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.

Full text

remote sensing
Article
An Operational Framework for Land Cover
Classification in the Context of REDD+ Mechanisms.
A Case Study from Costa Rica
Alfredo Fernández-Landa 1, *, Nur Algeet-Abarquero 1, Jesús Fernández-Moya 2,
María Luz Guillén-Climent 1, Lucio Pedroni 3, Felipe García 4, Andrés Espejo 5,
Juan Felipe Villegas 3, Miguel Marchamalo 6, Javier Bonatti 7, Iñigo Escamochero 1,
Pablo Rodríguez-Noriega 1, Stavros Papageorgiou 8and Erick Fernandes 8
1Agresta S. Coop., Duque Fernán Nuñez 2, Madrid 28012, Spain; nalgeet@agresta.org (N.A.-A.);
mguillen@agresta.org (M.L.G.-C.); iescamochero@agresta.org (I.E.); prodriguez@agresta.org (P.R.-N.)
2Freelance, Plaza Constitución 8, Chapinería, Madrid 28694, Spain; jesusfmoya@gmail.com
3Carbon Decisions International (CDI), Residencial La Castilla, Paraíso de Cartago 30201, Costa Rica;
lpedroni@carbondecisions.com (L.P.); jfvillegas@carbondecisions.com (J.F.V.)
4DIMAP, CEI Montegancedo, Madrid 28223, Spain; fgarcia@dimap.es
5AFOLU Global Services, C/Jimenez Diaz, Pozuelo Alarcón 28224, Spain;
andres.espejo@afoluglobalservices.com
6UPM, Avda Profesor Aranguren, Madrid 28040, Spain; miguel.marchamalo@upm.es
7UCR, Ciudad de la Investigación, San José 11501, Costa Rica; jbonatti2011@gmail.com
8World Bank, 1818 H Street, NW, Washington, DC 20433, USA; spapageorgiou@worldbank.org (S.P.);
efernandes@worldbank.org (E.F.)
*Correspondence: afernandez@agresta.org; Tel.: +34-975-215-202
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 7 April 2016; Accepted: 8 July 2016; Published: 13 July 2016
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.
Keywords:
open source; forest; deforestation; IR MAD; Random Forest; Landsat; QGIS; R;
ORFEO; python
Remote Sens. 2016,8, 593; doi:10.3390/rs8070593 www.mdpi.com/journal/remotesensing

Remote Sens. 2016,8, 593 2 of 17
1. Introduction
“Reducing Emissions from Deforestation and Forest Degradation and the Role of Conservation,
Sustainable Management of Forests and Enhancement of Forest Carbon Stocks in Developing
Countries” (REDD+) was first introduced as a global mechanism to create results-based incentives
at the 11th Conference of the Parties (COP) to the United Nations Framework Convention on
Climate Change (UNFCCC) in 2005. Since then, REDD+ has become one of the most important
and dynamic issues regarding climate change mitigation in the forest sector; having a big impact on the
environmental agenda of many developing countries [
1
]. However, designing effective REDD+ policies
and actions as well as assessing their impact on Greenhouse Gas (GHG) emissions requires a solid
information basis [
2
]. This includes the development of credible national Forest Reference Emission
Levels and/or Forest Reference Levels (FRELs/FRLs), which serve as benchmarks for assessing each
country’s performance in implementing REDD+ activities [
3
] and the establishment of robust and
transparent National Forest Monitoring Systems (NFMS) for measuring and reporting forest-related
GHG emissions and removals accurately and consistently over time [
4
]. Indeed, the lack of reliable and
comparable data on land cover (LC) and land cover change (LCC) has been reported as an important
barrier for the establishment of FRELs/FRLs and the Measurement, Reporting and Verification (MRV)
of forest-related GHG emissions and removals in developing countries [5–8].
As pointed out by other authors [
9
], satellite imagery is perhaps the only affordable data source
available to developing countries for generating LC information with sufficient consistency and
periodicity. The generation of reliable historical LC data is crucial for developing FRELs/FRLs which
have to be established taking into account historical data [
3
]. However, the acquisition of costly satellite
imagery and the use of proprietary software for its processing would require allocating significant
resources to monitoring activities, which may not be affordable for many developing countries, thus
undermining the sustainability of their NFMS [
10
]. Fortunately, today, there are no-cost or low-cost
alternatives that allow a robust and consistent LC analysis. Landsat imagery provides medium spatial
resolution multispectral data (http://landsat.usgs.gov) free of charge for users that are ideal for
establishing national FRELs/FRLs since it provides land-cover information without interruption since
1972 [
9
,
11
]. In addition, free open source QGIS software (www.qgis.org) integrates other open source
software such as R and Python. These software products have been proven to be a powerful tool
for remote sensing processing and viable alternatives to other high-cost specialized software [
12
].
However, proof-of-concept on the extensive use of open source software in complex LC classification
processes is still needed.
The main objective of this study is to design a consistent and replicable methodological workflow
for processing LC and LCC maps from free satellite imagery with open source software useful in the
REDD+ Reference level schemes.
Costa Rica has been a pioneer among tropical countries in the implementation of conservation-
oriented policies, such as the Payments for Environmental Services (PES) Program and the Forest Law
No. 7575 of 1996, which prohibits land use change in forested areas. Currently, Costa Rica intends to
continue its efforts in forest conservation through the establishment of a national REDD+ program
which will seek results-based payments. In order to evaluate the REDD+ program performance,
historical data (including LC data) is required for setting the national FRELs/FRLs. Some LC studies
have been carried out in Costa Rica since the 1970s, analyzing the influence of environmental policies
and other socioeconomic factors on forest dynamics [
13
–
22
]. However, despite the quality and quantity
of historical LC data in the country, these data were produced with different objectives and have
different technical specifications [
23
], raising the question of whether any detected changes would
be due to actual LC dynamics or to the lack of consistency in the methods used. Generating these
data using a consistent methodology that can be replicated in the future for monitoring purposes is
an important step for Costa Rica in preparation for its participation in future REDD+ mechanisms.
In addition, the abundance of available data to assess the accuracy of historical LC maps makes Costa
Rica a perfect ground for testing a methodology that can be used worldwide to generate consistent

