Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts

Fire use for land management is widespread in natural tropical and plantation forests, causing major environmental and economic damage. Recent studies combining active fire alerts with annual forest-cover loss information identified fire-related forest-cover loss areas well, but do not provide detailed understanding on how fires and forest-cover loss are temporally related. Here, we combine Sentinel-1-based, near real-time forest cover information with Visible Infrared Imaging Radiometer Suite (VIIRS) active fire alerts, and for the first time, characterize the temporal relationship between fires and tropical forest-cover loss at high temporal detail and medium spatial scale. We quantify fire-related forest-cover loss and separate fires that predate, coincide with, and postdate forest-cover loss. For the Province of Riau, Indonesia, dense Sentinel-1 C-band Synthetic Aperture Radar data with guaranteed observations of at least every 12 days allowed for confident and timely forest-cover-loss detection in natural and plantation forest with user’s and producer’s accuracy above 95%. Forest-cover loss was detected and confirmed within 22 days in natural forest and within 15 days in plantation forest. This difference can primarily be related to different change processes and dynamics in natural and plantation forest. For the period between 1 January 2016 and 30 June 2017, fire-related forest-cover loss accounted for about one third of the natural forest-cover loss, while in plantation forest, less than ten percent of the forest-cover loss was fire-related. We found clear spatial patterns of fires predating, coinciding with, or postdating forest-cover loss. Only the minority of fires in natural and plantation forest temporally coincided with forest-cover loss (13% and 16%) and can thus be confidently attributed as direct cause of forest-cover loss. The majority of the fires predated (64% and 58%) or postdated forest-cover loss (23% and 26%), and should be attributed to other key land management practices. Detailed and timely information on how fires and forest cover loss are temporally related can support tropical forest management, policy development, and law enforcement to reduce unsustainable and illegal fire use in the tropics

Space-time monitoring of tropical forest changes using observations from multiple satellites

