Leveraging Python for data modeling, visualization and remote sensing of fire hotspots in the Amazon: a case study of Santana do Araguaia, Brazil

Conservation of the Amazon rainforest is essential because it plays an important role in global biodiversity and climate stability. In this work, python was used in data modeling, visualization and remote sensing for analysis of fire hotspots generated from the region where Santana do Araguaia is lo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International archives of the photogrammetry, remote sensing and spatial information sciences. Jg. XLVIII-3/W3-2024; S. 27 - 33
1. Verfasser: da Silveira, Jackson Vinícius Mendes
Format: Journal Article Tagungsbericht
Sprache:Englisch
Veröffentlicht: Gottingen Copernicus GmbH 02.11.2024
Copernicus Publications
Schlagworte:
Biodiversity
Change detection
Climate models
Conservation
Data analysis
Fires
Forest management
Graphics
Graphs
Hot spots
Information management
Land conservation
Land cover
Modelling
Normalized difference vegetative index
Python
Rainforests
Regression models
Remote sensing
Risk analysis
Vegetation
Visualization
Brazil
Amazon Basin
ISSN:2194-9034, 1682-1750, 2194-9034
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Conservation of the Amazon rainforest is essential because it plays an important role in global biodiversity and climate stability. In this work, python was used in data modeling, visualization and remote sensing for analysis of fire hotspots generated from the region where Santana do Araguaia is located. With the use of shapefiles and csv data and different Python libraries, like pandas, geopandas for geographical functions and libraries with graphics packages such as matplotlib, seaborn and rasterio. Methodology encompasses a series of analytical techniques to explore the relationships between environmental variables and fire risk. These techniques include generating linear regression models to study specific correlations, creating choropleth maps to visualize spatial patterns, mapping fire hotspots to identify high-risk areas and using 2d regression graphs for detailed analysis. In addition, time series analyses are conducted using static visualizations to track changes in fire risk over time, offering a comprehensive approach to understanding fire dynamics. Remote sensing techniques are used to produce normalized difference vegetation index maps and detect changes in vegetation and land cover. This research demonstrates how Python tools can reinforce fire risk analysis and support forest management blueprint, providing relevant information for conservation efforts.
Bibliographie:ObjectType-Case Study-2
ObjectType-Feature-4
SourceType-Conference Papers & Proceedings-1
content type line 22
ObjectType-Report-1
ObjectType-Article-3
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLVIII-3-W3-2024-27-2024