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

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018)

Understanding ‘saturation’ of radar signals over forests

There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain ‘saturation’ point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points

The BIOMASAR algorithm: An approach for retrieval of forest growing stock volume using stacks of multi-temporal SAR data

Retrieval of forest growing stock volume (GSV) is a major topic of investigation in the remote sensing community due to the necessity of accurate and updated information on forest resources, which is not achievable using traditional survey methods at the regional and global level. Radar remote sensing has the advantage of being able to acquire images over any part of the Earth with a high repetition frequency. While an image can be formed regardless of the cloud cover and the solar illumination, the environmental conditions can play a significant role on the measurements collected by the sensor. This aspect is of particular relevance in case of forest-related studies and for high frequencies. C-band SAR backscatter is generally deemed as useless when aiming at forest resources assessment due to the weak sensitivity with respect to biophysical properties. Furthermore, the strong sensitivity to the dielectric properties of the scattering objects make C-band SAR backscatter an unreliable tool for monitoring forests. With increasing number of studies on extraction of forest biophysical properties from SAR data, the research community tends toward a clear focus on the use of low frequency SAR. In this paper we demonstrate that accurate estimates of forest GSV can be obtained also from C-band backscatter data under the requirement that large stack of observations are available. The estimation of the GSV is carried out by means of the BIOMASAR algorithm, which combines conventional SAR processing techniques in the case of multi-temporal data stacks (calibration, co-registration, multi-temporal speckle filter), the inversion of Water-Cloud-like model relating the GSV to the forest backscatter, and a multi-temporal combination of GSV estimates from each image. While the single parts forming the BIOMASAR algorithm approach are well known (Askne et al, IEEE TGRS, 1995; Kurvonen et al, IEEE TGRS, 1999), the implementation in an automated approach to retrieve GSV is a novel aspect. Model training, which is traditionally based on in situ measurements of forest GSV and corresponding forest backscatter measurements, is carried out by consideration of the backscattering for unvegetated and dense forest areas. These are identified by means of the MODIS Vegetation Continuous Fields product. For each pixel the corresponding measures of central tendency in a finite-size window are computed. The multi-temporal combination exploits the different sensitivities of the forest backscatter to GSV, which can be derived from the estimates of the a priori unknown model parameters. The BIOMASAR algorithm was first presented in (Santoro et al, Proc ENVISAT Symposium, 2007) and has now been validated in the case of ENVISAT ASAR ScanSAR data using in situ information from five test sites within the boreal zone. The validation activities were carried out within an ESA Support to Science Element (STSE) Project. The algorithm performs well for all validation sites. The retrieval RMSE is generally below 40% for full resolution data and below 20% for aggregated versions at reduced spatial resolution. The most prominent result is that the retrieved GSV was never affected by saturation, with estimates of GSV in line with in situ data up to 300 m3/ha. This unexpected result opens up the possibility of exploiting the extensive archives of ENVISAT ASAR ScanSAR data for pan-boreal forest GSV retrieval at a spatial resolution required by ecological and carbon accounting models

Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements

Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100 m and 1 km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Quebec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300 m3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1 km pixel size. Larger errors were obtained at 100 m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10 W 10 pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data

Forest Dragon 2: Mid-term results of the European partners

One of the main objectives of the Forest DRAGON 2 project is the evaluation of the Forest DRAGON 1 large area forest growing stock volume (GSV) maps generated for Northeast and Southeast China, based on ERS-1/2 tandem coherence data from the mid 1990s. A special cross-comparison design mainly based on freely available Earth Observation products has been developed in consequence of lack of extensive in situ measurements. A reasonable agreement above 70 % between the forest GSV maps and the EO products in terms of forest/ non-forest could be achieved for NE and SE China. The assessment of forest cover and structure changes in China from the mid 1990s into the current decade is addressed by a pilot study at the regions of Daxinganling and Xiaoxinganling. A one-year stack (2007) of Envisat ASAR GMM data has been processed at 1-km pixel size with the BIOMASAR algorithm to obtain continuous GSV. Accordingly, the ERS-1/2 tandem data has been reprocessed to 1 km to allow an intercomparison of the two products, which in turn allowed observing scaling effects on the forest GSV. Preliminary results show plausible detection of forest cover changes

Providing low-budget estimations of carbon sequestration and greenhouse gas emissions in agricultural wetlands

The conversion of wetlands to agriculture through drainage and flooding, and the burning of wetland areas for agriculture have important implications for greenhouse gas (GHG) production and changing carbon stocks. However, the estimation of net GHG changes from mitigation practices in agricultural wetlands is complex compared to dryland crops. Agricultural wetlands have more complicated carbon and nitrogen cycles with both above- and below-ground processes and export of carbon via vertical and horizontal movement of water through the wetland. This letter reviews current research methodologies in estimating greenhouse gas production and provides guidance on the provision of robust estimates of carbon sequestration and greenhouse gas emissions in agricultural wetlands through the use of low cost reliable and sustainable measurement, modelling and remote sensing applications. The guidance is highly applicable to, and aimed at, wetlands such as those in the tropics and sub-tropics, where complex research infrastructure may not exist, or agricultural wetlands located in remote regions, where frequent visits by monitoring scientists prove difficult. In conclusion, the proposed measurement-modelling approach provides guidance on an affordable solution for mitigation and for investigating the consequences of wetland agricultural practice on GHG production, ecological resilience and possible changes to agricultural yields, variety choice and farming practice

Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR

This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10°N) from hyper-temporal observations of Envisat Advanced Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01° were used to estimate GSV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate, subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest ( 300 m3/ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE between ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%)

Sensitivity of sentinel-1 interferometric coherence to crop structure and soil moisture

This paper investigates the sensitivity of Sentinel-1 (S-1) interferometric coherence to crop structure and near surface soil moisture (SSM) content. The study analyzes a data set collected in 2017 over the Apulian Tavoliere agricultural site (Southern Italy). The data set includes: i) in situ data over more than 600 agricultural fields monitored during the 2017 winter and spring growing seasons; ii) time-series of S-1 IW VV and VH backscatter and interferometric coherence; iii) time series of S-1 SSM maps. The temporal behavior of S-1 coherence and VH backscatter has been assessed over the monitored agricultural fields. Initial results indicate a stronger sensitivity of S-1 coherence than VH backscatter to crop geometric structure. In addition, an analysis at site scale, conducted before and after an important rain event, indicates a change of SSM from 0.18 to 0.30 m3/m3 along with a change of S-1 coherence from 0.61 to 0.53