Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series
Introduction
With deforestation in the tropics accounting for upwards of 20% of global CO2 emissions (Gullison etal., 2007), mitigation efforts against global climate change must include considerations to reduce tropical deforestation and forest degradation. To this end, international climate negotiations include the development of a mechanism aimed at the “Reduction of Emissions from deforestation and degradation and considerations for conservation, enhancement of carbon stocks”, commonly known as REDD+. For a results-based mechanism such as REDD + to be successful, countries are required to establish robust Measuring, Reporting and Verification (MRV) systems with which to report forest changes and impact of REDD + activities.
A key component of a REDD+ MRV is the assessment of activity data— the area of forest undergoing change processes, including deforestation, forest degradation, and forest regrowth (Penman etal., 2003). To support REDD+ MRV and other efforts to conserve tropical forest resources, participating countries need to establish robust forest monitoring systems to track activity data at regular time-frames (Holmgren & Marklund, 2007). Remote sensing based approaches play a key role in forest monitoring, as they provide the best opportunity for mapping forest area change over large areas (De Sy etal., 2012, DeVries and Herold, 2013, Herold and Johns, 2007, Sanz-sanchez and Penman, 2013). To date, only few remote sensing based forest monitoring systems exist in tropical countries, the most advanced of which are the PRODES and DETER systems of the Brazilian Space Agency (INPE), used for annual deforestation mapping and near real-time deforestation monitoring, respectively (INPE, 2014a, INPE, 2014b). Considerable advancements in monitoring capacities are needed for other tropical countries to establish similar forest monitoring systems (Romijn, Herold, Kooistra, Murdiyarso, & Verchot, 2012).
To track forest change over time, most change detection methods rely on the selection of imagery from key points in time, which necessitates the selection of appropriate imagery from the archive from which to derive change information. These bi-temporal change detection methods range from simple image differencing methods (Coppin, Jonckheere, Nackaerts, Muys, & Lambin, 2004) to statistically-based methods such as the Multivariate Alteration Detection method (Nielsen, Conradsen, & Simpson, 1998). An important constraint in the selection of imagery for such change detection methods is the loss of data due to a number of contaminations or errors. First, where bi-temporal change detection methods require that the source imagery be cloud-free for a gap-free change product, cloud cover presents a key constraint (Ju & Roy, 2008), especially in the tropics where cloud cover is frequently high (Mitchard etal., 2011). Second, other sensor-specific sources of data loss can present significant constraints to the selection of imagery for detecting change. Notably, the scan-line corrector (SLC) on board the Landsat 7 Enhanced Thematic Mapper (ETM +) failed in March 2003, resulting in the loss of approximately 22% of data from each scene (Zhang, Li, & Travis, 2007). While methods exist to fill these gaps with data derived from other scenes or even other sensors (Chen etal., 2011, Zhu, Liu and Chen, 2012a), introduction of extraneous data (e.g., from other images) into the data processing chain can introduce additional errors into the processing chain (Alexandridis etal., 2013, Bédard etal., 2008). Introducing gap-filling or other data fusion methods into the preprocessing chain for bi-temporal change detection approaches can also introduce uncertainties related to the actual acquisition date of the source data, which can have implications on quantitative estimates of forest change (Pelletier, Ramankutty, & Potvin, 2011).
Another potential drawback of using a bi-temporal change detection approach relates to the dynamic behaviour of vegetation over time. Basing change estimates on differencing between images at only two points in time risks interpreting natural phenological change as actual land cover change (Verbesselt etal., 2010, Zhu, Woodcock and Olofsson, 2012b). This problem is especially pronounced in tropical regions, where frequent cloud cover can severely limit the choice of imagery available per year, sometimes necessitating the use of non-anniversary imagery in change detection studies. Confusion between forest and non-forest spectral signatures can arise as a result of imagery from different seasons, which can lead to increased errors in the change classification result (Coppin etal., 2004).
With the opening of the U.S. Geological Service (USGS) Landsat data archive, large amounts of medium-resolution optical earth observation data have been made freely available to the public, which combined with continued advances in the field of cloud computing for geo-spatial data (Evangelidis etal., 2014, Lee and Kang, 2013) has allowed for high temporal resolution forest change monitoring at unprecedented spatial scales (Hansen etal., 2013). Similar developments in multi-temporal satellite image analysis have been previously realized in the case of coarse resolution datasets, including AVHRR (Cihlar etal., 2004, Cihlar etal., 1997, Pinzon and Tucker, 2014, Tucker etal., 2005) and MODIS (de Jong etal., 2013, Roerink etal., 2003, Roerink etal., 2000, Verbesselt etal., 2010) time series data, based on their high return rates and rich historical archives. A number of temporal trajectory methods based on Landsat time series data have been developed in recent years to make more extensive use of the temporal domain. Some methods construct regular (e.g., annual) image composites to understand disturbance-recovery dynamics (Huang etal., 2010, Kennedy etal., 2010), while others use all available data to allow for considerations of more complex change dynamics, such as phenology (Zhu, Woodcock, etal., 2012) and transient forest changes (Broich etal., 2011). Despite the number of temporal trajectory change detection approaches recently published in the literature, many of these methods have been developed in temperate forests with relatively high data availability (Huang etal., 2010, Kennedy etal., 2010, Zhu and Woodcock, 2014, Zhu, Woodcock and Olofsson, 2012b). There are relatively few studies demonstrating these methods in tropical areas with lower data availability due to persistent cloud cover (Duveiller etal., 2008, Ernst etal., 2013, Mitchard etal., 2011) or excessive gaps in the Landsat archive (Broich etal., 2011).
