About 44% of global forests are concentrated in the tropics (1,770,156 thousand ha in 2015 [1
]), and is also where the vast majority of forest loss occurs, with reported rates of loss of 6.4 M ha year−1
between 2010 and 2015 [1
]. In Colombia, approximately one third of forest cover has been cleared since the year 1700, as a result of multiple, heterogeneous historical processes [2
]. At the beginning of the 20th century the agricultural footprint rapidly increased due to population growth; cattle ranching played an especially important role in landscape change dynamics within the country [2
]. Currently, ranching represents one of the key economic subsectors in Colombia, contributing to approximately 3.5% of the overall Gross Domestic Product (GDP) and 27% of the agricultural and livestock GDP [3
]. Cattle ranching exploited more than 38 million hectares over the last 50 years, holding approximately 23.5 million heads, supporting 7% and 28% of national and rural employment, respectively.
Information related to forest trends are critical to different actors involved in the decision-making of policies and investments promoting the conservation of forests and their ecosystem services. Globally, several efforts have been put in place to develop consistent and robust methodologies to assess forest extension and change [4
]. As a response to the rapid advance of global forest loss and degradation, the UN Framework Convention on Climate Change (UNFCCC) launched the Reducing Emissions from Deforestation and Forest Degradation program (REDD+). The general aim of REDD+ is to contribute to the mitigation of climate change by reducing greenhouse gas (GHG) emissions by decreasing and reversing forest loss and degradation, and by increasing the removal of GHGs through conservation and the expansion of forests [11
]. In 2008, the national government of Colombia in collaboration with UN launched the UN-REDD program in Colombia; since then, multiple collaboration initiatives, promoted especially by NGOs and multilateral organizations, implemented environmental programs based on the REDD+ approach that presented the Readiness Preparation Proposal for Colombia in 2013.
Dominating the forestry-based climate mitigation programs in Latin America and the Caribbean [12
], REDD+ programs face multiple challenges for their operational implementation and the achievement of multiple goals involving climate change, biodiversity conservation, and sustainable development [13
]. For effective implementation and assessment of such programs it is often necessary to obtain systematic Earth Observation-based forest data, together with specific methods and protocols integrating ground truth, geospatial information, and capacity building to ensure the project’s monitoring, reporting, and verification (MRV) [14
]. Consequently, several international partnerships, like the Global Forest Information Initiative (GFOI), have been established to provide national forest monitoring systems with guidelines to exploit Earth observation data for REDD+, in order to foster robust, reliable, and achievable forest monitoring and assessment [16
The Kyoto & Carbon (K&C) initiative, an international collaborative project led by the JAXA Earth Observation Research Center (EORC), was designed to contribute data and information to the UNFCCC Kyoto Protocol and international actors for the development of a Terrestrial Carbon Observing system, together with giving continuation to the previous initiatives, such as the Global Rain Forest Mapping (GRFM) and the Global Boreal Observing Satellite (GBFM) [17
]. The K&C research is based on the Advanced Land Observing Satellites (ALOS). ALOS carries on board the active sensor Phased Array L-band SAR (PALSAR). ALOS PALSAR is considered a pathfinder global monitoring mission due the improvement of sensor performance and its systematic data-observation strategy, providing reliable wall-to-wall observations at fine resolution with consistency in spatial and temporal resolutions [18
]. This ensures land observation acquisition through multiple missions, i.e., ALOS-1 (2006–2011) and ALOS-2 (2014–present). ALOS PALSAR information has been extensively used in forest applications, such as forest mapping [9
], deforestation monitoring [20
], aboveground biomass estimation [22
], and mangrove monitoring [23
]. In addition to the advantage of cloud-free imagery provided by the SAR sensors, ALOS L-band provides key information related to forest canopy and surface features [24
]. With the ability to penetrate vegetation canopy, ALOS PALSAR L-band sensors, compared to other SAR instruments (e.g., C-band based), are more sensitive to trees’ aboveground structural characteristics, providing very suitable Earth Observations data for forest monitoring [4
] with a systematic acquisition strategy.
Recent small-scale deforestation patterns found in Amazonian countries [25
] have been found to be increasingly related to land cover conversion from small landowners, e.g., Brasil [26
]. Current methodological and technical advances in remote sensing [19
] allow the inclusion of robust small-scale deforestation detection in the assessment phase of deforestation monitoring programs. Previous projects exploit ALOS PALSAR data to quantify deforestation patterns at small-scale farm level to detect deforestation events below the hectare [27
], or use Landsat data to estimate deforestation with a minimum area detection of 6.25 ha [28
] to assess farmers’ ‘no deforestation’ compliance agreements.
In this work we exploit ALOS PALSAR data to assess zero deforestation agreements of the Colombian Mainstream Sustainable Cattle Ranching project (MSCR) in five different regions of Colombia. The MSCR aims are to improve the ecosystem functioning of degraded pastures lands through the implementation of sustainable silvopastoral practices, contributing to national goals to reduce the total cattle ranching land, contribute to climate change mitigation, as well as to generate socioeconomic benefits. The MSCR project integrates small holder cattle ranching farmers in a payment for the environmental services scheme (PES), where farmers have compromised through the signing of a contract to a zero-deforestation agreement inside the farms during the project’s life. Farmers receive materials, technical assistance, and PES associated with the establishment of silvopastoral systems and the restoration/conservation of areas that include forest. During the technical assistance phase project’s staff monitored the fulfillment of the zero-deforestation agreements by field inspection of the farm’s forest areas. The integration of a further assessment based on remote sensing imagery provides the required independent key performance indicator of estimation of deforestation extent at the project level, complementing field deforestation monitoring at the individual farm level. The MSCR project is supported by several international institutions including the Global Environment Facility (GEF), the UK’s Department of Energy and Climate Change (DECC), the World Bank (financial support and supervision), national Colombian agencies like the Center for Research in Sustainable Systems of Agricultural Production (CIPAV), the National Federation of Cattle Ranching, (FEDEGAN), the Action Fund for Environment and Children, and The Nature Conservancy.