Forests provide essential goods and services to humanity, but human-induced forest disturbances have been on ongoing at alarming rates, undermining the capacity for forests to continue providing essential goods and services. In recent years, the understanding of the short-term and long-term impacts of deforesting and degrading forest ecosystems has improved, and global efforts to reduce forest loss are ongoing. However, in many parts of the globe, significant forest areas continue to be lost. To fully protect forest ecosystems efficiently, timely, reliable and location-specific information on new forest disturbances is needed. Frequent and large-area forest mapping and monitoring using satellite observations can provide timely and cost-effective information about new forest disturbances. However, there are still key weaknesses associated with existing forest monitoring systems. For example, the capacity for forest monitoring systems to detect new disturbances accurately and timely is often limited by persistent cloud cover and strong seasonal dynamics. Persistent cloud can be addressed by using observations from multiple satellite sensors, but satellite sensors often have inter-sensor differences which make integration of observations from multiple sensors challenging. Seasonality can be accounted for using a seasonal model, but image time series are often acquired at irregular intervals, making it difficult to properly account for seasonality. Furthermore, with existing forest monitoring systems, detecting subtle, low-magnitude disturbances remains challenging, and timely detection of forest disturbances is often accompanied by many false detections. The overall objective of this thesis is to improve forest change monitoring by addressing the key challenges which hinders accurate and timely detection of forest disturbances from satellite data. In the next paragraphs, I summarise how this thesis tackled some of the key challenges which hamper effective monitoring of forest disturbances using satellite observations. Chapter 2 addresses the challenge of seasonality by developing a spatial normalisation approach that allows us to account for seasonality in irregular image time series when monitoring forest disturbances. In this chapter, I showed that reducing seasonality in image time series using spatial normalisation leads to timely detection of forest disturbances when compared to a seasonal model approach. With spatial normalisation, near real-time forest monitoring in dry forests, which has been challenging for many years, is now possible. Applying spatial normalisation in areas where evergreen and deciduous forests co-exist is however challenging. Therefore, further research is needed to improve the spatial normalisation approach to ensure that it is applicable to areas with a combination of different forest types. In particular, a spatial normalisation approach which is forest type-specifics is desirable. In this chapter, forest disturbances were detected by analysing single pixel-time series. Spatial information was only used to reduce seasonality. Taking in account the fact that forest disturbances are spatio-temporal events, I investigated whether there is an added-value of combining both spatial and temporal information when monitoring forest disturbances from satellite image time series. To do this, I first developed a space-time change detection method that detects forest disturbances as extreme events in satellite data cubes (Chapter 3). I showed that, by combining spatial and temporal information, forest disturbances can still be detected reliably even with limited historical observations. Therefore, unlike approaches which detect forest disturbances by analysing single pixel- time series, the space-time approach does not require huge amount of historical images to be pre-processed when monitoring forest disturbances. I then evaluated the added-value of using space-time features when confirming forest disturbances (Chapter 4). I showed that using a set of space-time features to confirm forest disturbances enhance forest monitoring significantly by reducing false detections without compromising temporal accuracy. With space-time features, the discrimination of forest disturbances from false detections is no longer based on temporal information only, hence providing opportunity to also detect low-magnitude disturbances with high confidence. Based on the analysis for conditional variable importance, I showed that features which are computed using both spatial and temporal information were the most important predictors of forest disturbances, thus enforcing the view that forest disturbances should be treated as spatio-temporal in order to improve forest change monitoring. In Chapter 2 – 4, forest disturbances where detected from medium resolution Landsat time series. Yet, recent studies showed that small-scale forest disturbances are often omitted when using Landsat time series. In Chapter 5, I investigated whether detection of small-scale forest disturbances can be improved by using the 10m resolution time series from recently launched Sentinel-2 sensor. I also investigated whether the spatial normalisation approach developed in Chapter 2 can be used to reduce inter-sensor differences in multi-sensor optical time series. I showed that the 10m resolution Sentinel-2 time series improves the detection of small-scale forest disturbances when compared to 30m resolution. However, the 10m resolution does not supersede the importance of frequent satellite observations when monitoring forest disturbances. I also showed that spatial normalisation approach developed in Chapter 2 can reduce inter-sensor differences in multi-sensor optical time series significantly to generate temporally consistent time series suitable for forest change detection. Spatial normalisation does not completely remove inter-sensor differences, but the differences are significantly reduced. Monitoring of forest disturbances is increasingly done using a combination of Synthetic Aperture Radar (SAR) and optical time series. Therefore, Chapter 6 investigated whether the spatial normalisation approach developed in Chapter 2 can also reduce seasonal variations in SAR time series to facilitate the integration of SAR-optical time series for forest monitoring in dry tropical forests. This Chapter demonstrated that seasonal variations in SAR time series can also be reduced through spatial normalisation. As a result, observations from SAR and optical time series were combined to improve near real-time forest change detection in dry tropical forest. In Chapter 7, it is demonstrated that spatial normalisation has potential to also reduce inter-sensor differences in SAR-optical time series, resulting into temporally consistent SAR-optical time series. In conclusion, this thesis developed a space-time forest monitoring framework that addresses some key challenges affecting satellite-based forest monitoring. In particular, new methods that allow for timely and accurate detection of forest disturbances using observations from multiple satellites were developed. Overall, the methods developed in this research contribute to our capacity to accurately and timely detect forest disturbances in both dry and humid forests.</p

Trends in Spring Phenology of Western European Deciduous Forests

Plant phenology is changing because of recent global warming, and this change may precipitate changes in animal distribution (e.g., pests), alter the synchronization between species, and have feedback effects on the climate system through the alteration of biogeochemical and physical processes of vegetated land surface. Here, ground observations (leaf unfolding/first leaf separation of six deciduous tree species) and satellite-derived start-of-growing season (SOS) are used to assess how the timing of leafing/SOS in Western European deciduous forest responded to climate variability between 2001 and 2011 and evaluate the reliability of satellite SOS estimates in tracking the response of forest leafing to climate variability in this area. Satellite SOS estimates are derived from the Normalized Difference Vegetation Index (NDVI) time series of the Moderate Resolution Imaging Spectroradiometer (MODIS). Temporal trends in the SOS are quantified using linear regression, expressing SOS as a function of time. We demonstrated that the growing season was starting earlier between 2001 and 2011 for the majority of temperate deciduous forests in Western Europe, possibly influenced by regional spring warming effects experienced during the same period. A significant shift of up to 3 weeks to early leafing was found in both ground observations and satellite SOS estimates. We also show that the magnitude and trajectory of shifts in satellite SOS estimates are well comparable to that of in situ observations, hence highlighting the importance of satellite imagery in monitoring leaf phenology under a changing climate

Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2

Combining observations from multiple optical and synthetic aperture radar (SAR) satellites can provide temporally dense and regular information at medium resolution scale, independently of weather, season, and location. This has the potential to improve near real-time deforestation monitoring in dry tropical regions, where traditional optical only monitoring systems typically suffer from limited data availability due to persistent cloud cover. In this context, the recently launched Sentinel-1 satellites promise unprecedented potential, because for the first time dense and regular SAR observations are free and openly available. We demonstrate multi-sensor near real-time deforestation detection in tropical dry forests, through the combination of Sentinel-1 C-band SAR time series with ALSO-2 PALSAR-2 L-band SAR, and Landsat-7/ETM+ and 8/OLI. We used spatial normalisation to reduce the dry forest seasonality in the optical and SAR time series, and combined them within a probabilistic approach to detect deforestation in near real-time. Our results for a dry tropical forest site in Bolivia, showed that, as a result of high observation availability of Sentinel-1, deforestation events were detected more timely with Sentinel-1 than compared to Landsat and ALOS-2 PALSAR-2. The spatial and temporal accuracies of the multi-sensor approach were higher than the single-sensor results. We improved the precision of the reference data derived from the multi-sensor satellite time series, which enabled a more robust estimation of the temporal accuracy. We quantified how the near real-time deforestation detection is associated with a trade-off between the confidence in detection and the temporal accuracy. We showed that the trade-off affects the choice on how to use the near-real time data for different applications such as fast alerting with high temporal accuracy but lower confidence versus accurate detection at lower temporal detail. When aiming for a high confidence in change area estimates for example, deforestation was detected with a user's accuracy of 88%, a producer's accuracy of 89% (low area bias), and a mean time lag of 31 days using all sensors. This is on average 7 days earlier than when using only Sentinel-1 observations, and six weeks earlier than when relying only on Landsat observations. We showed that confident near real-time deforestation alerts can be provided with a mean time lag of 22 days, but these are associated with a higher commission error. With more dense time series data expected from the Sentinel-1 and -2 sensors for the upcoming decade, spatial and temporal detection accuracy of multi-sensor deforestation monitoring in the tropics will improve further

Monitoring Deforestation at Sub-Annual Scales as Extreme Events in Landsat Data Cubes

Current methods for monitoring deforestation from satellite data at sub-annual scales require pixel time series to have many historical observations in the reference period to model normal forest dynamics before detecting deforestation. However, in some areas, pixel time series often do not have many historical observations. Detecting deforestation at a pixel with scarce historical observations can be improved by complementing the pixel time series with spatial context information. In this work, we propose a data-driven space-time change detection method that detects deforestation events at sub-annual scales in data cubes of satellite image time series. First we spatially normalised observations in the local space-time data cube to reduce seasonality. Subsequently, we detected deforestation by assessing whether a newly acquired observation in the monitoring period is an extreme when compared against spatially normalised values in a local space-time data cube defined over reference period. We demonstrated our method at two sites, a dry tropical Bolivian forest and a humid tropical Brazilian forest, by varying the spatial and temporal extent of data cube. We emulated a “near real-time” monitoring scenario, implying that observations in the monitoring period were sequentially rather than simultaneously assessed for deforestation. Using Landsat normalised difference vegetation index (NDVI) time series, we achieved a median temporal detection delay of less than three observations, a producer’s accuracy above 70%, a user’s accuracy above 65%, and an overall accuracy above 80% at both sites, even when the reference period of the data cube only contained one year of data. Our results also show that large percentile thresholds (e.g., 5th percentile) achieve higher producer’s accuracy and shorter temporal detection delay, whereas smaller percentiles (e.g., 0.1 percentile) achieve higher user’s accuracy, but longer temporal detection delay. The method is data-driven, not based on statistical assumption on the data distribution, and can be applied on different forest types. However, it may face challenges in mixed forests where, for example, deciduous and evergreen forests coexist within short distances. A pixel to be assessed for deforestation should have a minimum of three temporal observations, the first of which must be known to represent forest. Such short time series allow rapid deployment of newly launched sensors (e.g., Sentinel-2) for detecting deforestation events at sub-annual scales

