Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series

Highlights

• Robust data-driven method to track complex forest change processes

• Small-scale forest disturbances detected using NDVI time series

• Demonstration of BFAST Monitor algorithm on irregular time series data

• First forest change study in Ethiopian montane forests at high temporal resolution

• Potential of detecting degradation using change magnitude is shown.

Abstract

Remote sensing data play an important role in the monitoring of forest changes. Methods are needed to provide objective estimates of forest loss to support monitoring efforts at various scales, and with increasing public availability of remote sensing data, accurate deforestation measurements at high temporal resolution are becoming more realistic. While several time series based methods have recently been described in the literature, there are few studies focusing on tropical forest areas, where low data availability and complex change processes present challenges to forest disturbance monitoring. Here, we present a robust data-driven method to track tropical deforestation and degradation based on Landsat time series data. Based on the previously reported Breaks For Additive Season and Trend Monitor (BFAST Monitor) method (Verbesselt etal., 2012), we show that BFAST Monitor, when applied to Landsat NDVI time series data using sequentially defined monitoring periods, can be used to track small-scale forest disturbances annually in an Afromontane forest system in southern Ethiopia. Using an ordinal logistic regression (OLR) approach, change magnitude, calculated based on differences between observed and expected values in a monitoring period, was found to be an essential predictor variable for disturbances. After applying a NDVI change magnitude threshold of − 0.065, overall accuracy was estimated to be 78%, and both producer's and user's accuracy of the disturbance class were estimated to be 73%. The method and results presented here are relevant to tropical countries engaged in REDD + for whom data availability and complex forest change dynamics limit the ability to reliably track forest disturbances over time.

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).