Published

2024-02-28

Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data

Exploración del potencial de la plataforma de Google Earth Engine (GEE) para el análisis de patrones de perturbación forestal a través de macrodatos

DOI:

https://doi.org/10.15446/esrj.v27n4.110128

Keywords:

Drought, wildfire, forest change detection, big data analysis, Google Earth Engine (en)
sequía , incendio forestal, cambio en la detección forestal, análisis de datos, Google Earth Engine (es)

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Authors

  • Tunahan Çinar Duzce University
  • Abdurrahim Aydin Duzce University image/svg+xml

Climate change has led to various adverse consequences, with natural disasters being one of the most striking outcomes. Natural disasters negatively impact life, causing significant disruptions to the ecosystem. Prompt identification of affected areas and initiation of the rehabilitation process are imperative to address the disturbances in the ecosystem. Satellite imagery is employed for the rapid and cost-effective detection of damages caused by natural disasters. In this conducted study, the outputs of climate change wildfire, forest change detection, and drought analysis, have been examined, all of which worsens the impacts on the ecosystem. The analysis of drought involved using MODIS data, while Sentinel-2A satellite images were utilized to identify wildfire areas and changes in forested regions caused by windthrow. The research focused on Ganja, Azerbaijan, as the area for drought analysis. The driest June between 2005 and 2018 was assessed using the Vegetation Condition Index (VCI) in conjunction with data from the National Centers for Environmental Information (NOAA). At the Düzce Tatlıdere Forest Management Directorate, the Normalized Difference Red Edge Index (NDRE) was utilized between the years 2018 and 2019 to detect the changes occurring in forested areas due to windthrow. The NDRE synthetic band was added to satellite images for the years 2018 and 2019, and a Random Forest (RF) algorithm was employed to classify the data. The classification results were evaluated using Total Accuracy and Kappa statistics. The study includes the detection of the Normalized Burn Ratio (NBR) applied to determine the extent of the wildfire that occurred in the Solquca village of the Qabala region in Azerbaijan in 2021. According to the analysis of the VCI and NOAA, June 2014 was identified as the driest month in Ganja. In the Tatlıdere region, the analysis indicated that 4.22 hectares experienced reforestation, while 24 hectares experienced deforestation. The NBR analysis has revealed that ~1007 hectares of land were burned in the Solquca village of Qabala. The analyses conducted provide information regarding the use of satellite imagery in relation to changes in forest areas due to drought, wildfire, and windthrow.

El cambio climático ha generado varias consecuencias adversas, con los desastres naturales como uno de los efectos más notables. Los desastres naturales impactan negativamente la vida  y causan grandes daños en los ecosistemas. La identificación temprana de las áreas afectadas y el comienzo de los procesos de rehabilitación son necesarios para abordar los desajustes en los ecosistemas. Las imágenes satelitales se emplean para una detección rápida y eficaz de los daños causados por los desastres naturales. En este estudio se examinan los resultados de los incendios forestales por el cambio climático, la detección de los cambios en los bosques y los análisis de sequías, los cuales empeoran aún más los ecosistemas. El análisis de sequías se elaboró con información satelital MODIS, mientras que las imágenes satelitales de Sentinel-2ª se utilizaron para identificar las áreas de incendios forestales y los cambios en las regiones boscosas causados por el viento. El área para el análisis de las sequías se ubica en Ganja, Azerbaiyán. El mes de junio más seco entre 2005 y 2018 se evaluó con el Índice de Condición Vegetal y con información del Centro Nacional para la Información Ambiental. En el Directorado de Administración Forestal Düzce Tatlıdere se ejecutó la  Diferencia Normalizada de Borde Rojo (NDRE, del inglés Normalized Difference Red Edge Index) entre los años 2018 y 2019 para detectar los cambios ocurridos en las áreas boscosas debido a los daños en árboles ocasionados por fuertes vientos. Luego se añadió una banda sintética NDRE a las imágenes satelitales para los años 2018 y 2019 y se empleó un algoritmo de bosques aleatorios para clasificar la información. Los resultados de clasificación se evaluaron con las estadísticas Precisión Total y Kappa. El estudio incluye la aplicación del Índice Normalizado de Área Quemada para determinar la extensión del incendio forestal que ocurrió en la villa de Solquca, en la región de Qabala, en Azerbaiyán, durante el 2021. De acuerdo con los análisis de Índice de Condición Vegetal y del Centro Nacional para la Información Ambiental, junio de 2014 fue identificado como el mes más seco en Ganja. En la región de Tatlidere los análisis indican que 4.22 hectáreas experimentaron un proceso de reforestación, mientras que 24 hectáreas experimentaron deforestación. El Índice Normalizado de Área Quemada reveló que unas 1007 hectáreas de tierra se quemaron en el incendio de la villa de Solquca. Estos análisis realizados proveen de información relacionada al uso de imágenes satelitales en relación con los cambios en las áreas forestales debido a la sequía, los incendios forestales y los daños en bosques ocasionados por fuertes vientos.

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How to Cite

APA

Çinar, T. and Aydin, A. (2024). Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data. Earth Sciences Research Journal, 27(4), 437–448. https://doi.org/10.15446/esrj.v27n4.110128

ACM

[1]
Çinar, T. and Aydin, A. 2024. Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data. Earth Sciences Research Journal. 27, 4 (Feb. 2024), 437–448. DOI:https://doi.org/10.15446/esrj.v27n4.110128.

ACS

(1)
Çinar, T.; Aydin, A. Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data. Earth sci. res. j. 2024, 27, 437-448.

ABNT

ÇINAR, T.; AYDIN, A. Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data. Earth Sciences Research Journal, [S. l.], v. 27, n. 4, p. 437–448, 2024. DOI: 10.15446/esrj.v27n4.110128. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/110128. Acesso em: 2 mar. 2025.

Chicago

Çinar, Tunahan, and Abdurrahim Aydin. 2024. “Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data”. Earth Sciences Research Journal 27 (4):437-48. https://doi.org/10.15446/esrj.v27n4.110128.

Harvard

Çinar, T. and Aydin, A. (2024) “Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data”, Earth Sciences Research Journal, 27(4), pp. 437–448. doi: 10.15446/esrj.v27n4.110128.

IEEE

[1]
T. Çinar and A. Aydin, “Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data”, Earth sci. res. j., vol. 27, no. 4, pp. 437–448, Feb. 2024.

MLA

Çinar, T., and A. Aydin. “Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data”. Earth Sciences Research Journal, vol. 27, no. 4, Feb. 2024, pp. 437-48, doi:10.15446/esrj.v27n4.110128.

Turabian

Çinar, Tunahan, and Abdurrahim Aydin. “Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data”. Earth Sciences Research Journal 27, no. 4 (February 28, 2024): 437–448. Accessed March 2, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/110128.

Vancouver

1.
Çinar T, Aydin A. Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data. Earth sci. res. j. [Internet]. 2024 Feb. 28 [cited 2025 Mar. 2];27(4):437-48. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/110128

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1. Tunahan Çinar, Ayşegül Uslu, Abdurrahim Aydin. (2025). Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes. Earth Science Informatics, 18(2) https://doi.org/10.1007/s12145-025-01701-7.

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