Remote Sens. 2016,8, 593 3 of 17
time series of historical LC maps based on free for users satellite imagery, ancillary data and free open
source software (QGIS and R).
2. Materials and Methods
2.1. Historical Period
The choice of historical period for the design of the LC time series takes into account two major
government policies and actions relative to forest conservation in Costa Rica: (1) the launch of the
Payments for Environmental Services (PES) Program (23 January 1997) subsequent to the Forest law of
1996; and (2) the endorsement of Ecomercados II project by law (7 March 2008), which scaled up the
PES Program significantly. Taking these temporal landmarks into account, the following years were
considered in order to analyze the LC dynamics before and after their implementation: 1985/1986,
1991/1992, 1997/1998, 2000/2001, 2007/2008, 2011/2012 and 2013/2014.
2.2. Data
Full coverage of Costa Rica requires seven Landsat tiles (paths 14–16 and rows 52–54) (Figure 1).
The combination of multiple images for each date and tile was necessary in order to reduce the
percentage of cloud cover and cloud shadows in the final maps, yet only images within a 14 month
range were considered in order to ensure temporal consistency as suggested under the Verified Carbon
Standard Jurisdictional and Nested REDD+ (VCS-JNR) protocols. For this reason, the LC maps
compiled data over two years (e.g., 1985/1986). A total of 384 Landsat images (30 m resolution)
were used to fill the gaps caused by the abundant cloud cover and shadows: 48 from 1985/1986,
31 from 1991/1992, 65 from 1997/1998, 46 from 2000/2001, 53 from 2007/2008, 53 from 2011/2012
and 88 from 2013/2014 (Figure 1). Imagery from Landsat 4 and 5 Thematic Mapper (TM), Landsat 7
Enhanced Thematic Mapper (ETM+) and Landsat 8 Operational Land Imager and Thermal InfraRed
Sensor (OLI/TIRS) were used. The images were selected between December and April (dry season) to
minimize seasonal variations, except for the tiles in path 16, where some images were selected also
from the rainy season to avoid confusion caused by the presence of deciduous forest.
Remote Sens. 2016, 8, 593 3 of 17
to generate consistent time series of historical LC maps based on free for users satellite imagery,
ancillary data and free open source software (QGIS and R).
2. Materials and Methods
2.1. Historical Period
The choice of historical period for the design of the LC time series takes into account two major
government policies and actions relative to forest conservation in Costa Rica: (1) the launch of the
Payments for Environmental Services (PES) Program (23 January 1997) subsequent to the Forest law
of 1996; and (2) the endorsement of Ecomercados II project by law (7 March 2008), which scaled up
the PES Program significantly. Taking these temporal landmarks into account, the following years
were considered in order to analyze the LC dynamics before and after their implementation:
1985/1986, 1991/1992, 1997/1998, 2000/2001, 2007/2008, 2011/2012 and 2013/2014.
2.2. Data
Full coverage of Costa Rica requires seven Landsat tiles (paths 14–16 and rows 52–54) (Figure 1).
The combination of multiple images for each date and tile was necessary in order to reduce the
percentage of cloud cover and cloud shadows in the final maps, yet only images within a 14 month
range were considered in order to ensure temporal consistency as suggested under the Verified
Carbon Standard Jurisdictional and Nested REDD+ (VCS-JNR) protocols. For this reason, the LC
maps compiled data over two years (e.g., 1985/1986). A total of 384 Landsat images (30 m resolution)
were used to fill the gaps caused by the abundant cloud cover and shadows: 48 from 1985/1986, 31
from 1991/1992, 65 from 1997/1998, 46 from 2000/2001, 53 from 2007/2008, 53 from 2011/2012 and 88
from 2013/2014 (Figure 1). Imagery from Landsat 4 and 5 Thematic Mapper (TM), Landsat 7
Enhanced Thematic Mapper (ETM+) and Landsat 8 Operational Land Imager and Thermal InfraRed
Sensor (OLI/TIRS) were used. The images were selected between December and April (dry season)
to minimize seasonal variations, except for the tiles in path 16, where some images were selected also
from the rainy season to avoid confusion caused by the presence of deciduous forest.
Figure 1. Details of the number of Landsat images processed for each year of the time series and each
of the 7 Landsat tiles which covers Costa Rica: paths (p) 14–16, rows (r) 52–54.
Figure 1.
Details of the number of Landsat images processed for each year of the time series and each
of the 7 Landsat tiles which covers Costa Rica: paths (p) 14–16, rows (r) 52–54.

Remote Sens. 2016,8, 593 4 of 17
In addition to the aforementioned Landsat satellite images, several data products were compiled
in collaboration with local institutions: (i) Rapid Eye imagery (5 m resolution) from 2012 for the
validation procedure of LC maps; (ii) a Digital Elevation Model (30 m resolution) from the Shuttle Radar
Topography Mission (SRTM) as predictor variables; (iii) 10,000 control points with LC information from
2013 based on Rapid Eye imagery to create the training sample provided by SINAC (Conservation
Area national System from Costa Rica); (iv) 21,395 control points with LC information (considered as
ground reference data) for the years 1985/1986, 2000/2001 and 2013/2014 provided by INBio (National
Biodiversity Institute from Costa Rica) for independent validation of the final land cover maps.
2.3. Classification Scheme
Seven individual LC maps were produced for the years 1985/1986, 1991/1992, 1997/1998,
2000/2001, 2007/2008, 2011/2012 and 2013/2014 through an open source software based workflow
(Figure 2).
Remote Sens. 2016, 8, 593 4 of 17
In addition to the aforementioned Landsat satellite images, several data products were compiled
in collaboration with local institutions: (i) Rapid Eye imagery (5 m resolution) from 2012 for the
validation procedure of LC maps; (ii) a Digital Elevation Model (30 m resolution) from the Shuttle
Radar Topography Mission (SRTM) as predictor variables; (iii) 10,000 control points with LC
information from 2013 based on Rapid Eye imagery to create the training sample provided by SINAC
(Conservation Area national System from Costa Rica); (iv) 21,395 control points with LC information
(considered as ground reference data) for the years 1985/1986, 2000/2001 and 2013/2014 provided by
INBio (National Biodiversity Institute from Costa Rica) for independent validation of the final land
cover maps.
2.3. Classification Scheme
Seven individual LC maps were produced for the years 1985/1986, 1991/1992, 1997/1998,
2000/2001, 2007/2008, 2011/2012 and 2013/2014 through an open source software based workflow
(Figure 2).
Figure 2. Methodological workflow for land cover and land cover change classification.
Figure 2. Methodological workflow for land cover and land cover change classification.

Remote Sens. 2016,8, 593 5 of 17
The LC maps for the years 1985/1986, 1991/1992 and 2000/2001 show a simplified classification
scheme of two classes: forest and non-forest. The LC maps for the years 1997/1998, 2007/2008,
2011/2012 and 2013/2014 show a complete classification scheme of 12 different classes, which
represents a disaggregation of the IPCC Land Use Categories (Table 1). This classification scheme was
developed following the 2006 IPCC Guidelines for National Greenhouse Gas Inventories and it was
agreed as part of the national REDD+ process with relevant stakeholders. In addition to these 12 LC
classes, six forest age classes per forest class were obtained through a multi-temporal analysis of the
maps. Forest was defined as a minimum land area of one hectare with tree crown cover of more than
30% and a minimum tree height at maturity of 5 m, in accordance with the definition stated for the
Clean Development Mechanism (CDM) projects in the country.
Table 1.
Comparison between the Land Use Categories considered in IPCC (2006) and the classes
considered in the Land Cover (LC) Classification in Costa Rica for the years 1997/1998, 2007/2008,
2011/2012 and 2013/2014.
IPCC
2006
Class
LC Costa Rica LC Class Description
Forest
Forest Including also planted forests
Mangrove Waterlogged forest
Palm forest Waterlogged forest
Settlement
Settlement
Grassland
Grassland Including anthropic pastures and natural
grasslands mainly in waterlogged areas
Wetland
Water Water bodies
Other
land
Bare land
Paramo
Cropland
Annual crops Including rice, sugar cane and other annual crops
Pineapple
Coffee
Other permanent crops
Including oil palm, orange, mango and other permanent crops
LC change maps were produced through post-classification of individual maps, which were used
to derive a LC change matrix for each of the six periods. Deforestation was defined as the transition
from any of the forest to a non-forest class while forest gain was defined as the opposite transition.
Forests existing since the first LC map were considered “primary forest” for this study, as it will be
further discussed.
2.4. Software
Imagery preparation was completed using different open-source software. Open-source libraries
and geographic information systems such as ORFEO Tool Box (OTB) [
24
], Geospatial Data Abstraction
Library (GDAL) [
25
], System for Automated Geoscientific Analyses (SAGA) [
26
] and Geographic
Resources Analysis Support System (GRASS) [
27
] were used through QGiS [
28
] processing framework.
QGiS framework is a geoprocessing environment used to integrate native and third-party algorithms
from QGIS. Specific tools and workflows adapted to the needs of the project were developed using
Python scripts for the QGIS processing framework. In addition, R was used as the environment for
statistical computing [29].
2.5. Imagery Pre-Processing
Geometric validation was firstly carried out for every single image with 20 ground control points
per image. Whenever geometric correction was needed, orthorectification was carried out with the
Digital Elevation Model (DEM). The radiometric calibration involved the use of standard equations