Spatio-temporal characterization of surface water dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021)

Socio-economic damages caused by extreme floods have been increasing rapidly in recent years, mainly driven by changes in the climate and modulated by increasing human population in deltic areas and floodplains. The Cuvelai-Etosha Basin (CEB) in southern Africa, covering southern Angola and northern Namibia, experiences socially and economically devastating extreme floods. Yet, accurate information on past and current surface water changes and dynamics is lacking. Here, we estimate and map the surface water extents in the CEB and its surroundings (CEB + S) for 32 years (1990–2021) from Landsat data using random forest models to provide long-term baseline information on surface water changes and dynamics. Based on the reference data, a total of 15,677 ± 1080 km2 have been inundated by surface water in the CEB + S during 1990–2021. This extent was accurately mapped by our local water extent product (mapped area = 16,273 km2, user’s accuracy = 91.5 ± 2.5%, producer’s accuracy = 91.1 ± 6%). With user’s and producer’s accuracy of 91%, our overall water extent provides the first most accurate long-term baseline information on surface water inundation in CEB + S necessary for local spatial planning processes to minimise future negative impacts of floods in the basin. Interannual variability of surface water extent is, however, high, with water extent ranging from 520.8 ± 375.7 km2 to 12372.3 ± 1154.7 km2 during the 1990–2021 period. The largest annual water extents (>10,000 km2) were recorded in 2006, 2008, 2009, 2011, and 2017, whereas the smallest extents (<1000 km2) were recorded in 1992 and 2019. We found that over 40% of the area inundated in the CEB + S during 1990–2021 was inundated less than 9 times. With human population increasing rapidly in the CEB + S, rarely inundated areas with short water residence could become a prime target for human settlements, which may lead to huge socio-economic damages during extreme floods if no preventive measures are put in place. Globally available surface water maps from the Global Land Analysis and Discovery (GLAD) and European Commission’s Joint Research Centre (JRC) did not provide realistic surface water extent for CEB + S, especially during years with extreme floods. Therefore, locally adopted product for operational monitoring of surface water in the CEB + S is needed to provide accurate information for informing spatial planning processes and surface water resource management strategies in this endorheic basin and help minimise future negative impacts of floods

Sustainable intensification of dairy production can reduce forest disturbance in Kenyan montane forests

Increasing demand for food and the shortage of arable land call for sustainable intensification of farming, especially in Sub-Saharan Africa where food insecurity is still a major concern. Kenya needs to intensify its dairy production to meet the increasing demand for milk. At the same time, the country has set national climate mitigation targets and has to implement land use practices that reduce greenhouse gas (GHG) emissions from both agriculture and forests. This study analysed for the first time the drivers of forest disturbance and their relationship with dairy intensification across the largest montane forest of Kenya. To achieve this, a forest disturbance detection approach was applied by using Landsat time series and empirical data from forest disturbance surveys. Farm indicators and farm types derived from a household survey were used to test the effects of dairy intensification on forest disturbance for different farm neighbourhood sizes (r = 2–5 km). About 18% of the forest area was disturbed over the period 2010–2016. Livestock grazing and firewood extraction were the dominant drivers of forest disturbance at 75% of the forest disturbance spots sampled. Higher on-farm cattle stocking rates and firewood collection were associated with 1–10% increased risk of forest disturbance across farm neighbourhood sizes. In contrast, higher milk yields, increased supplementation with concentrated feeds and more farm area allocated to fodder production were associated with 1–7 % reduced risk of forest disturbance across farm neighbourhood sizes. More intensified farms had a significantly lower impact on forest disturbance than small and resource-poor farms, and large and inefficient farms. Our results show that intensification of smallholder dairy farming leads to both farm efficiency gains and reduced forest disturbance. These results can inform agriculture and forest mitigation policies which target options to reduce GHG emission intensities and the risk of carbon leakage

Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

Monitoring of abnormal changes on the earth’s surface (e.g., forest disturbance) has improved greatly in recent years because of satellite remote sensing. However, high computational costs inherently associated with processing and analysis of satellite data often inhibit large-area and sub-annual monitoring. Normal seasonal variations also complicate the detection of abnormal changes at sub-annual scale in the time series of satellite data. Recently, however, computationally powerful platforms, such as the Google Earth Engine (GEE), have been launched to support large-area analysis of satellite data. Change detection methods with the capability to detect abnormal changes in time series data while accounting for normal seasonal variations have also been developed but are computationally intensive. Here, we report an implementation of BFASTmonitor (Breaks For Additive Season and Trend monitor) on GEE to support large-area and sub-annual change monitoring using satellite data available in GEE. BFASTmonitor is a data-driven unsupervised change monitoring approach that detects abnormal changes in time series data, with near real-time monitoring capabilities. Although BFASTmonitor has been widely used in forest cover loss monitoring, it is a generic change monitoring approach that can be used to monitor changes in a various time series data. Using Landsat time series for normalised difference moisture index (NDMI), we evaluated the performance of our GEE BFASTmonitor implementation (GEE BFASTmonitor) by detecting forest disturbance at three forest areas (humid tropical forest, dry tropical forest, and miombo woodland) while comparing it to the original R-based BFASTmonitor implementation (original BFASTmonitor). A map-to-map comparison showed that the spatial and temporal agreements on forest disturbance between the original and our GEE BFASTmonitor implementations were high. At each site, the spatial agreement was more than 97%, whereas the temporal agreement was over 94%. The high spatial and temporal agreement show that we have properly translated and implemented the BFASTmonitor algorithm on GEE. Naturally, due to different numerical solvers being used for regression model fitting in R and GEE, small differences could be observed in the outputs. These differences were most noticeable at the dry tropical forest and miombo woodland sites, where the forest exhibits strong seasonality. To make GEE BFASTmonitor accessible to non-technical users, we developed a web application with simplified user interface. We also created a JavaScript-based GEE BFASTmonitor package that can be imported as a module. Overall, our GEE BFASTmonitor implementation fills an important gap in large-area environmental change monitoring using earth observation data

Space-time detection of deforestation, forest degradation and regeneration in montane forests of Eastern Tanzania

Naturally isolated montane forests in East Africa are hotspots of biodiversity, often characterised by high species endemism, and are fundamental contributors to water services. However, they are located in areas highly suitable for agriculture, making them a prime target for agricultural activities. The Eastern Arc Mountains (EAM) in Eastern Tanzania are within the target regions for agricultural development under the Southern Agricultural Growth Corridor of Tanzania (SAGCOT). However, forest monitoring initiatives that track long-term forest dynamics and the ecological impact of current agricultural development policies on forests, are lacking. Here, we use the STEF (Space-Time Extremes and Features) algorithm and Landsat time series to track forest disturbances (deforestation and degradation) and forest gains (regeneration) as spatio-temporal events over seventeen years (2001–2017) in the montane forests of the Mvomero District in Tanzania. We found that 27 % (∼ 20 487 ha) of montane forests were disturbed between 2001 and 2017, mainly led by deforestation (70 %). Small-scale crop farms with maize, banana, and cassava crops, were the most planted on deforested areas. Most disturbances occurred at lower elevation (lowland montane), but there was an increasing shift to higher elevations in recent years (2011–2017). Forest disturbances exclusively occurred at small spatial scales, a pattern similar to other forest montane landscapes in Africa, which lowers detection capabilities in global forest loss products. Our locally calibrated and validated deforestation map (Producer's accuracy = 80 %; User’s accuracy = 78 %) shows a gross underestimation of forest cover loss (>10 000 ha) by global forest loss products in these mountainous forest landscapes. Overall, we found few areas undergoing forest regeneration, with only 9 % of the disturbed forest regenerating over 17 years. Long-term conversion to cropland prevented regeneration in the lowlands, with regeneration mainly happening at higher elevations. However, the shift of deforestation and forest degradation to higher elevations may challenge high elevation regeneration trends, leaving the remaining blocks of montane forest in the Mvomero District at a risk of degradation and disappearance. Without effective forest conservation measures, market-driven agricultural development is likely to trigger an expansion of cropland at the expense of forests to meet the increased demand for the agricultural products promoted, with negative impact on biodiversity, carbon sequestration and water services