Remote sensing scientists have employed the free and open Landsat archive to produce pathfinding large area data sets characterizing both land cover extent and change. Such efforts will soon lead to systematic monitoring and area estimation of national to global scale land change with Landsat augmented by ESA’s Sentinel 2 sensors. Here, we propose a generic method to characterize large area land cover and land use extent and change by employing medium spatial resolution map products as stratifiers for the allocation of probability-based samples of reference data. Methods employ available reference data for area estimation of land changes following good practice guidance, including commercial high spatial resolution imagery and in situ data. Our proposed method focuses on land cover themes of biophysical vegetative traits such as tree and herbaceous cover, with secondary layers targeting land use, specifically forestry, cropland and urbanization. Change areas are estimated from sampled referenced data and used to create final unbiased maps. Several national to global-scale examples will be presented within an overall hierarchical monitoring strategy.

Tropical evergreen forests provide essential ecosystem services1,2. Fine scale mapping and characterization of their disturbances are needed to support global conservation policies3 and to accurately quantify their contribution to global carbon fluxes4. However, limited information exists on their remaining extent and long-term historical changes. Here, we produced a wall-to-wall map of tropical evergreen forest cover dynamics at 30-meter resolution from 1982 to 2017. Each individual image of the full Landsat archive (1 140 000 scenes) has been mapped using an expert system to allow all disturbances in the forest cover - including from selective logging activities and fires that are visible during a short period - to be depicted and characterized in terms of timing (dates and duration), sequential dynamics, intensity and extent. The performance of our disturbance classifier has been validated against 12 235 reference sample plots resulting into 9.4% omissions, 8.1% false detections and 91.4% overall accuracy.
Our dataset depicts the evergreen forest extent and patterns of disturbances through two complementary layers: a transition map (Fig. 1) and an annual change dataset (Fig. 2).
The transition map captures the resulting disturbance dynamics over the 36-year period by depicting (i) remaining undisturbed forests, (ii) two types of degraded forests (corresponding mostly to either logged or burned forests), (iii) young forest regrowth, and (iv) deforested land that includes three subcategories of converted land cover: (a) water bodies (new dams and river flow changes); (b) tree plantations; and (c) other land cover that includes infrastructure, agriculture and mining. The annual change dataset is a collection of 36 maps depicting - for each year between 1982 and 2017 - the spatial extents of undisturbed forests and disturbances.
We found that pan-tropical forest disturbances have been largely under-estimated so far. For the first time at this scale, we discriminate deforestation from degradation and we underline the importance of the degradation process in tropical forest ecosystems. Our analysis shows the trends of deforestation and degradation by country, sub-region and continent. Finally, we extrapolated the recent average rates of disturbances to predict the extent of the undisturbed tropical evergreen forest by 2050.
We intend to apply this approach to future Landsat data (from 2018) and to adapt it to Sentinel 2 data (available since 2015) towards a near real time monitoring of evergreen forests with higher frequency of observations and finer spatial resolution.

1. Gibson, L., Lee, T. M., Koh, L. P., Brook, B. W., Gardner, T. A., Barlow, J., Peres, C. A., Bradshaw, C. J. A., Laurance, W. F., Lovejoy, T. E., Sodhi, N. S. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011). doi:10.1038/nature10425
2. Watson, J. E. M. , Evans, T., Venter, O., Williams, B., Tulloch, A., Stewart, C., Thompson, I., Ray, J. C., Murray, K., Salazar, A., McAlpine, C., Potapov, P., Walston, J., Robinson, ., J. G., Painter, M., Wilkie, D., Filardi, C., Laurance, W. F., Houghton, R. A., Maxwell, S., Grantham, H., Samper, C., Wang, S., Laestadius, L., Runting, R. K., Silva-Chávez, G. A., Ervin, J., Lindenmayer, D. The exceptional value of intact forest ecosystems. Nature Ecology & Evolution 2, 599–610 (2018). Doi:10.1038/s41559-018-0490-x
3. Mackey, B., DellaSala, D. A., Kormos, C., Lindenmayer, D., Kumpel, N., Zimmerman, B., Hugh, S., Young, V., Foley, S., Arsenis, K., Watson, J. E. M. Policy options for the world’s primary forests in multilateral environmental agreements. Conserv. Lett. 8, 139–147 (2015). doi:10.1111/conl.12120
4. Mitchard, E.T.A. The tropical forest carbon cycle and climate change, Nature, 559, 527-534 (2018).