Remote Sens. 2016,8, 593 6 of 17
to convert calibrated Digital Numbers (DN) to a satellite reflectance. Imagery were first converted
to at-satellite radiance, and then to at-satellite reflectance [
30
]. Iteratively Re-weighted Multivariate
Alteration Detection (IR-MAD) [
31
] was used for radiometric normalization of the images. The
IR-MAD Python scripts developed by Canty [
32
] were integrated in QGIS. Images with the least
cloud coverage were selected as reference images for the process. IR-MAD uses Canonical Correlation
analysis (CCA) to find linear combinations between two groups of variables (i.e., the spectral bands
of subject and reference images) to generate normalized multispectral images. This algorithm is
commonly used in direct change detection approaches [
31
] but it has shown a good performance for
radiometric normalization, avoiding the need for training the classifier for each year with specific ROIs
which presents clear advantages in long term monitoring schemes.
An adaptation of the methodology from Martinuzzi et al. [
33
] was used for cloud and cloud
shadow detection for Landsat 4, 5 and 7. For Landsat 8 images, the Quality Assessment Band was
employed for cloud detection. A cloud distance map was elaborated to focus on the cloud shadow
detection. Near-infrared values taking into account solar illumination and mean cloud height were
used in the detection of cloud shadows. The cloud distance map consisted on a range of 4–32 pixels
West and 2–27 pixels North from the cloud position. Quality control and validation of the cloud and
shadow mask were done with a random point set over 18 randomly selected images. Results from the
validation of the masks showed an accuracy of over 95% in the randomly sampled images.
2.6. Predictor Variables
Once the set of images was pre-processed as described in the previous sections, several variables
were calculated based on the spectral information of the Landsat images in order to use them
as co-variates for image classification. A Normalized Difference Vegetation Index (NDVI) and
different Haralick’s texture indices were computed, i.e., Mean, Sum Entropy, Difference of Entropies,
Difference of Variances, IC1 and IC2 [
34
]. In addition, several variables were calculated based on
the DEM: elevation, slope, hillshade, plan curvature, profile curvature, Convergence Index (CI) and
Multiresolution Index of Valley Bottom Flatness (MRVBF) [
35
]. Finally, a stack of 20 bands was
generated including 6 spectral bands (1–5 and 7 for Landsat 4, 5 and 7; 2–7 for Landsat 8), NDVI,
6 texture indices and 7 variable derivatives from the DEM (Table 2).

Remote Sens. 2016,8, 593 7 of 17
Table 2.
Predictor variables used in the supervised classification used to perform a Land Cover (LC)
Classification in Costa Rica for the years 1985/1986, 1991/1992, 1997/1998, 2000/2001, 2007/2008,
2011/2012 and 2013/2014. Bands 1–13 are derived from Landsat images spectral data and bands 14–20
are derived from the Digital Elevation Model from the Shuttle Radar Topography Mission (STRM 30 m).
Group of Variables Band Number Variable
Spectral
1 Blue
2 Green
3 Red
4 NIR
5 SWIR-1
6 SWIR-2
Vegetation Index 7 Normalized Difference Vegetation Index (NDVI)
Texture Indices
8 Mean
9 Sum Entropy
10 Difference of Entropies
11 Difference of Variances
12 IC1
13 IC2
Digital Elevation Model
14 Elevation
15 Slope
16 Hillshade
17 Plan curvature
18 Profile curvature
19 Convergence Index (CI)
20 Multiresolution Index of Valley Bottom Flatness (MRVBF)
2.7. Image Classification
A supervised classification method was used to classify the 20 band stacks described above.
The Random Forest (RF) classifier [
36
] was selected to perform this classification. This methodology
was implemented in R, using the R package “RandomForest: Breiman and Cutler
'
s Random Forests
for classification and regression” [
37
], following the methodology proposed by Horning [
38
], modified
for multiple image classification with a single algorithm. The 20 variables described above were used
as explanatory co-variables in the RF models (Table 2). Random forests improve classification accuracy
by growing an ensemble of classification trees and letting them vote on the classification decision.
The percentage of the Random Forest decision trees voting for the final classification was computed as
a classification quality factor (CQ). The classification using RF has two main steps: (i) training of the
model; (ii) classifying the image using the trained classifier.
For the model training, regions of interest (ROIs) were manually defined based on (i) 10,000
control points with land cover information based on Rapid Eye imagery; (ii) high resolution historical
images from Google Earth; (iii) Landsat images. These ROIs are considered as ground reference
data, and they were used to adjust three different sets of models using: (i) Landsat 8 images (RF_L8
models); (ii) Landsat 5 and 7 images of the 2011/2012 and 2013/2014 dry seasons (RF_L5&7 models);
and (iii) Landsat 5 and 7 images of the 2011/2012 and 2013/2014 rainy seasons, only for the NW tile
(RF_L5&7_rainy model). Therefore classifiers have been adjusted using ground truth from 2011 to 2014
and have subsequently been applied to all images in the time period (from 1985 to 2014). Models RF_L8
were applied to the same Landsat 8 images used for training. The models RF_L5&7 were applied to
all Landsat 5 and 7 images (from 1985 onwards) of the dry season. For the NW tile, RF_L5&7_rainy
model was applied to all the other Landsat 5 and 7 images (from 1985 onwards) of the rainy season.
Mean Decrease Accuracy (MDA) was used to evaluate the importance of the variables in the RF
models. For each predictor variable, MDA is the average of the accuracy across the RF minus the
accuracy after permutation of the predictor. Variable importance helps identifying the most influential
predictor variables in the classification of each LC class.

