An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica

Abstract

REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.

Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica

“…Furthermore, a visual examination of the regional Landa product revealed large forest omission errors (i.e., forest incorrectly classified as agriculture) in the zones dominated by banana and coffee cultivation, respectively. The cause of the locally prevalent forest misclassification errors remains unclear, but could be related to insufficient training data or the Bayesian classifier used in their study [8]. Because these zones were not extensive across a large portion of Costa Rica, they did not have a major effect on overall accuracy.…”

“…Tropical dry forests and humid forests cover extensive areas in Costa Rica, and can be distinguished by their average monthly precipitation as well as seasonal phenology. Average monthly precipitation across Costa Rica ranges from about 70 mm to 410 mm [45] and elevation ranges from sea level to~4000 m [8].…”

“…The two global products were: (1) The University of Maryland's Global Forest Change 2000-2015 tree canopy cover product (version 1.3) developed by Hansen et al [5] (hereafter GFC, or Hansen; and (2) a modification of the Massey et al [46,47] global cropland dataset (GCL). The regional product (hereafter, Landa) was developed specifically for Costa Rica by Fernandez-Landa et al [8] using a Landsat imagery time series between 1985 and 2014. The GFC product quantifies global tree cover (vegetation taller than 5 m) in the year 2000 and quantifies global forest change (tree cover gain and loss) between 2000 and 2015.…”

“…Like the derived GCL global product, the regional Landa product is a forest/non-forest binary classification, for the year 2014. The Landa product defined forest as a minimum land area of one hectare with tree canopy cover of more than 30%, and a minimum tree height of 5 m. The regional forest cover product was developed to track forest resources in the context of REDD+ [8] and has a minimum mapping unit of one hectare.…”

“…Humid tropical forests play a vital role in various global processes and are under threat [1][2][3][4][5]. Accurate global and regional maps are needed to support conservation policies across the tropics [5][6][7][8], and recent advances in remote sensing have enabled global maps of tropical forest ecosystems [5,9,10]. However, accurately measuring forest cover in the humid tropics is difficult [11].…”