Beyond NDVI: A Proposed Vegetation Cover Index (VCI) for Crop and Vegetation Segregation

The proposed work introduces a vegetation cover index (VCI) for segregating the vegetation and crop land cover using a base index NBR, for the proposed index water class has been excluded beforehand in the preprocessing steps. This new index assigns negative values to urban and barren lands, which further helps extract the target classes easily. Moreover, the vegetation and crop classes can also be filtered out and identified on their own with minimum confusion. This index works well in the correlation with Essential climate variables LAI, FCOVER and FAPAR and outperforms the NDVI in this regard, further in terms of separability of the target classes, the proposed index attains the M statistics value of ${1. 6 2 5 9}$ as compared to ${1. 1 4 7 8}$ of NDVI index, which confirms that this index is more suitable for the classification of the vegetation and crop than that of NDVI which has long been the benchmark for vegetation monitoring

Authors

Institutions

Publication Details

Published
2024-12-02
DOI
https://doi.org/10.1109/ingarss61818.2024.10984351
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond NDVI: A Proposed Vegetation Cover Index (VCI) for Crop and Vegetation Segregation

Triloki Pant, Harsh Srivastava
1 links
Remote Sensing and LiDAR Applications
article

Beyond NDVI: A Proposed Vegetation Cover Index (VCI) for Crop and Vegetation Segregation

Triloki Pant, Harsh Srivastava
article en

Abstract

The proposed work introduces a vegetation cover index (VCI) for segregating the vegetation and crop land cover using a base index NBR, for the proposed index water class has been excluded beforehand in the preprocessing steps. This new index assigns negative values to urban and barren lands, which further helps extract the target classes easily. Moreover, the vegetation and crop classes can also be filtered out and identified on their own with minimum confusion. This index works well in the correlation with Essential climate variables LAI, FCOVER and FAPAR and outperforms the NDVI in this regard, further in terms of separability of the target classes, the proposed index attains the M statistics value of ${1. 6 2 5 9}$ as compared to ${1. 1 4 7 8}$ of NDVI index, which confirms that this index is more suitable for the classification of the vegetation and crop than that of NDVI which has long been the benchmark for vegetation monitoring

Indian Institute of Information Technology Allahabad (IN)
Ministry of Education
Openalex Percentile: Top 75%
Remote Sensing and LiDAR Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.