Remote Sens. 2016,8, 593 8 of 17
2.8. Post-Processing of the Classification
After classifying all 384 Landsat images with the trained RF models, classification results were
composited and mosaicked in order to have a single LC map for the whole country for each year of
the temporal series. The composites were produced through a specific Python tool developed ad-hoc
which assigns the most suitable class to each pixel based on a measure of classification quality (CQ)
given by the RF model to each class in each pixel (e.g., a pixel classified as forest in six different images
and as coffee in two different images would be considered as forest, unless the sum of the CQ of every
coffee classification was higher than the sum of the CQ of all the forest classifications). The compositing
and mosaicking process continuously reduces clouds and clouds shadows gaps by assigning the class
with the highest CQ to each pixel. The output file of the algorithm was a three band raster: (i) a band
containing the classification mosaic; (ii) a band showing a measure of the RF classification quality; and
(iii) a band indicating the used image to assign the class to each pixel.
Additional improvements were made to the maps via the creation of masks to increase the
accuracy of the classification of some classes such as Settlements, Coffee, Paramo, Mangroves and
Palm forests. These masks were based on the criteria given by local experts involved in the national
REDD+ process and based on additional information such as the SINAC 2013 forest type map and the
DEM. A Sieve filter of 11 pixels was applied to the final maps to establish a minimum mapping unit of
1 ha according to Costa Rica forest definition.
Despite the high number of Landsat images used per year and per tile, there were still a relatively
small percentage of pixels without land cover information due to persistent cloud cover and shadows.
To solve this deficiency and report a LC map without any data gap, the final LC maps for the years
2000/2001, 2007/2008, 2011/2012 and 2013/2014 were completed with land cover information from
the Global Forest Change project [
39
]. It is important to note that the Global Forest Change project just
gives information to fill the gaps based on a non-forest and forest map classes, without considering the
IPCC classes used in the classification.
Post-classification LCC detection was made in order to evaluate the historical LC dynamics of
Costa Rica. Based on the information of forest area in each year of the series, an estimation of forest
gains and losses since 1985/1986 was generated by comparison between maps of the series.
2.9. Validation
The LC maps for the years 1985/1986, 2000/2001 and 2013/2014 were validated with external
independent field data provided by the local institutions INBIO and SINAC, not used for training the
classification models. A total of 5396, 7463 and 8536 points for the years 1985/1986, 2000/2001 and
2013/2014, respectively, were used for this process as valid ground reference data.
The LC change map of the period 2001–2011 was also validated. For this validation, a sampling
plan was developed following the guidance set by Olofsson [
40
] which resulted in a sampling size
of 649 points which were randomly selected and allocated to different post-stratification classes: 315
in stable forest areas, 223 stable non-forest areas, 63 in forest gain areas and 48 in deforestation areas.
The ground truthing response of these points was obtained from available high resolution imagery
in Costa Rica (2005 airborne photography and 2012 Rapid Eye imagery), free access high resolution
database (Google Earth Digital Globe) and Landsat imagery.
Accuracy indicators for all validations were estimated using the equations and guidance provided
in Olofsson [40].
3. Results
3.1. Land Cover Classification
The performed validation showed an overall accuracy of 87% for the LC map of 2013/2014 (with
full legend including all the described LC classes), 93% for the 2000/2001 map (only forest/non forest

Remote Sens. 2016,8, 593 9 of 17
classes) and 89% for the 1985/1986 map (only forest/non forest classes) (Table 3). These overall
accuracy values are within the range indicated by GOFC-GOLD [10].
Table 3.
Validation of the Land Cover (LC) classification in Costa Rica for the years 1985/1986,
2000/2001 and 2013/2014. These dataset was mainly provided by the National Biodiversity Institute
from Costa Rica (INBio) and is considered as a valid ground truth by the Government of Costa Rica.
Validation Year Overall
Accuracy Land Cover Class Producer’s
Accuracy
User’s
Accuracy
1985/1986 0.89 Forest 0.89 0.91
No-forest 0.88 0.85
2000/2001 0.93 Forest 0.92 0.94
No-forest 0.94 0.92
2013/2014 0.87
Forest 0.91 0.91
Palm forest 0.68 0.99
Mangrove forest 0.97 0.91
Settlement 0.99 0.92
Grassland 0.85 0.76
Paramo 0.94 0.85
Water 0.97 0.60
Bare land 0.19 0.58
Annual crops 0.81 0.88
Pineapple 0.88 0.93
Other permanent crops (including coffee)
0.82 0.93
The most important variable in the classification (Figure 3) is elevation (B14), followed by the
spectral variables (from B1 to B7) and slope (B15). The MRVBF (B20) and one of the texture variables
(B8) also contributed to a lesser extent to the classification, while the importance of the remaining
variables was small compared to the former (Figure 3). However, the relative importance of these
variables in the classification varies depending on each class (Figure 3). Regarding the spectral bands
(from B1 to B7) and slope (B15), it can also be observed how the relative importance of these variables
fluctuate depending on each class, e.g., the SWIR-1 band (B5) turned out to be a very important
variable for classifying grassland and coffee and its importance was lower in other classes such as
forest, waterlogged forests, settlement or pineapple, while slope (B15) was moderately important for
classifying forest, coffee or pineapple and its importance was lower in other classes such as grasslands,
settlement or waterlogged forests (Figure 3).
Remote Sens. 2016, 8, 593 9 of 17
variables was small compared to the former (Figure 3). However, the relative importance of these
variables in the classification varies depending on each class (Figure 3). Regarding the spectral bands
(from B1 to B7) and slope (B15), it can also be observed how the relative importance of these variables
fluctuate depending on each class, e.g., the SWIR-1 band (B5) turned out to be a very important
variable for classifying grassland and coffee and its importance was lower in other classes such as
forest, waterlogged forests, settlement or pineapple, while slope (B15) was moderately important for
classifying forest, coffee or pineapple and its importance was lower in other classes such as
grasslands, settlement or waterlogged forests (Figure 3).
Figure 3. Measurement of the relative variable importance of the predictor variables in the adjusted
Random Forests for classifying: (A) complete land cover classification; (B) forest class; (C) palm forest
classes; (D) settlement class; (E) grassland class; (F) pineapple class; (G) coffee class.
Despite the high number of Landsat images processed and classified in each year of the time
series, a small percentage of pixels remained without classification due to clouds and shadows
(Figure 4). However, this percentage is less than 2% of the country for every year in the series.
Figure 3.
Measurement of the relative variable importance of the predictor variables in the adjusted
Random Forests for classifying: (
A
) complete land cover classification; (
B
) forest class; (
C
) palm forest
classes; (D) settlement class; (E) grassland class; (F) pineapple class; (G) coffee class.