While the monitoring of tropical deforestation at large spatial scales has been well documented and is largely operational (Achard etal., 2010), methods able to track small-scale deforestation at high temporal resolution are currently lacking. Small-scale deforestation driven by small-holder subsistence agriculture is a prominent forest change process found in sub-Saharan African countries (Fisher, 2010, Joseph etal., 2013, Potapov etal., 2012). Monitoring these small changes is essential for such countries to play a role in climate change mitigation and to implement forest protection measures. Much of the research done on deforestation in tropical Africa has been undertaken in central Africa, where small-scale forest changes have been mapped using multi-temporal segmentation (Duveiller etal., 2008, Ernst etal., 2013), classification of annual Landsat time series (Hirschmugl, Steinegger, Gallaun, & Schardt, 2014), or analysis of all available Landsat ETM + data (Potapov etal., 2012). Persistent small-scale changes in these landscapes (usually related to small-holder agricultural expansion) are a major constraint to the accurate mapping and accounting of deforestation (Tyukavina etal., 2013).
In this paper, we describe a robust and novel approach to monitoring forest disturbance in the tropics using Landsat time series data using the Breaks For Additive Season and Trend (BFAST) Monitor method (Verbesselt, Zeileis, & Herold, 2012). Recent work has been done to demonstrate this algorithm on Landsat times series for tropical forest monitoring using fused Landsat-SAR time series (Reiche, Verbesselt, Hoekman, & Herold, 2015) or Landsat-MODIS time series (Dutrieux, Verbesselt, Kooistra, & Herold, in review). The goal of this study was to investigate the suitability of the BFAST Monitor method to detect forest disturbances using Landsat time series data over a tropical montane forest system in southwestern Ethiopia. To this end, we addressed two objectives: (i)to test the method in an area with lower data density typical of tropical montane forest systems experiencing regular cloud cover; and (ii) to develop an approach to track small-scale forest disturbances characteristic of changes driven by small-holder agriculture expansion in the tropics. The monitoring approach demonstrated in this study and described in this paper can serve a number of purposes, including acting as a key component in REDD + monitoring systems (Sanz-sanchez & Penman, 2013).
Section snippets
Geographic and biophysical characteristics
This study was carried out in the UNESCO Kafa Biosphere Reserve (http://www.kafa-biosphere.org), located in the Afromontane forests in Southern Nations Nationalities and People's Region (SNNPR) state of southern Ethiopia. Due to availability of very high resolution (VHR) reference imagery, we focused our research on a subset of the Biosphere Reserve, bound by 7.22°E to 7.84°E and 35.59°N to 37.17°N (Fig. 1). The Biosphere is comprised of three zones related to forest management: core (protected
Data acquisition and preprocessing
An overview of the methods used in this study is shown in Fig. 2. We downloaded all available Landsat ETM + with WRS-2 coordinates p170r55at processing level L1T and cloud cover below 70% from the USGS Glovis repository (http://glovis.usgs.gov). On average, 9 ETM + scenes were available per year until 2011, with fewer scenes in 1999, 2000 and 2008 in particular (Table 1). To convert raw imagery from digital number (DN) to Top of Atmosphere (ToA) Reflectance and Surface Reflectance (SR), we used
Robustness of the MOSUMbreakpoint test
The MOSUM breakpoint test proved effective in discriminating deforestation events from stable forest trajectories in the presence of noise and data gaps in the time series data. Discrete deforestation events (Fig. 5B) resulted in a sudden and persistent decrease in NDVI, whereas stable forests were largely free of breakpoints (Fig. 5A). Forests undergoing degradation intense enough to cause canopy openings also led to breakpoint detection (Fig. 5C), even though some degree of seasonality due to
Forest change in the Ethiopian Afromontane forests
In this paper we present the first detailed study on forest disturbances in the Ethiopia Afromontane forests, in which we estimate total forest loss of roughly 11,000 ha between 2005 to 2012 in the UNESCO Kafa Biosphere Reserve, representing approximately 3% of the total forest area. This deforestation rate corresponds to a loss of roughly 0.4% of forest land per year, which is comparable to change rates estimated by Getahun etal. (2013) for a neighbouring Afromontane forest area (0.19% between
Conclusions
In this paper, we show that the BFAST Monitor method (Verbesselt etal., 2012) for breakpoint detection in time series is applicable to Landsat ETM + time series data for forest disturbance monitoring for an area in southwestern Ethiopia characterized by highly fragmented Afromontane forests. Using high resolution time series imagery as reference data, we estimated an overall accuracy of 78%, with associated user's and producer's accuracy of 73% for disturbances. Our results show that magnitude
Acknowledgments
Funding for the work of Ben DeVries was provided in the frame of the project “Climate protection and preservation of primary forests— A management model using the wild coffee forests in Ethiopia as an example”, implemented by the Nature and Biodiversity Conservation Union (NABU), German and Ethiopian branches, with funding from the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMU) (IKI-1 project
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