Specific research objectives of this work are:
To develop an ALOS PALSAR processing workflow to classify forest and generate forest change products at local scales.
To assess zero deforestation agreements implementation in 2615 farms participating to the Colombian Mainstream Sustainable Cattle Ranching project by exploiting ALOS PALSAR forest-change products.
Forest and forest change products generated using ALOS PALSAR showed satisfactory overall accuracy (OA) values for all the regions analyzed (Table 4
), presenting no significant differences among regions. High overall accuracy can be partially explained by the high accuracies obtained in the nonforest class, which is the class with largest area proportion (82%) compared to the forest and deforestation classes. This is a well-known limitation of this accuracy parameter.
Regions with highly complex topography (Boyacá-Santander and partially Cesar River Valley) presented lower accuracy levels for the forest and deforestation classes, respectively. Our results suggest that local slope orientation present in mountains with respect to the incidence angle of SAR sensors had a relevant effect on image distortion [49
] or hampered the surface visibility by the sensor [50
]. Here the integration with optical sensor information was necessary to reduce the misclassified areas. Higher accuracy performance was obtained in the Meta foothills region, which is characterized mostly by flat areas compared with mountain regions (i.e., Boyacá-Santander and Coffee Ecoregion). The Cesar river Valley region showed lower classification accuracy values, especially user’s accuracy, indicating an overestimation in the deforestation extension.
Deforestation products were characterized by misclassification errors especially within regions with dry conditions (Cesar River Valley and Bajo Magdalena), compared to the humid regions (Coffee ecoregion, Boyacá-Santander, and Meta foothills). During forest classification, detection of dry forests was more challenging, due to both (i) the high variation in vegetation structure observed in the ALOS PALSAR Fine Beam Dual imagery, and (ii) the seasonal deciduous behavior (phenology), well-observed using Landsat optical imagery. Our results suggest that dry forests mapping could need comprehensive ground truth data surveys to integrate the remote sensing-based mapping workflow. This is especially necessary for Colombia, where limited detailed and spatially explicit information is available for this forest type [30
The low proportion of forests in the project’s farms reflects historical processes of deforestation, prior to the implementation of the MSCR project. The study regions characterized by greater proportion of forest cover within farms are located in mountainous or partially mountainous areas, possibly due to the limited accessibility [52
], while those with less forest cover proportion are associated with dry regions which are historically characterized by high agricultural pressure [30
In the Meta foothills region, zero hectares of deforestation detected between 2010 and 2016 (Table 3
) advocates the fulfillment of zero deforestation. For the flat Bajo Magdalena region, deforestation estimate is 1.6 ha and with low variation due to uncertainties (Table 2
), while in the mountain region of Boyacá-Santander, the deforestation estimated area (3.7 ha) is overestimated based on relatively low UA accuracy results. This was somehow expected due to the presence of steep slopes characteristic of the region, coupled with the lower efficiency of radar sensing in these topographic conditions [52
In the case of the Cesar River Valley region, where major deforestation areas were detected and the lowest UA calculated, a postvalidation visual assessment was performed, revealing that the largest deforested area corresponds to a single event of approximately 33 ha; for this single event the area of the corresponding farm was estimated using the circular buffer method, so the direct attribution of noncompliance had an additional uncertainty due to the simplified geometric protocol used.
Nevertheless, other multi-temporal postclassification studies reported change classes as showing generally higher commission errors compared to errors associated to stable classes (forest/nonforest) [48
]. Multiple research has discussed general minimum accuracy standards for remote sensing-based thematic mapping [57
], although there is no global consensus for thematic accuracy of deforestation products. We stress that projects associated with zero deforestation agreements monitoring and assessment should always report omission and commission accuracy indices of each thematic class coupled with error-adjusted areas and their confidence intervals. Minimum accuracies should be included in the project specific requirements, together with additional procedures to corroborate agreements compliance/noncompliance, e.g., field corroboration of change detection procedures based on remote sensing analysis.
The application of ALOS PALSAR FBD imagery was found to provide significant and consistent information for the detection of forest and nonforest cover; the results were especially relevant for highly clouded tropical conditions [4
]. Nonetheless, we found that its application in mountainous areas presents some limitations, since the signal of ALOS PALSAR resulted rather affected by the topography characteristics of some regions. The integration of highly detailed digital elevation models, dense temporal image series and improved preprocessing routines should be used to generate more accurate products for forest, nonforest, and deforestation detection and quantification in these specific conditions. The integration of optical sensors information improved the detection of forests and deforestation in topographic areas characterized by steep slopes, however, at the cost of the time of processing. Operational integration of Synthetic Aperture Radar imagery with optical imagery can significantly improve the consistency and robustness of forest monitoring in the tropics [58
], to achieve efficient forest monitoring procedures for interoperability and classifications needs.