Remote Sens. 2016,8, 593 10 of 17
Despite the high number of Landsat images processed and classified in each year of the time series,
a small percentage of pixels remained without classification due to clouds and shadows (Figure 4).
However, this percentage is less than 2% of the country for every year in the series.
Remote Sens. 2016, 8, 593 9 of 17
variables was small compared to the former (Figure 3). However, the relative importance of these
variables in the classification varies depending on each class (Figure 3). Regarding the spectral bands
(from B1 to B7) and slope (B15), it can also be observed how the relative importance of these variables
fluctuate depending on each class, e.g., the SWIR-1 band (B5) turned out to be a very important
variable for classifying grassland and coffee and its importance was lower in other classes such as
forest, waterlogged forests, settlement or pineapple, while slope (B15) was moderately important for
classifying forest, coffee or pineapple and its importance was lower in other classes such as
grasslands, settlement or waterlogged forests (Figure 3).
Figure 3. Measurement of the relative variable importance of the predictor variables in the adjusted
Random Forests for classifying: (A) complete land cover classification; (B) forest class; (C) palm forest
classes; (D) settlement class; (E) grassland class; (F) pineapple class; (G) coffee class.
Despite the high number of Landsat images processed and classified in each year of the time
series, a small percentage of pixels remained without classification due to clouds and shadows
(Figure 4). However, this percentage is less than 2% of the country for every year in the series.
Figure 4.
Distribution of the forest and non-forest land cover classes in Costa Rica for every year of the
temporal series (1985/1986, 1991/1992, 1997/1998, 2000/2001, 2007/2008, 2011/2012 and 2013/2014).
Details about the percent of the country where no class could be assigned due to clouds and shadows
are reported in parenthesis. There are not gaps due to cloud and cloud shadows for the years 2000/2001,
2007/2008, 2011/2012 and 2013/2014. For the remainder of the years, gaps without information were
filled using land cover information from the Global Forest Change project [39].
3.2. Land Cover Change Detection
Forest cover changes were validated for the period 2001–2011, with an overall accuracy of 86%
(Table 4). Even though this result may be considered as satisfactory, there is a noticeable difference
between the accuracy in forest and non-forest stable classes (87%–88% user accuracy) and the forest
change classes (62%–75% user accuracy) (Table 4). LC and LCC areas for the entire historical time
series are specified in the Costa Rica Forest Reference Emission Level available at the Forest Carbon
Partnership Facility (FCPF).
Table 4.
Validation of the observed Land Cover Changes in Costa Rica between 2001 and 2011.
The validation was done using the methodology proposed by Olofsson [
40
] and it is based on 649
randomly selected control points. Confidence intervals (95% significance level) of the accuracy values
are reported in parenthesis.
Change Classes Description Producer’s Accuracy
(Omission)
User’s Accuracy
(Commission) Overall Accuracy
Deforestation Forest to Non-Forest 0.49 (0.36–0.62) 0.62 (0.47–0.77)
0.86 (0.83–0.89)
Afforestation/reforestation
Non-Forest to Forest 0.50 (0.38–0.62) 0.75 (0.62–0.88)
Forest (no change) Forest remaining Forest 0.95 (0.93–0.97) 0.88 (0.84–0.91)
Non-forest (no change) Non-Forest remaining Non-Forest 0.84 (0.8–0.88) 0.87 (0.83–0.92)
Forest age-classes were estimated through a multi-temporal analysis of the maps. Age classes
vary according to the time difference between the maps. The elapsed time since the appearance of
forest in a pixel allows assigning the forest age-class to each forest pixel of the maps (Table 5). Forests
remaining as forests are ascending in age-class in each study period, so a forest age-class area can be

Remote Sens. 2016,8, 593 11 of 17
compared with the subsequent age-class area in the following study period (e.g., forest age class 1 in
1991/1992 would correspond with forest age class 2 in 1997/1998).
Table 5.
Forest age computed for the time series dataset since 1985/1986. Forest age classes show
different age per period due to different time period lengths.
Forest Change Detection Period 1991 1997 2000 2007 2011 2013
1986–1991 1–6 7–12 13–15 16–22 23–26 27–28
1992–1997 1–6 7–9 10–16 17–20 21–22
1998–2000 1–3 4–10 11–14 15–16
2001–2007 1–7 8–11 12–13
2008–2011 1–4 5–6
2012–2013 1–2
4. Discussion
4.1. Land Cover Classification
The usefulness of classifiers based on decision trees, e.g., data mining tools C5 classifier developed
by Quinlan [
41
], had already been discussed for Costa Rica, as they allow the incorporation of spectral
information together with a variety of other variables such as textural bands and DEM derivatives,
among others [
42
,
43
]. The incorporation of global no charge for user DEM, as Aster Global Elevation
Map which is more recent and with highest resolution or Sentinel imagery from mission Copernicus,
would help improve the final results in further research. The classifications performed as part of
the present paper were based on the Random Forest models, which are nowadays considered as the
most efficient tool for adjusting these kinds of models [
12
,
44
–
48
]. The good performance of RF in this
LC analysis at a national scale reinforces the evidence for the high potential of this tool to perform
operational classification tasks [
12
]. This is especially relevant considering that RF is a free open source
classifier which can be easily used in a free open source environment such as R. Indeed, the performed
LC was fully implemented using free open source software (ORFEO, GDAL, SAGA, GRASS, QGIS
and R), pointing out the capability of these tools for the operational implementation of this kind of
LC tasks at a national level, handling a high number of images, as it has been previously shown [
12
].
Different free tools such as the available set for temporal data processing in GRASS are also interesting
alternatives to include in the workflow to LC and LCC maps at the national scale.
Although the models were only trained for 2011/2012 and 2013/2014 imagery, the validated
performance of the model over 2000/2001 and 1985/1986 imagery shows the effectiveness of the
normalization process with the IR–MAD algorithm. Ground truth LC datasets might do not exist
when generating a long historical series, especially for the oldest periods of these series. However,
large amount of high quality data is usually available in recent years. Therefore, the most recent
and complete available field information is used in the adjustment and validation processes. Our
methodology allows generating a long historical LC series even though ground truth is only available
for a few periods of the series.
The high relevance of elevation in LC has been previously described by other authors [
12
,
48
–
50
].
In a country such as Costa Rica, with an elevation range of 0–3819 m in an area of just 51,100 km
2
,
elevation plays a key role in the arrangement of land cover in the landscape and it has been traditionally
observed as a determinant ecological variable and a proxy for several other environmental variables
such as precipitation and temperature [
51
]. Landsat SWIR-1 band (2nd most important variable in the
present study, Figure 3) has also been previously identified as a key variable for LC, associated with its
vegetation and soil moisture sensitivity properties [
12
]. Moisture sensitivity was an important factor
in the present study because Landsat imagery was selected from the dry period and, consequently,
different land covers situated in different landscape positions maintain different moisture levels.
Indeed, the SWIR-1 band was the most relevant variable for grassland determination (Figure 3) which
explains the inclusion within this grassland class of many wetlands with grassland vegetation.

Remote Sens. 2016,8, 593 12 of 17
Obtaining close-to-cloud-free LC maps for every year of the series is considered as one of the
biggest accomplishments of the present study and one of the keystones of the proposed methodology.
Clouds represent a significant inconvenient in humid tropical regions such as Costa Rica [
33
] and
achieving a cloud-free LC is important in the REDD+ context in order to comply with the principle
of completeness.
The remaining low proportion of data gaps caused by the permanent presence of clouds and
shadows could be then filled with data from global products. Global Forest Change project [
39
] was
used in the present case, but others global projects can be also used, such as the Global Forest/Non
Forest map from ALOS Palsar mosaics [52].
4.2. Land Cover Change Detection
Although post-classification LCC detection has some limitations (due to errors propagation)
compared to direct LCC detection, post-classification was selected due to the need of mapping large
number of classes in each LC and LCC maps. The methodology presented allows knowing the LC
class after deforestation that permits more accurate estimations of GHG emissions from land use and
land use change.
The six Forest age-classes obtained through the multi-temporal analysis of the series (Table 5)
allows for accurately assigning forest age in each period thereby reducing the area considered as
“primary forest” along the series. This is useful for assigning more accurate emission factors to
deforested areas and to assign more accurate removal factors to young secondary forests as well as
modeling future rates of carbon emissions and removals. This methodology is feasible due to the
large number of available study periods. Some limitations arise nevertheless from the hypothesis to
determine the first age-class as forest areas in the first year (1985/1986) of the series as “primary forest”
(Table 5) as it is likely that forests in 1985/1986 include significant areas of secondary forests which
resulted from forests regenerated in the 80s as a consequence of the cattle crisis [
53
]. Therefore, some
of the deforestation detected over these “primary forests” would eventually occur over these young
secondary forests. This limitation can be mitigated by dividing the areas classified as forest in the
first year of the time series (1985/1986) into “primary” and “secondary” forest. For the subsequent
construction of FRELs/FRLs, this division was performed based on an auxiliary map with areas of
secondary forest in 1978–1980 elaborated by the National Meteorological Institute of Costa Rica.
The complexity to separate primary and older secondary forest is a common limitation in all
remote sensing temporal series of LC change. The shorter the time series, the greater the influence
of the potential limitation would be. A short temporal series generates a lower age-class range and
therefore a less accurate estimation of emissions and removals, e.g., assuming as “primary forest” all
forests from 2000/2001 is less accurate than assuming forest from 1985/1986 as “primary forest”.
By linking deforestation dynamics with forest age, a more accurate description of the process
of LCC and associated GHG emissions along the time series can be presented. Figure 5b shows
an example of the spatial distribution of age classes and deforestation processes, pointing out that
these processes occur mainly along the border of areas of “primary forests” and in areas of young
secondary forests. Deforestation of young secondary forests is the dominant land-use change process
in the time series (Figure 5b). This provides a new understanding of deforestation processes in the
context of REDD+ initiatives in Costa Rica. Emission factors of young secondary forests are lower
than those of older or primary forests and accounting for this difference is critical for the construction
of a credible FRELs/FRLs and posterior MRV. Moreover, forest age classes could be used to help
define when regenerated vegetation is considered as forest, either according to the definition of
“forest” under REDD+ or according to the legal definition of “forest”. In Costa Rica, the relationship
between vegetation age and diameter at breast height (dbh) of the trees (trees of more than 15 cm
dbh according to the Forest Law No. 7575) could be used to assess whether an area with new woody
vegetation is already a “forest” according to the Forest Law. Grassland-fallow dynamics in Costa Rica
illustrates this situation. Clearing recently forested land before it reaches the thresholds at which it

