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Article

Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data

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Department of Soils and Natural Resources, Faculty of Agronomy, Universidad de Concepción, Vicente Méndez 595, Casilla 537, Chillán 3812120, Chile
2
Doctoral Program in Agronomic Sciences, Faculty of Agronomy, Universidad de Concepción, Vicente Méndez 595, Casilla 537, Chillán 3812120, Chile
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Department of Silviculture, Faculty of Forest Sciences, Universidad de Concepción, Victoria 631, Casilla 160-C, Concepción 4030000, Chile
4
Food and Agriculture Organization (FAO) of the United Nations, Tegucigalpa, Distrito Central 11101, Honduras
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Wildland Resources Department, Utah State University, Logan, UT 84322-5230, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2531; https://doi.org/10.3390/rs12162531
Received: 7 June 2020 / Revised: 28 July 2020 / Accepted: 4 August 2020 / Published: 6 August 2020
(This article belongs to the Special Issue Forest Degradation Monitoring)
Current estimates of CO2 emissions from forest degradation are generally based on insufficient information and are characterized by high uncertainty, while a global definition of ‘forest degradation’ is currently being discussed in the scientific arena. This study proposes an automated approach to monitor degradation using a Landsat time series. The methodology was developed using the Google Earth Engine (GEE) and applied in a pine forest area of the Dominican Republic. Land cover change mapping was conducted using the random forest (RF) algorithm and resulted in a cumulative overall accuracy of 92.8%. Forest degradation was mapped with a 70.7% user accuracy and a 91.3% producer accuracy. Estimates of the degraded area had a margin of error of 10.8%. A number of 344 Landsat collections, corresponding to the period from 1990 to 2018, were used in the analysis. Additionally, 51 sample plots from a forest inventory were used. The carbon stocks and emissions from forest degradation were estimated using the RF algorithm with an R2 of 0.78. GEE proved to be an appropriate tool to monitor the degradation of tropical forests, and the methodology developed herein is a robust, reliable, and replicable tool that could be used to estimate forest degradation and improve monitoring, reporting, and verification (MRV) systems under the reducing emissions from deforestation and forest degradation (REDD+) mechanism. View Full-Text
Keywords: forest degradation; REDD+; Google Earth Engine; random forest; dynamic land cover change; Landsat; carbon; MRV forest degradation; REDD+; Google Earth Engine; random forest; dynamic land cover change; Landsat; carbon; MRV
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MDPI and ACS Style

Duarte, E.; Barrera, J.A.; Dube, F.; Casco, F.; Hernández, A.J.; Zagal, E. Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data. Remote Sens. 2020, 12, 2531. https://doi.org/10.3390/rs12162531

AMA Style

Duarte E, Barrera JA, Dube F, Casco F, Hernández AJ, Zagal E. Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data. Remote Sensing. 2020; 12(16):2531. https://doi.org/10.3390/rs12162531

Chicago/Turabian Style

Duarte, Efraín, Juan A. Barrera, Francis Dube, Fabio Casco, Alexander J. Hernández, and Erick Zagal. 2020. "Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data" Remote Sensing 12, no. 16: 2531. https://doi.org/10.3390/rs12162531

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