EventsThe 3rd International Electronic Conference on Forests — Exploring New Discoveries and New Directions in Forests
Published
This submission belongs to the session S6. Forest Inventory, Quantitative Methods and Remote Sensing of the event The 3rd International Electronic Conference on Forests — Exploring New Discoveries and New Directions in Forests
Published date
21 Oct, 2022
Academic Editor
author-avatarGiorgos Mallinis
Citation
Carlos I Arbizu, Elgar Barboza, Wilian Salazar, David Gálvez, Lamberto Valqui, David Saravia, Jhony Gonzales, Héctor V. Vásquez, Changes in forest cover and land use in the dry forest of Tumbes (Peru) using Sentinel data in Google Earth Engine, in Proceedings of The 3rd International Electronic Conference on Forests — Exploring New Discoveries and New Directions in Forests, 15 October–31 October 2022, MDPI: Basel, Switzerland, doi: 10.3390/IECF2022-13095
Share
Email
Facebook
Twitter
LinkedIn

Changes in forest cover and land use in the dry forest of Tumbes (Peru) using Sentinel data in Google Earth Engine

image
Wilian Salazar 1
David Gálvez 2
image
image
1. Instituto Nacional de Innovación Agraria
2. Universidad Nacional de Frontera
3. Instituto Nacional de Innovación Agraria, Peru
Abstract

Dry forests are home to large amounts of biodiversity and are providers of ecosystem services and control the advance of deserts. However, globally these ecosystems are being threatened by various factors such as climate change, deforestation and changes in land use. The objective of the study was to identify the dynamics of changes in forest cover and land use, and the factors associated with the transformations of the dry forest using Google Earth Engine (GEE). The study area comprises the dry forest ecosystem in the department of Tumbes located in northern Peru. The annual collection of Sentinel 2 satellite images from 2017 and 2021 was analyzed. We identified the classes of urban (U), crop (C), bare soil (BS), body of water (BW), open dry forest (ODF) and dense dry forest (DDF). Subsequently, the supervised Random Forest (RF) classification was applied. The results showed that the areas of the ODF and DDF between 2017 and 2021 remained about 83% unchanged. Likewise, a greater surface change is shown in classes U and C of 45 and 23%, respectively. The application of GEE allowed us to evaluate the changes in forest cover and land use in the dry forest and from this, it provided important information for the sustainable management of this ecosystem.

Keywords
remote sensing
random forest
Land-use change
Time series
Peruvian Coast
Manuscript
Mid rotation Responses of Soil Preparation Intensity and Timing of Weed Control of Radiata Pine
TRENDS ON EUCALYPTUS WOOD DENSITY IN SITES WITH DIFFERENT WATER AVAILABILITY