Remote Sens. 2016,8, 593 13 of 17
is considered “forest” is a common practice among landowners to avoid the ban on forest clearing
under the aforementioned law [
21
]. This clearing of young forest age-classes that is detected in our
results (Figure 5b) has been documented in Costa Rica before [
17
] and it is considered one of the most
common patterns of LC dynamics in the country.
Remote Sens. 2016, 8, 593 13 of 17
Figure 5. Illustration of land cover change dynamics in sample areas in Costa Rica: (a) Permanent
cropland expansion from 1986 to 2013; (b) Forest age class distribution and deforestation patterns
over different forest age classes.
The ample number of LC classes allows for improving the study of deforestation drivers in a
spatial explicitly model. The comparison of LC maps clearly shows the dynamics of land cover in
Costa Rica in the last 28 years, e.g., pineapple crops expansion in the last decades over former forest
or grassland areas (Figure 5a). This information can be used for the interconnection and
complementarity of monitoring efforts for different initiatives in the national greenhouse gas
emissions framework, such as Nationally Appropriate Mitigation Actions (NAMAs) in the
agriculture or other sectors and the National REDD+ Strategy.
The high comparability of Landsat images and the use of a single classification model for all
years ensures strong consistency among the generated historical LC maps, which is considered a
mandatory requirement for this kind of analysis within the REDD+ context [7].
Figure 5.
Illustration of land cover change dynamics in sample areas in Costa Rica: (
a
) Permanent
cropland expansion from 1986 to 2013; (
b
) Forest age class distribution and deforestation patterns over
different forest age classes.
A challenge for Costa Rica, and possibly for other countries as well, is that the parameters and
thresholds used to define “forest” under the national legislation are different of those used in the
context of REDD+ and the national GHG inventory. Defining a minimum forest age class could thus
help finding a definition of “deforestation” that is consistent with the Law and applicable in the context
of REDD+, helping also to determine the periodicity required in order for future monitoring events to
appropriately assess deforestation events.

Remote Sens. 2016,8, 593 14 of 17
Additionally, the spatial pattern of different forest age classes obtained through this time series
(e.g., Figure 5b) allows complementary studies of secondary forest fragmentation, which has been
identified as a key issue in deforestation and forest regeneration studies in the region [43,53,54].
In countries like Costa Rica, where a large proportion of the forests are secondary forests, the study
of a long historical period to assess LC and LCC is critical for assessing the distribution of age classes
in forested areas and to generate more accurate estimates of emissions from deforestation. Our study
covers a historical period of nearly 30 years and includes seven LC maps. Although the historical
period considered for establishing the national FRELs/FRLs is usually around 10 years, the analysis
of a longer period is critical for avoiding an over-estimation of emissions from deforestation. This is
especially important in tropical areas where the fast growth of vegetation enables a rapid recuperation
of forest cover detectable in satellite images, although the carbon content of young secondary forests
may take decades and even centuries to equal the carbon content of primary forests.
The ample number of LC classes allows for improving the study of deforestation drivers in a
spatial explicitly model. The comparison of LC maps clearly shows the dynamics of land cover in
Costa Rica in the last 28 years, e.g., pineapple crops expansion in the last decades over former forest or
grassland areas (Figure 5a). This information can be used for the interconnection and complementarity
of monitoring efforts for different initiatives in the national greenhouse gas emissions framework,
such as Nationally Appropriate Mitigation Actions (NAMAs) in the agriculture or other sectors and
the National REDD+ Strategy.
The high comparability of Landsat images and the use of a single classification model for all years
ensures strong consistency among the generated historical LC maps, which is considered a mandatory
requirement for this kind of analysis within the REDD+ context [7].
5. Conclusions
A methodology for generating consistent data for the establishment of a historical time series
and subsequent monitoring of LC dynamics, based on Landsat imagery and open-source software is
presented here and tested as a valid operational framework in the context of the REDD+ mechanism.
The good validation results for both LC and LC change maps confirms the capability of Random Forest,
R package, QGIS and other free open source software (ORFEO, GDAL, SAGA, GRASS, QGIS and R)
for the implementation of LC classification maps, which can lower the costs of this kind of projects
within the REDD+ context for many developing countries.
An almost cloud-free LC map for every year of the time series was obtained by combining a
large number of images. The procedure presented in this paper overcomes the inconvenience of dense
cloud cover recurrent in humid tropical regions, without additional costs. Therefore, the usage of a
worldwide open access imagery database such as Landsat allows for filling the gaps originating from
clouds and cloud shadow removal, representing a cost-effective and replicable methodology with high
relevance for developing countries in the context of REDD+.
The usage of the IR-MAD algorithm in image normalization provides consistency in the
composition of single image classification and to the entire time series of activity data from LCC
allowing for the application of the same trained machine learning algorithm across the entire period of
the series.
Due to the requirement of mapping a large number of LC and LCC classes, post-classification LCC
detection was selected instead of direct change detection. Although having a large number of LC and
LCC classes is a clear advantage for this study, the lower accuracy of deforestation and reforestation
mapping shows the need for improving post-classification LCC methodologies to increase the accuracy,
reducing error propagation.
In addition, this study shows the advantages of obtaining forest age classes from long time
series of activity data. Forest age classes allow improving the assessment of secondary regeneration
according to the forest definition applied and for a more accurate estimation of GHG emissions when
deforestation is assessed.

