Predicting forest cover in distinct ecosystems: the potential of multi-source sentinel-1 and -2 data fusion

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution

Tropical Forest Canopy Height and Aboveground Biomass Estimation Using Airborne Lidar and Landsat-8 Data, a Sensitivity Study with Respect to Landsat-8 Data Temporal Availability, in Mai Ndombe Province, Democratic Republic of Congo

Tropical forests’ structure information, such as forest canopy height, is a key component in any estimate of carbon stock. Tropical rainforests constitute the most forested ecosystems that harbor the largest biodiversity on Earth and store more carbon (above and belowground biomass) than any other ecosystem in the world. However, estimates of forest canopy structure is lacking over most of the regions that host this ecosystem because of both the structure’s complexity of this ecosystems and the incomplete or lack of up-to-date national forest inventory data necessary to derive forest canopy height and aboveground biomass. This study explores the capability of Landsat-8 imagery to predict dominant forest canopy height and aboveground biomass in Mai Ndombe province, Democratic Republic of Congo – a country that host half of the Congo Basin forests – within the context of the temporal availability of Landsat-8 imagery. A random forest regression model was used to predict dominant forest canopy height at 30 m spatial resolution from (a) only the July 14th 2013 (dry season) Landsat-8 image, (b) only the December 8th 2014 (wet season) Landsat-8 image, and (c) both images. The accuracy of the random forest regression model was performed on test data (n=2639) resulting in a, for the best prediction when using both dates together, RMSE = 3.84 m, R2 = 0.47. The model was then applied to the study area to derive forest canopy height using predictor variables from (a) only the dry season, (b) only the wet season, and (c) both images. The allometry equation defined by Xu et al. (2017) was used to generate aboveground biomass maps from (a) only the July 14th 2013 (dry season) Landsat-8 image, (b) only the December 8th 2014 (wet season) Landsat-8 image, and (c) both images using the study area forest canopy height maps. Field plots of aboveground biomass measurements were compared to predicted aboveground biomass maps for validation purpose. Validation process revealed a better prediction of aboveground biomass (RMSE= 83.77 Mg.ha-1) when the forest canopy height maps derived with both images was used to estimate aboveground biomass

Current knowledge on the Cuvette Centrale peatland complex and future research directions

CongoPeat Early Careers Researchers Group is a group of early career researchers who work directly or in partnership with the NERC funded CongoPeat project (NERC reference no.: NE/R016860/1; https://congopeat.net), which has provided the authors with full or partial financial and academic support.The Cuvette Centrale is the largest tropical peatland complex in the world, covering approximately 145,000 km2 across the Republic of Congo and the Democratic Republic of Congo. It stores ca. 30.6 Pg C, the equivalent of three years of global carbon dioxide emissions and is now the first trans-national Ramsar site. Despite its size and importance as a global carbon store, relatively little is known about key aspects of its ecology and history, including its formation, the scale of greenhouse gas flows, its biodiversity and its history of human activity. Here, we synthesise available knowledge on the Cuvette Centrale, identifying key areas for further research. Finally, we review the potential of mathematical models to assess future trajectories for the peatlands in terms of the potential impacts of resource extraction or climate change.Publisher PDFPeer reviewe

Age, extent and carbon storage of the central Congo Basin peatland complex

Peatlands are carbon-rich ecosystems that cover just three per cent of Earth's land surface, but store one-third of soil carbon. Peat soils are formed by the build-up of partially decomposed organic matter under waterlogged anoxic conditions. Most peat is found in cool climatic regions where unimpeded decomposition is slower, but deposits are also found under some tropical swamp forests. Here we present field measurements from one of the world's most extensive regions of swamp forest, the Cuvette Centrale depression in the central Congo Basin. We find extensive peat deposits beneath the swamp forest vegetation (peat defined as material with an organic matter content of at least 65 per cent to a depth of at least 0.3 metres). Radiocarbon dates indicate that peat began accumulating from about 10,600 years ago, coincident with the onset of more humid conditions in central Africa at the beginning of the Holocene. The peatlands occupy large interfluvial basins, and seem to be largely rain-fed and ombrotrophic-like (of low nutrient status) systems. Although the peat layer is relatively shallow (with a maximum depth of 5.9 metres and a median depth of 2.0 metres), by combining in situ and remotely sensed data, we estimate the area of peat to be approximately 145,500 square kilometres (95 per cent confidence interval of 131,900-156,400 square kilometres), making the Cuvette Centrale the most extensive peatland complex in the tropics. This area is more than five times the maximum possible area reported for the Congo Basin in a recent synthesis of pantropical peat extent. We estimate that the peatlands store approximately 30.6 petagrams (30.6 × 10(15) grams) of carbon belowground (95 per cent confidence interval of 6.3-46.8 petagrams of carbon)-a quantity that is similar to the above-ground carbon stocks of the tropical forests of the entire Congo Basin. Our result for the Cuvette Centrale increases the best estimate of global tropical peatland carbon stocks by 36 per cent, to 104.7 petagrams of carbon (minimum estimate of 69.6 petagrams of carbon; maximum estimate of 129.8 petagrams of carbon). This stored carbon is vulnerable to land-use change and any future reduction in precipitation

