant correlation with the forest classes, stand volume appeared to have the strongest correlation with forest classes. These are 0.64 and 0.59 for four and three strata respectively. The results of supervised classification also show the four strata of heavily degraded forest, moderate disturbance, insignificant disturbance, and dense forest were discriminated more clearly comparing to the forest stratified into three classes. The proof is that overall accuracy of supervised classification was 86% with Kappa of 0.8 for four classes, meanwhile, these are 77% and 0.62 respectively for forest area classified into 3 classes.

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Quantify Forest Stand Volume Using SPOT 5 Satellite Image

10.1007/978-3-030-81443-4_42 ◽

2021 ◽

pp. 659-670

Author(s):

Nguyen Thi Thanh Huong ◽

Luong The Phuong

Keyword(s):

Satellite Image ◽

Forest Stand ◽

Stand Volume ◽

Spot 5

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Estimation of Forest Stand Parameters Using SPOT-5 Satellite Images and Topographic Information

10.20944/preprints201710.0017.v1 ◽

2017 ◽

Author(s):

Shiqin Xie ◽

Wei Wang ◽

Qian liu ◽

Jinghui Meng ◽

Tianzhong Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Satellite Image ◽

Forest Stand ◽

Textural Features ◽

Spectral Features ◽

Topographic Information ◽

Study Results ◽

Stand Parameters ◽

Spot 5

In recent years, remote sensing technology has been widely used to predict forest stand parameters. In order to compare the effects of different features of remote sensing images and topographic information on the prediction of forest stand parameters, multivariate stepwise regression analysis method was used to build estimation models for important forest stand parameters by using textural and spectral features as well as topographic information of SPOT-5 satellite images in northeastern Heilongjiang Province in China as independent variables. The study results show that the optimal window to predict forest stand parameters using textural features of SPOT-5 satellite image is 9×9; the ability of textural features was better than that of spectral features in terms of predicting forest stand parameters; with the inclusion of topographic information, the accuracy of prediction of all models was improved, of which elevation has the most significant effect. The highest accuracy was achieved when predicting the stand volume (SV) (R2adj=0.820), followed by basal area (BA) (R2adj =0.778), accuracy of both above models exceeded 75%. The results show that models combined use of textural, spectral features and topographic information of SPOT-5 images have a good application prospect in predicting forest stand parameters.

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