Remote Sens. 2016,8, 593 15 of 17
Acknowledgments:
This work was developed in the framework of the consultancy “Generating a consistent
historical times series of activity data from land use change for the development of Costa Rica’s REDD+ Reference
Level” funded by the Forest Carbon Partnership Facility (FCPF). The authors are very grateful to the technicians of
the institutions involved in the REDD+ process in Costa Rica, especially FONAFIFO, SINAC, INBio, IMN, PRIAS,
MAG and INEC. FONAFIFO was completely involved in the technical process and decision making, supervised
the methodological decisions and documents, coordinated the meetings with others institutions and provided
and requested all the necessary information. IMN and SINAC participated in different workshops during the
methodological design and provided historical maps, land use datasets and imagery essential for several phases
of the work. INBio provided a huge volume of ground truth data to the validation process of the maps. Comments
and information provided from all these local institutions were essential for the good outcome of the project.
Author Contributions:
Alfredo Fernández-Landa, Nur Algeet-Abarquero, Jesús Fernández-Moya and
María Luz Guillén-Climent
designed the methodology, radiometrically corrected the Landsat imagery, performed
the image classification, generated and validated the final maps and wrote the document. Felipe García
downloaded and prepared the satellite information, geometrically corrected and validated the Landsat images, and
performed clouds and clouds shadows detection. Iñigo Escamochero programmed specifies tools and workflows
in python and adapted IR-MAD scripts into QGiS processing framework. Lucio Pedroni participated as REDD+
expert in the methodological design and in the final version of the paper. Juan Felipe Villegas participated in
the mosaicking process and final edition of maps. The REDD+ expert Andrés Espejo assisted in the design
of the validation procedure and contributed on the writing process. Miguel Marchamalo, Javier Bonatti and
Pablo Rodríguez-Noriega assisted in the whole process like land use expert in Costa Rica, remote sensing expert
and forest carbon projects expert respectively. Stavros Papageorgiou and Erick Fernandes developed the original
terms of reference for the study and participated in the discussions on methodologies and of the early results
during workshops in Costa Rica, and in the final review of the manuscript.
Conflicts of Interest:
The authors declare no conflict of interest. As mentioned above, World Bank technical staff
developed the original terms of reference for the study and participated in the discussions on methodologies and
of the early results during workshops in Costa Rica, and in the final review of the manuscript.
References
1.
Angelsen, A.; McNeill, D. Evolution of REDD. In Analysing REDD+: Challenges and Choices; Angelsen, A.,
Brockhaus, M., Sunderlin, W.D., Verchot, L.V., Eds.; Center for International Forestry Research: Bogor Barat,
Indonesia, 2012; pp. 31–51.
2.
Angelsen, A.; Brockhaus, M.; Sunderlin, W.D.; Verchot, L.V. Analysing REDD: Challenges and Choices;
Center for International Forestry Research (CIFOR): Bogor, Indonesia, 2012.
3.
UNFCCC. Report of the Conference of the Parties on its Seventeenth Session; Decision 12/CP.17, Durban,
28 November to 11 December 2011; UNFCC: Bonn, Germany, 2011.
4.
UNFCCC. Methodological Guidance for Activities Relating to Reducing Emissions from Deforestation and Forest
Degradation and the Role of Conservation, Sustainable Management of Forests and Enhancement of Forest Carbon
Stocks in Developing Countries; Decision 4/CP.15, UN Doc FCCC/CP/2009/11/Add.1 (30 March 2010);
UNFCCC: Bonn, Germany, 2010.
5.
De Sy, V.; Herold, M.; Achard, F.; Asner, G.P.; Held, A.; Kellndorfer, J.; Verbesselt, J. Synergies of multiple
remote sensing data sources for REDD monitoring. Curr. Opin. Environ. Sustain.
2012
,4, 696–706. [CrossRef]
6.
Joseph, S.; Herold, M.; Sunderlin, W.D.; Verchot, L.V. REDD readiness: Early insights on monitoring,
reporting and verification systems of project developers. Environ. Res. Lett. 2013,8, 034038. [CrossRef]
7.
Korhonen-Kurki, K.; Brockhaus, M.; Duchelle, A.E.; Atmadja, S.; Thuy, P.T. Multiple levels and multiple
challenges for REDD. In Analysing REDD 2012; Center for International Forestry Research (CIFOR): Bogor,
Indonesia, 2012; Volume 91.
8.
UNFCCC. Cost of Implementing Methodologies and Monitoring Systems Relating to Estimates of Emissions from
Deforestation and Forest Degradation, the Assessment of Carbon Stocks and Greenhouse Gas Emissions from Changes
in Forest Cover, and the Enhancement of Forest Carbon Stocks; Technical Paper FCCC/TP/2009/1; UNFCCC:
Bonn, Germany, 2009.
9.
Hansen, M.C.; Loveland, T.R. A review of large area monitoring of land cover change using Landsat data.
Remote Sens. Environ. 2012,122, 66–74. [CrossRef]
10.
GOFC-GOLD. A Sourcebook of Methods and Procedures for Monitoring and Reporting Anthropogenic Greenhouse
Gas Emissions and Removals Associated with Deforestation, Gains and Losses of Carbon Stocks in Forests Remaining
Forests, and Forestation; GOFC-GOLD Land Cover Project Office: Wageningen University, The Netherlands,
2013; p. 236.