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Going beyond the green : senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Environmental Research Letters 12 (2017): 085004, doi:10.1088/1748-9326/aa7242.In sub-Saharan Africa (SSA), tropical dry forests and savannas cover over 2.5 million km2 and support livelihoods for millions in fast-growing nations. Intensifying land use pressures have driven rapid changes in tree cover structure (basal area, biomass) that remain poorly characterized at regional scales. Here, we posed the hypothesis that tree cover structure related strongly to senesced and non-photosynthetic (NPV) vegetation features in a SSA tropical dry forest landscape, offering improved means for satellite remote sensing of tree cover structure compared to vegetation greenness-based methods. Across regrowth miombo woodland sites in Tanzania, we analyzed relationships among field data on tree structure, land cover, and satellite indices of green and NPV features based on spectral mixture analyses and normalized difference vegetation index calculated from Landsat 8 data. From satellite-field data relationships, we mapped regional basal area and biomass using NPV and greenness-based metrics, and compared map performances at landscape scales. Total canopy cover related significantly to stem basal area (r 2 = 0.815, p  60%) at all sites. From these two conditions emerged a key inverse relationship: skyward exposure of NPV ground cover was high at sites with low tree basal area and biomass, and decreased with increasing stem basal area and biomass. This pattern scaled to Landsat NPV metrics, which showed strong inverse correlations to basal area (Pearson r = −0.85, p < 0.01) and biomass (r = −0.86, p < 0.01). Biomass estimates from Landsat NPV-based maps matched field data, and significantly differentiated landscape gradients in woody biomass that greenness metrics failed to track. The results suggest senesced vegetation metrics at Landsat scales are a promising means for improved monitoring of tree structure across disturbance and ecological gradients in African and other tropical dry forests.The project was funded by the US National Science Foundation Partnerships for International Research and Education (PIRE) program, project title 'Ecosystems and Human Well-Being' (Award # 0968211) PI Chris Neill. Additional research and dissertation support was provided to Marc Mayes from Brown University

Protected Area Performance in the Dry Forests and Savannahs of West Africa: a Study using L-band Synthetic Aperture Radar

Tropical ecosystems harbour the highest concentrations of biodiversity on Earth and play a pivotal role in the global carbon cycle, yet deforestation and degradation continue unabated in many regions, with net forest loss at 5.5 million ha yr-1 between 2010 and 2015. Protected areas offer a partial solution to this problem, with a growing body of evidence demonstrating their effectiveness for habitat conservation in the dense forests of Amazonia, Central Africa and Southeast Asia. Despite containing over a quarter of global biodiversity hotspots and being low density but significant carbon stores, tropical drylands have received far less attention in conservation terms, and research into protected areas in these ecosystems is far more limited. The overall effectiveness of protected areas in different dryland regions, and the factors influencing performance, are less understood. By measuring protected area performance as a function of aboveground biomass change, this study investigated the effectiveness of protected areas in the savannah belt of Nigeria, a country with a long history of environmental degradation. L-band Synthetic Aperture Radar (SAR), a form of remote sensing that penetrates the vegetation canopy, provided a means of consistently monitoring aboveground biomass change over time. Twenty-one areas, ranging in size from 117,000 ha to 608,410 ha, and offering varying levels of protection according to IUCN designations, were selected, with aboveground biomass changes between 2007 and 2017 determined by subjecting L-band SAR data to a novel approach called ‘Biomass Matching’. The combination of SAR and Biomass Matching allowed aboveground biomass changes within these protected areas to be detected and estimated without the need for supplementary field data, which is usually required to calibrate such remote sensing data. All but four protected areas experienced increases in aboveground biomass over the study period, with mean change being +1.22 Mg ha-1, compared to +0.26 Mg ha-1 for a set of twelve similar unprotected areas. Furthermore, their performance was affected by an array of factors, though accessibility and management efficacy were deemed the most influential. These results suggest that, with appropriate monitoring and resourcing, protected areas in Nigerian dry forests and savannahs can provide effective habitat conservation, though more inaccessible areas will inherently perform better