Vegetation structural composition mapping of a complex landscape using forest cover density transformation and random decision forest classifier: a comparison

Article

Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area

Projo Danoedoro et al.

Indonesian Journal of Geography (2023)

添加到收藏夹

Article

Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery

Projo Danoedoro et al.

International Journal on Advanced Science, Engineering and Information Technology (2022)

添加到收藏夹

Article Environmental Sciences

Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature-A Case Study for Lousa Region, Portugal

Pegah Mohammadpour et al.

Summary: This study used Sentinel-2 data to map vegetation in the complex and mixed vegetation cover of the Lousa district in Portugal. By incorporating GLCM texture features and vegetation indices, the classification accuracy can be increased.

REMOTE SENSING (2022)

添加到收藏夹

Proceedings Paper Environmental Sciences

The Effect on Atmospheric and Topographic Corrections on the Vegetation Density Mapping using Several Vegetation Indices: A Case Study in Arjuno-Welirang Volcanoes, East Java

Huwaida Nur Salsabila et al.

Summary: This study aimed to evaluate the effects of atmospheric and topographic corrections on vegetation density estimates using Landsat 8 OLI imagery in the Arjuno-Welirang volcanic complex in East Java. The results showed that the topographic correction improved the correlation coefficients between spectral information and measured vegetation density in the field, particularly for SAVI, EVI, and MSARVI transformations, while NDVI and ARVI showed slight decreases.

SEVENTH GEOINFORMATION SCIENCE SYMPOSIUM 2021 (2021)

添加到收藏夹

Proceedings Paper Environmental Sciences

Comparing Several Pixel-based Classification Methods for Vegetation Structural Composition Mapping using Sentinel 2A Imagery in Salatiga Area, Central Java

Haeydar Anggara Hadi et al.

Summary: This study compared three pixel-based classification algorithms for mapping the structural composition of vegetation and evaluated the impact of additional data on the classification process. The results showed that the Maximum Likelihood algorithm achieved the highest accuracy when combining original bands and RVI, demonstrating its effectiveness in mapping vegetation structure composition.

SEVENTH GEOINFORMATION SCIENCE SYMPOSIUM 2021 (2021)

添加到收藏夹

Article Computer Science, Information Systems