Analyzing seasonal variation in the vegetation cover using NDVI and rainfall in the dry deciduous forest region of Eastern India

Dutta, Shyamal; Rehman, Sufia; Chatterjee, Soumen; Sajjad, Haroon

doi:10.1016/b978-0-12-822931-6.00003-4

Cited by 12 publications

(6 citation statements)

References 75 publications

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“…The datasets used in this paper consist of Normalized Difference Vegetation Index (NDVI) time series data of 10-m spatial resolution collected from a 15-day Sentinel-2 composite, which covers a region of 0.5 degrees by 0.5 degrees for each of the four studied countries. NDVI is a remote sensing index used to assess vegetation health and quantity [ 40 ]. NDVI also helps differentiate vegetated land from other land-cover types in imagery as it tends to have a positive value for pixels related to vegetated land and zero or a negative value for pixels pertaining to other types of land-cover, such as water [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

A Light-Weight Cropland Mapping Model Using Satellite Imagery

Hussain

Abuhani

Khan

et al. 2023

Sensors

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Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine-learning-based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions, namely Iran, Mozambique, Sri-Lanka, and Sudan, where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction, and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 and 0.98 across the four test regions at hand.

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Section: Methodsmentioning

confidence: 99%

A Light-Weight Cropland Mapping Model Using Satellite Imagery

Hussain

Abuhani

Khan

et al. 2023

Sensors

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“…NDVI values were examined in several literatures to distinguish the healthy green, unhealthy green and nongreen space (Aburas et al, 2015;Hashim et al, 2019;Julien et al, 2006;Li et al, 2015). As the NDVI values may be generated from the reflectance of healthy vegetation (Saravanan et al, 2018), it is recommended for this research due to its most widespread usage for healthy vegetation detection (Choubin et al, 2019;Dutta et al, 2021;Jiang et al, 2006). Using the surface reflectance of the visible red band and near infrared band, which are reflected by healthy plants and have wavelengths of around 0.6 m (red) and 0.9 m (near-infrared), NDVI values were determined from satellite photos (Choubin et al, 2019;Yang et al, 2021).…”

Section: Ndvi Analysismentioning

confidence: 99%

Studying the spatio-temporal changes of urban green space: A case study of Pokhara Metropolitan City

Subedi

Pokhrel²

2023

J. Eng. Iss. Solut.

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Green space refers to land areas covered primarily with vegetation, including parks, gardens, nature reserves, and other similar areas. These spaces offer various benefits such as supporting biodiversity, enhancing air and water quality, promoting mental and physical well-being, providing opportunities for recreation and social interaction, and improving the overall beauty of the environment. To assess the dynamics of green space in Pokhara Metropolitan City (PMC), maximum likelihood classification and normalized difference vegetation index (NDVI) methods were utilized to analyze satellite imagery. The study showed two distinct patterns of urban green space, with the green space in the central city gradually decreasing, while the nearby suburban and remote areas of PMC experienced an increase as agricultural land was transformed into grassland due to population migration. Consequently, the area of moderately healthy vegetation increased, while healthy vegetation decreased from 4926 hectares (10.61% of total area) to 2535 hectares (5.46% of total area). Additionally, the area of no vegetation such as urban areas increased from 1.8% to 2.17%. An accuracy assessment was used to validate the analyzed data, resulting in an overall accuracy of 81.91% and a Kappa coefficient of 0.63. The primary factors contributing to the loss of green spaces were urban growth and population growth within the inner metropolis. Therefore, it is crucial to maintain PMC's green areas and give more attention to this issue while developing municipal plans and policies in the future.

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“…Winter dominates from late October to late February, with a minimum temperature of 6-8 °C. Summer begins during mid-May and has a maximum temperature of 35-38 °C (Dutta et al 2021). These climatic conditions provide the most desirable seasons for tea plantations: spring and autumn, with moderate temperatures and little rain.…”

Section: Study Areamentioning

confidence: 99%

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

2023

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Background Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration. Objectives The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2. Methods A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations. Results The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45–89%. During 32 years (1990–2022), about 1280.47 km 2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96). Conclusions This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.

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Analyzing seasonal variation in the vegetation cover using NDVI and rainfall in the dry deciduous forest region of Eastern India

Cited by 12 publications

References 75 publications

A Light-Weight Cropland Mapping Model Using Satellite Imagery

A Light-Weight Cropland Mapping Model Using Satellite Imagery

Studying the spatio-temporal changes of urban green space: A case study of Pokhara Metropolitan City

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

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