Remote Sens. 2016,8, 593 16 of 17
11.
Vittek, M.; Brink, A.; Donnay, F.; Simonetti, D.; Desclée, B. Land cover change monitoring using Landsat
MSS/TM satellite image data over West Africa between 1975 and 1990. Remote Sens.
2014
,6, 658–676.
[CrossRef]
12.
Mellor, A.; Haywood, A.; Stone, C.; Jones, S. The performance of random forests in an operational setting for
large area sclerophyll forest classification. Remote Sens. 2013,5, 2838–2856. [CrossRef]
13.
Barton, D.N.; Faith, D.; Rusch, G.; Acevedo, H.; Paniagua, L.; Castro, M. Environmental service payments:
Evaluating biodiversity conservation trade-offs and cost-efficiency in the Osa Conservation Area, Costa Rica.
J. Environ. Manag. 2009,90, 901–911. [CrossRef] [PubMed]
14.
Daniels, A.E.; Bagstad, K.; Esposito, V.; Moulaert, A.; Rodriguez, C.M. Understanding the impacts of Costa
Rica’s PES: Are we asking the right questions? Ecol. Econ. 2010,69, 2116–2126. [CrossRef]
15.
Fagan, M.; DeFries, R.; Sesnie, S.; Arroyo, J.; Walker, W.; Soto, C.; Chazdon, R.; Sanchun, A. Land cover
dynamics following a deforestation ban in northern Costa Rica. Environ. Res. Lett.
2013
,8, 034017. [CrossRef]
16.
Kalacska, M.; Sanchez-Azofeifa, G.A.; Rivard, B.; Calvo-Alvarado, J.C.; Quesada, M. Baseline assessment
for environmental services payments from satellite imagery: A case study from Costa Rica and Mexico.
J. Environ. Manag. 2008,88, 348–359. [CrossRef] [PubMed]
17.
Morse, W.C.; Schedlbauer, J.L.; Sesnie, S.E.; Finegan, B.; Harvey, C.A.; Hollenhorst, S.J.; Kavanagh, K.L.;
Stoian, D.; Wulfhorst, J. Consequences of environmental service payments for forest retention and recruitment
in a Costa Rican biological corridor. Ecol. Soc. 2009,14, 23.
18.
Sanchez-Azofeifa, G.A.; Rivard, B.; Calvo, J.; Moorthy, I. Dynamics of tropical deforestation around national
parks: Remote sensing of forest change on the Osa Peninsula of Costa Rica. Mt. Res. Dev.
2002
,22, 352–358.
[CrossRef]
19.
Sanchez-Azofeifa, A.; Pfaff, A.; Robalino, J.A.; Boomhower, J.P. Costa Rica’s payment for environmental
services program: intention, implementation, and impact. Conserv. Biol.
2007
,21, 1165–1173. [CrossRef]
[PubMed]
20.
Sánchez-Azofeifa, G.A.; Harriss, R.C.; Skole, D.L. Deforestation in Costa Rica: A quantitative analysis using
remote sensing imagery. Biotropica 2001,33, 378–384. [CrossRef]
21.
Sierra, R.; Russman, E. On the efficiency of environmental service payments: a forest conservation assessment
in the Osa Peninsula, Costa Rica. Ecol. Econ. 2006,59, 131–141. [CrossRef]
22.
Pontius, R.G.; Cornell, J.D.; Hall, C.A. Modeling the spatial pattern of land-use change with GEOMOD2:
Application and validation for Costa Rica. Agric. Ecosyst. Environ. 2001,85, 191–203. [CrossRef]
23.
FONAFIFO. Estudio de Cobertura Forestal de Costa Rica; Cooperación Financiera Costa Rica—Alemania:
San José, Costa Rica, 2012.
24. OTB Development Team. ORFEO Tool Box. Centre National d’Études Spatiales; CNES: Paris, France, 2013.
25.
GDAL. GDAL—Geospatial Data Abstraction Library; Open Source Geospatial Foundation: Beaverton, OR,
USA, 2015.
26.
Conrad, O.; Bechtel, B.; Bock, M.; Dietrich, H.; Fischer, E.; Gerlitz, L.; Wehberg, J.; Wichmann, V.; Böhner, J.
System for Automated Geoscientific Analyses (SAGA) version 2.1.4. Geosci. Mod. Dev.
2015
,8, 1991–2007.
[CrossRef]
27.
GRASS Development Team. Geographic Resources Analysis Support System (GRASS) Software; Version 7.0;
Open Source Geospatial Foundation: Beaverton, OR, USA, 2015.
28.
QGIS Development Team. QGIS Geographic Information System; v 2.4; Open Source Geospatial Foundation:
Beaverton, OR, USA, 2009.
29. R Development Core Team. R 2.4.0; R Foundation for Statistical Computing: Vienna, Austria, 2006.
30.
Chander, G.; Markham, B.L.; Helder, D.L. Summary of current radiometric calibration coefficients for Landsat
MSS, TM, ETM, and EO-1 ALI sensors. Remote Sens. Environ. 2009,113, 893–903. [CrossRef]
31.
Canty, M.J.; Nielsen, A.A. Automatic radiometric normalization of multitemporal satellite imagery with the
iteratively re-weighted MAD transformation. Remote Sens. Environ. 2008,112, 1025–1036. [CrossRef]
32.
Canty, M.J. Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL
and Python; CRC Press: Boca Raton, FL, USA, 2014.
33.
Martinuzzi, S.; Gould, W.A.; González, O.M.R. Creating Cloud-Free Landsat ETM Data Sets in Tropical
Landscapes: Cloud and cloud-Shadow Removal; US Department of Agriculture, Forest Service, International
Institute of Tropical Forestry Río Piedras: San Juan, Puerto Rico, 2007.
34. Haralick, R.M. Statistical and structural approaches to texture. Proc. IEEE 1979,67, 786–804. [CrossRef]

Remote Sens. 2016,8, 593 17 of 17
35.
Gallant, J.C.; Dowling, T.I. A multiresolution index of valley bottom flatness for mapping depositional areas.
Water Resour. Res. 2003,39. [CrossRef]
36. Breiman, L. Random forests. Mach. Learn. 2001,45, 5–32. [CrossRef]
37. Liaw, A.; Wiener, M. Classification and regression by randomForest. R News 2002,2/3, 18–22.
38.
Horning, N. Introduction to Decision Trees and Random Forests; American Museum of Natural History’s Center
for Biodiversity and Conservation: New York, NY, USA, 2013.
39.
Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.;
Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-resolution global maps of 21st-century forest cover
change. Science 2013,342, 850–853. [CrossRef] [PubMed]
40.
Olofsson, P.; Foody, G.M.; Herold, M.; Stehman, S.V.; Woodcock, C.E.; Wulder, M.A. Good practices for
estimating area and assessing accuracy of land change. Remote Sens. Environ. 2014,148, 42–57. [CrossRef]
41. Quinlan, J.R. C4. 5: Programs for Machine Learning; Morgan Kaufmann: San Mateo, CA, USA, 1993.
42.
Sesnie, S.E.; Gessler, P.E.; Finegan, B.; Thessler, S. Integrating Landsat TM and SRTM-DEM derived variables
with decision trees for habitat classification and change detection in complex neotropical environments.
Remote Sens. Environ. 2008,112, 2145–2159. [CrossRef]
43.
Algeet-Abarquero, N.; Sánchez-Azofeifa, A.; Bonatti, J.; Marchamalo, M. Land cover dynamics in Osa Region,
Costa Rica: Secondary forest is here to stay. Reg. Environ. Chang. 2014. [CrossRef]
44.
Pal, M. Random forest classifier for remote sensing classification. Int. J. Remote Sens.
2005
,26, 217–222.
[CrossRef]
45.
Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random forests for land cover classification. Pattern Recog. Lett.
2006,27, 294–300. [CrossRef]
46.
Waske, B.; Braun, M. Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS J.
Photogramm. Remote Sens. 2009,64, 450–457. [CrossRef]
47.
Ghimire, B.; Rogan, J.; Galiano, V.R.; Panday, P.; Neeti, N. An evaluation of bagging, boosting, and random
forests for land-cover classification in Cape Cod, Massachusetts, USA. GiSci. Remote Sens.
2012
,49, 623–643.
[CrossRef]
48.
Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the
effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens.
2012,67, 93–104. [CrossRef]
49. Fahsi, A.; Tsegaye, T.; Tadesse, W.; Coleman, T. Incorporation of digital elevation models with Landsat-TM
data to improve land cover classification accuracy. For. Ecol. Manag. 2000,128, 57–64. [CrossRef]
50.
Dewan, A.M.; Yamaguchi, Y. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote
sensing to promote sustainable urbanization. Appl. Geogr. 2009,29, 390–401. [CrossRef]
51. Holdridge, L.R. Life Zone Ecology; Tropical Science Center: San Jose, Costa Rica, 1967.
52.
Shimada, M.; Itoh, T.; Motooka, T.; Watanabe, M.; Shiraishi, T.; Thapa, R.; Lucas, R. New global
forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens. Environ.
2014
,155, 13–31.
[CrossRef]
53.
Arroyo-Mora, J.P.; Sánchez-Azofeifa, G.A.; Rivard, B.; Calvo, J.C.; Janzen, D.H. Dynamics in landscape
structure and composition for the Chorotega region, Costa Rica from 1960 to 2000. Agric. Ecosyst. Environ.
2005,106, 27–39. [CrossRef]
54.
Broadbent, E.N.; Zambrano, A.M.A.; Dirzo, R.; Durham, W.H.; Driscoll, L.; Gallagher, P.; Salters, R.; Schultz, J.;
Colmenares, A.; Randolph, S.G. The effect of land use change and ecotourism on biodiversity: A case study
of Manuel Antonio, Costa Rica, from 1985 to 2008. Landsc. Ecol. 2012,27, 731–744. [CrossRef]
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