Analyzing Land Cover Changes over Landsat-7 Data using Google Earth Engine

Srivastava, Anubhava; Biswas, Susham

doi:10.1109/icais56108.2023.10073795

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…A number of methods have been employed to determine the area occupied by vegetation [8]. These methods involve classifying the outcome based on supervised and unsupervised learning, automatic image processing, the generation of index pictures, as well as visual interpretation [9]. The dynamics of plant cover can be analyzed using spectral and temporal indices obtained from time-lapse images.…”

Section: Introductionmentioning

confidence: 99%

Exploring Forest Transformation by Analyzing Spatial-temporal Attributes of Vegetation using Vegetation Indices

Srivastava¹,

Umrao²,

Biswas³

2023

IJACSA

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The world's ecosystem and environment are rapidly deteriorating with an increase in the depletion of forest conditions due to forest fires. In recent past years, wildfire incidents in Sikkim have increased due to severe climatic changes such as turbulent rainfall, untimely summers, extreme droughts in winters, and a reduction in the percentage of yearly rainfall. Forest fires are one of the numerous kinds of disasters that impose disastrous changes on the entire environment and disrupt the complex correspondence of the flora and fauna. The research's goal is to examine the vegetation indices based on different climates to know why forest vegetation is decreasing day by day from 2000 to 2023. The frequent changes in forest vegetation are extensively studied by using satellite images. This data has been collected by three satellites Landsat-5, Landsat-8, and Landsat-9 on different vegetation indices NDVI, EVI, and NDWI. East Sikkim area is chosen to compute forest vegetation indices based on the heap's landmass this region is unexplored yet and also studied about the forest changes by using different spatial temporal indices in the range of the entire district in the future. The authors of this paper have used Landsat multi-spectral data to assess changes in the area of vegetation in a sub-tropical region like a dense forest region in east Sikkim. The analysis depicts space images, computes vegetation indices (NDVI, EVI, NDWI), and accomplishes mathematical computation of findings. The proposed method will be helpful to discuss the variance of vegetation in the entire East Sikkim region at the time span of 2000-2023. In the analysis, we find that mean and standard deviation values change over the years in all indices. Later, we also calculated changes by using a classification model and find a total 10% change in forest areas in approximately 22 years.

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Section: Introductionmentioning

confidence: 99%

Exploring Forest Transformation by Analyzing Spatial-temporal Attributes of Vegetation using Vegetation Indices

Srivastava¹,

Umrao²,

Biswas³

2023

IJACSA

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AI-Driven Environmental Monitoring Using Google Earth Engine

Srivastava,

Sharma

2024

Smart Sensors, Measurement and Instrumentation

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Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine

Srivastava

2024

Geodesy and Cartography

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By rapid growth in programming tools, accessibility to end consumer computing power, and the availability of free satellite data, the data science and remote sensing fields have begun to converge in recent years. Before this major processing time is wasted in collection of data. Google Earth Engine easily overcomes above problem; it contains data from different satellites and has power of processing and computation also. Well known data provider satellites are present in the library of GEE and users can easily process and track real time data from these satellites over GEE. “Sentinel”, a mission of the European Space Agency and “Landsat”, an American Earth observation satellite have been used in a variety of remote sensing applications. GEE makes these data sets available to the general public. These datasets are utilised for computing and analysis purposes. The objective of this study is to find change in study area by using above discussed two satellite data, over each season of year on different category of classification (Random Forest, CART, GTB and SVM). This work focuses on improving the classification accuracy of different classification algorithm by reviewing training samples and analyzing post-classification with image differencing in the algebraic technique. Because Landsat data have a medium spatial resolution, therefore point-wise computation was used. Lastly, we also detect which data sets are working better on an appropriate machine learning algorithm, so after final calculation we estimate accuracy of each algorithm by using confusion matrix and kappa.

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Analyzing Land Cover Changes over Landsat-7 Data using Google Earth Engine

Cited by 3 publications

References 16 publications

Exploring Forest Transformation by Analyzing Spatial-temporal Attributes of Vegetation using Vegetation Indices

Exploring Forest Transformation by Analyzing Spatial-temporal Attributes of Vegetation using Vegetation Indices

AI-Driven Environmental Monitoring Using Google Earth Engine

Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine

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