Forest biomass retrieval approaches from earth observation in different biomes

The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of numerous forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha−1 to 55 t ha−1 (37% to 67% relative RMSE), and an overall bias ranging from −1 t ha−1 to +5 t ha−1 at pixel level. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha−1) in the lower AGB classes, and underestimation (up to 85 t ha−1) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level

Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Non peer reviewe

The effects of ditch dams on water‐level dynamics in tropical peatlands

A significant proportion of tropical peatlands has been drained for agricultural purposes, resulting in severe degradation. Hydrological restoration, which usually involves blocking ditches, is therefore a priority. Nevertheless, the influence of ditch blocking on tropical peatland hydrological functioning is still poorly understood. We studied water-level dynamics using a combination of automated and manual dipwells, and also meteorological data during dry and wet seasons over 6 months at three locations in Sebangau National Park, Kalimantan, Indonesia. The locations were a forested peatland (Forested), a drained peatland with ditch dams (Blocked), and a drained peatland without ditch dams (Drained). In the dry season, water tables at all sites were deeper than the Indonesian regulatory requirement of 40 cm from the peat surface. In the dry season, the ditches were dry and water did not flow to them. The dry season water-table drawdown rates — solely due to evapotranspiration — were 9.3 mm day−1 at Forested, 9.6 mm day−1 at Blocked, but 12.7 mm day−1 at Drained. In the wet season, the proportion of time during which water tables in the wells were deeper than the 40 cm limit ranged between 16% and 87% at Forested, 0% at Blocked, and between 0% and 38% at Drained. In the wet season, water flowed from the peatland to ditches at Blocked and Drained. The interquartile range of hydraulic gradients between the lowest ditch outlet and the farthest well from ditches at Blocked was 3.7 × 10−4 to 7.8 × 10−4 m m−1, but 1.9 × 10−3 to 2.6 × 10−3 m m−1 at Drained. Given the results from Forested, a water-table depth limit policy based on field data may be required, to reflect natural seasonal dynamics in tropical peatlands. Revised spatial designs of dams or bunds are also required, to ensure effective water-table management as part of tropical peatland restoration

Amplification of wildfire area burnt by hydrological drought in the humid tropics

Borneo’s diverse ecosystems, which are typical humid tropical conditions, are deteriorating rapidly, as the area is experiencing recurrent large-scale wildfires, affecting atmospheric composition1, 2, 3, 4 and influencing regional climate processes5, 6. Studies suggest that climate-driven drought regulates wildfires2, 7, 8, 9, but these overlook subsurface processes leading to hydrological drought, an important driver. Here, we show that models which include hydrological processes better predict area burnt than those solely based on climate data. We report that the Borneo landscape10 has experienced a substantial hydrological drying trend since the early twentieth century, leading to progressive tree mortality, more severe than in other tropical regions11. This has caused massive wildfires in lowland Borneo during the past two decades, which we show are clustered in years with large areas of hydrological drought coinciding with strong El Niño events. Statistical modelling evidence shows amplifying wildfires and greater area burnt in response to El Niño/Southern Oscillation (ENSO) strength, when hydrology is considered. These results highlight the importance of considering hydrological drought for wildfire prediction, and we recommend that hydrology should be considered in future studies of the impact of projected ENSO strength, including effects on tropical ecosystems, and biodiversity conservation

Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Lette

A conceptual model for assessing rainfall and vegetation trends in Sub-Saharan Africa from satellite data

Policymakers, governments and aid agencies require operational environmental monitoring in support of evidence-based policy-making and resource deployment in crisis situations. For Africa, this is only feasible at sub-continental scale with a large network of automated meteorological stations, a large number of highly coordinated field observers or with satellite remote sensing. The challenge with satellite data lies in the derivation of meaningful environmental indicators. This article describes a conceptual framework for understanding satellite-derived indicators of rainfall and vegetation greenness trends over Africa. It attributes observed vegetation changes to climatic (i.e. rainfall linked) and non-climatic drivers. A decade of annual rainfall and vegetation data over sub-Saharan Africa was analysed using satellite-based rainfall estimates [Famine Early Warning System Rainfall Estimation 2.0 (FEWSNET RFE 2.0)] from National Oceanic and Atmospheric Administration’s (NOAA’s) Climate Prediction Centre and the Normalized Difference Vegetation Index (NDVI) obtained from the Satellite Pour l’Observation de la Terre Vegetation (SPOT-VGT) sensor. Rainfall and vegetation greenness trends were analysed for 759 administrative regions of sub-Saharan Africa to identify those regions that have experienced a negative, positive or stable rainfall/vegetation trend over the period 2001–2010. The character of the relationship between the annual rainfall and max NDVI trends were examined to identify areas where the changes in greenness could be attributed to climatic (rainfall) and non-climatic (human land use or ecological disturbance) changes. Regions where increasing rainfall was associated with vegetation greening were found in West Africa, Central African Republic, West Cameroon and northeastern part of South Africa, whereas areas with evidence of ‘climatic vegetation degradation’ were located in Southern Madagascar, Nigeria, Kenya and the Garden Route region of South Africa

Restoration Ecology of Lowland tropical Peatlands in Southeast Asia: Current Knowledge and Future Research Directions

Studies of restoration ecology are well established for northern peatlands, but at an early stage for tropical peatlands. Extensive peatland areas in Southeast Asia have been degraded through deforestation, drainage and fire, leading to on- and off-site environmental and socio-economic impacts of local to global significance. To address these problems, landscape-scale restoration measures are urgently required. This paper reviews and illustrates, using information from on-going trials in Kalimantan, Indonesia, the current state of knowledge pertaining to (i) land-cover dynamics of degraded peatlands, (ii) vegetation rehabilitation, (iii) restoration of hydrology, (iv) rehabilitation of carbon sequestration and storage, and (v) promotion of sustainable livelihoods for local communities. For a 4500 km2 study site in Central Kalimantan, Indonesia, we show a 78% reduction in forest cover between 1973 and 2003 and demonstrate that fire, exacerbated by drainage, is the principal driver of land-use change. Progressive vegetation succession follows infrequent, low-intensity fires, but repeated and high-intensity fires result in retrogressive succession towards non-forest communities. Re-wetting the peat is an important key to vegetation restoration and protection of remaining peat carbon stocks. The effectiveness of hydrological restoration is discussed and likely impacts on greenhouse gas emissions evaluated. Initial results indicate that raised water levels have limited short-term impact on reducing CO2 emissions, but could be critical in reducing fire risk. We conclude that successful restoration of degraded peatlands must be grounded in scientific knowledge, relevant to socio-economic circumstances, and should not proceed without the consent and co-operation of local communitie

SInCohMap: Sentinel-1 multi-temporal interferometric coherence for land cover classification and mapping

International audienc

Assessing hypertemporal SENTINEL-1 COHERENCE maps for LAND COVER monitoring

International audienceThis paper presents the main concepts and the initial analysis of the ESA SEOM project SInCohMap 'EXPLOITATION OF SENTINEL-1 INTERFEROMETRIC COHERENCE FOR LAND COVER AND VEGETATION MAPPING'. The project evaluates the performance of using the interferometric coherence of S-1 time series for land cover and vegetation mapping. One of the main objectives of the project is to quantify the impact in using S-1 InSAR (Interferometric Synthetic Aperture Radar) data relative to traditional land cover and vegetation mapping using optical data (especially Sentinel-2, hereafter named S-2) or SAR-based (Synthetic Aperture Radar) approaches. In this framework, a Round-Robin consultation is used to assess the performances of the different classification methodologies. © 2017 IEEE