We present ongoing progress and future developments of moving from land cover to land use monitoring, and how recent research and novel approaches improve land change assessments. This development is underpinned by several international and national policies processes that increasingly include performance-based incentive systems to curb trends in forest loss in the tropics and to stimulate reforestation and forest restoration, and to enhance the goods and services provided by forests. Most prominently, international climate change negotiations lead to increased interest forest-related land use changes and forest regrowth monitoring. Furthermore, the sustainable development goals adopted by the UN in 2015, have 17 goals aimed at sustainably transforming our world. There are 232 targets each with one or more indicators which must be monitored for reporting by countries. Annual land cover and land use data are crucial for a number of the key indicators related to agriculture, water, climate, and ecosystems. Other agreements such as the Committee on Food Security could also benefit from land use information are increasingly demanded to provide information on crop and pasture productivity.
Here, we will review new approaches for providing spatial information on land use (change). We first present a series of examples for use of available spatial data and methods to support forest and land use related policy design and planning by looking at data-driven assessments on agriculture-forest dynamics and a new pan-tropical assessment of drivers of deforestation. We show how different scenarios of agricultural intensification versus expansion of agriculture affect food security can benefit from more satellite-data driven approaches (Examples from Kenia and Tanzania). Near-real time monitoring systems are increasingly available to track land use and land management characteristics and assist with implementation and enforcement of zero-deforestation pledges and sustainable supply chains (we show examples for Indonesia and Malaysia). In addition, we discuss how available forest change data and tools can aid in assessing (REDD+) policy performance at the local scale, and what important considerations are regarding accuracy, uncertainty and complementarity of different datasets. We present spatially explicit greenhouse gas emissions for the tropics developed with a data-driven approach, a key step towards the 2023 UNFCCC global stocktake adding a third data pillar besides large area vegetation models and national GHG inventories.
We conclude with specific considerations for the next steps in that arena by using Sentinel 1 and 2 data as prime input data.

Sustainable land management and preservation of natural resources have been highlighted as key target in the Sustainable Development Goals (UN, 2015), as well as in the UNCCD’s Land Degradation Neutrality (LDN) programme (UNCCD, 2015). This implies that repeated large scale land change assessment will be needed in order to assess the success or failure of reaching the SDG targets and LDN objectives. Yet, a consensus method for systematic assessment of changing pressure on terrestrial ecosystems and drivers attribution is still lacking at continental scale (WAD, 2018).

Defined as the rate of carbon uptake per unit of water lost, the water-use efficiency (WUE) is a key parameter for assessing change in terrestrial ecosystem response to hydro-climatic conditions (Tang et al., 2014). It can be estimated at the ecosystem level by the ratio between above-ground net primary productivity (ANPP) and actual evapotranspiration (ETa) (Huang et al., 2015). Extremes climate and land-use land-cover changes (LULCC) can result in abrupt changes in ecosystem functioning (EF). A contrary to traditional assessment methods based on linear models, piece-wise regression models allow capturing both abrupt and gradual changes in environmental time series (Horion et al., 2016, Verbesselt et al., 2010). Here we applied segmented trend analysis on time series of WUE (period 1999-2013) in order to assess changes in EF over Europe. WUE was estimated based on SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) (used as proxy for ANPP) and simulated ETa from the Soil Water Balance for European Water Accounting Model swb-EWA (Kurnik et al., 2013). Timing and occurrence of abrupt changes in WUE were related to drought events (as depicted by the Standardised Precipitation Evapotranspiration Index SPEI (Vicente-Serrano et al., 2010)), as well as to LULCC (based on archetypical change trajectories of Levers et al. (2015)). Further analyses were performed for areas with special interest (i.e. Natura 2000 and known cases of soil degradation).

At European scale, significant changes in WUE were observed in more than 30% of the territory, of which more than 20% were categorized as abrupt. Regions with increasing crop yield and intensification of wood production coincided overall with large-scale positive reversals in WUE, whereas decreased WUE were notably observed over areas with agricultural land abandonment. The footprint of major European droughts could also be retrieved from the WUE time series, and evidences of varying response to drought were found for the different eco-climatic biomes in Europe, with notably Mediterranean (/Northern) ecosystems showing increased (/decreased) WUE under increased dry conditions. We conclude that mapping of gradual and abrupt changes in WUE is a valuable tool for systematic assessment of ecosystem condition at continental scale, and a step towards facilitated driver attribution. Yet accurate information on current and past changes in land use and land management is still lacking to reach a comprehensive attribution of the driving forces behind the observed changes in ecosystem functioning.

