Comparative Analysis of Different Vegetation Indices of Noida City Using Landsat Data

2021 ◽  
pp. 209-221
Author(s):  
Richa Sharma ◽  
Lolita Pradhan ◽  
Maya Kumari ◽  
Prodyut Bhattacharya
Keyword(s):  
Comparative Analysis ◽  
Vegetation Indices ◽  
Landsat Data

scholarly journals ESTIMATING ABOVEGROUND BIOMASS OF BAMBOO AND MIXED BAMBOO FOREST IN THUA THIEN-HUE PROVINCE, VIET NAM USING PALSAR-2 AND LANDSAT OLI DATA

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 11-19
Author(s):  
T. T. Cat Tuong ◽  
H. Tani ◽  
X. F. Wang ◽  
V.-M. Pham
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Landsat Data ◽  
Bamboo Forest ◽  
Landsat 8 Oli ◽  
L Band

Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R2) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R2 of 0.60) over the performance of models using only SAR (R2 of 0.49) and only Landsat data (R2 of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R2 of 0.59 and an RMSE of 29.66 tons ha−1. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha−1). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.

scholarly journals Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks

2019 ◽  
Vol 8 (2) ◽  
pp. 71 ◽  
Author(s):  
Premysl Stych ◽  
Josef Lastovicka ◽  
Radovan Hladky ◽  
Daniel Paluba
Keyword(s):  
Time Series ◽  
National Park ◽  
Vegetation Indices ◽  
Forest Vegetation ◽  
Vegetation Changes ◽  
Landsat Data ◽  
Climate Data ◽  
Data Record ◽  
Climate Data Record ◽  
Level 2

This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged by wind and bark beetle calamities, which strongly influenced the health state of the forest vegetation at the end of the 20th and beginning of the 21st century. The analysis of the time series was based on the ten selected vegetation indices in different types of localities selected according to the type of forest disturbances. The Landsat data CDR (Climate Data Record/Level 2) was normalized using the PIF (Pseudo-Invariant Features) method and the results of the Time Series were validated by in-situ data. The results confirmed the high relevance of the vegetation indices based on the SWIR bands (e.g., NDMI) for the purpose of evaluating the individual stages of the disturbance (especially the bark beetle calamity). Usage of the normalized Landsat data Climate Data Record (CDR/Level 2) in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability of the corrected data.

Sign in / Sign up

Export Citation Format

Share Document