References:
Horion S, Prishchepov AV, Verbesselt J, De Beurs K, Tagesson T, Fensholt R (2016). Revealing turning points in ecosystem functioning over the Northern Eurasian agricultural frontier. Glob Chang Biol, 22, 2801-2817.
Huang MT, Piao SL, Sun Y et al. (2015). Change in terrestrial ecosystem water-use efficiency over the last three decades. Global Change Biology, 21, 2366-2378.
Kurnik B, Louwagie G, Erhard M, Ceglar A, Bogataj Kajfež L (2013). Analysing Seasonal Differences between a Soil Water Balance Model and in Situ Soil Moisture Measurementsat Nine Locations Across Europe. Environmental Modeling & Assessment, 19, 19-34.
Levers C, Müller D, Erb K et al. (2015). Archetypical patterns and trajectories of land systems in Europe. Regional Environmental Change.
Tang XG, Li HP, Desai AR et al. (2014). How is water-use efficiency of terrestrial ecosystems distributed and changing on Earth? Scientific Reports, 4.
UN (2015). 70/1 - Transforming our world: the 2030 Agenda for Sustainable Development pp Page, New York, United Nations.
UNCCD (2015). Achieving Land Degradation Neutrality. https://www.unccd.int/actions/achieving-land-degradation-neutrality, Last access on 12-07-2018.
Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115.
Vicente-Serrano SM, Begueria S, Lopez-Moreno JI (2010). A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23, 1696-1718.
WAD (2018). World Atlas of Desertification, Luxembourg, Publication Office of the European Union.

The expansion of oil palm plantations is a significant driver of deforestation in many countries across the tropics, causing biodiversity loss and increasing greenhouse gas emissions. As the demand for palm oil grows, more area gets allocated to grow this crop. For this reason, it is important to develop a methodology to map existing plantations as well as smallholder oil palm fields on a larger scale in order to assess impacts of plantation expansion. A few studies on oil palm mapping exist but they often focus on relatively small areas, i.e., countries or regions, and do not show transferability of their methods to other areas (Li 2015, Torbick 2015, Cheng 2016).

Therefore, in order to assess the impacts and dynamics of expansion of the area for palm oil production on large scale, we develop a pixel-based automatic approach for oil palm mapping at 10m resolution using Copernicus Sentinel-1 microwave and Sentinel-2 multi-spectral data as well as additional auxiliary datasets. The combination of these data inputs at 10m resolution enabled detecting not only large oil palm plantation but also oil palm fields under 1 ha in size. We aim to make our approach scalable and completely operational, what will enable a continuous oil palm plantation expansion monitoring.

We present our initial mapping results for Indonesia and Malaysia, and also demonstrate the transferability of the developed approach to other areas, including Brazil, Cameroon, Thailand and Mexico. We compare our results with those from other studies where maps are available at the regional level (Austin 2017, Cheng 2018). We perform a blind map validation and analyze the differences between maps. We discuss limitations of using SAR data when mapping oil palm on slopes. Using the Google Earth Engine capabilities, we are planning to scale the approach to the tropics and produce the first large scale oil palm plantation map.

In July 2018, Australia produced its first voluntary national review (VNR) of the Sustainable Development Goals. Recognising the importance of collecting data that are relevant, reliable and timely, the government developed an SDG data platform to house available datasets on the SDG indicators and in tandem stocktaking the status of data collection against the 232 SDG indicators. A whole of government initiative, the platform is sought to assist in identifying new datasets and on refining the SDG indicators, particularly as the move from a Tier II to a Tier I or II occur, where additional datasets may be needed.

The government recognises that Earth Observation (EO) technology can help inform decision-makers about performance against certain SDG Targets and Indicators. Digital Earth Australia (DEA) led by Geoscience Australia has enabled the compilation, analysis and interpretation decades of satellite data into information and insights about Australia’s terrestrial and marine ecosystems. EO-derived information could help in setting baselines against which to set SDG targets, and in turn, measure progress against the agreed SDGs; aspects that the first VNR did not tackle.

This presentation will show initial results of an inter-institutional effort led by Geoscience Australia, to map land cover and land cover change dynamics using multi-scale time series of Landsat and Copernicus Sentinel data sets, and linking those layers with relevant environmental variables within a Drivers-Pressure-State-Response (DPSIR) framework We will also review the environmental variables available for Australia from both Earth observations and other sources (e.g., models) but also identify gaps both in terms of spatial products and temporal availability. Layers already available within DEA include annual hydroperiods (days), extent of intertidal mudflats and vegetation cover (%). We will convey how these layers can be integrated within the framework of a) the Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) to generate land cover and change classifications and b) the DPSIR and related Response-Pressure-State-Benefits (RPSB) indicators to support policy review, development and decisions. Further, we demonstrate the benefits of the approach and EO in general for supporting the System of Environmental and Economic Accounting (SEEA) for Australia. Contributions to the development of SDG indicators dashboards (e.g. SDG15.2.1; SDG 15.3.1),State of Environment Reports, alert systems, and environmental management from DEA-EO derived products are also anticipated.