Above-ground Biomass Datasets Research Articles

Estimating aboveground biomass of tropical urban forests with UAV-borne hyperspectral and LiDAR data

Urban trees and forests can contribute to climate change mitigation by sequestering carbon in their living tissues, with aboveground biomass (AGB) playing a pivotal role. This study explores the capability of UAV-borne hyperspectral and LiDAR data for estimating AGB in tropical urban forests. Structural attributes of trees, such as diameter at breast height (DBH), total height, and wood density, were collected from over 5600 individuals, forming a comprehensive AGB dataset. Our methodology included two primary AGB estimation strategies: an area-based strategy that correlated AGB with hyperspectral and canopy height data across various grid sizes and an individual tree crown (ITC)-based method that integrated canopy height, spectral signatures of individual trees and crown area. The findings indicate that increasing the grid size from 10 m to 50 m improved the R2 from 0.24 ± 0.04–0.61 ± 0.13, mainly due to reduced border effects. Furthermore, integrating canopy height and hyperspectral data enhanced the R2 of AGB estimates from 0.61 ± 0.13–0.70 ± 0.09 for a 50 × 50 m grid. Crucially, wavelengths centered at the green peak and red-edge were identified as key bands in AGB retrieval. Integrating hyperspectral and LiDAR data did not significantly enhance results for individual trees, where AGB was closely linked to tree height and crown area. This study underscores the potential of utilizing integrated UAV-borne sensors for biomass assessment in urban forest settings.

Challenges of Including Wet Grasslands with Variable Groundwater Tables in Large-Area Crop Production Simulations

Large-scale assessments of agricultural productivity necessitate integrated simulations of cropland and grassland ecosystems within their spatiotemporal context. However, simultaneous simulations face limitations due to assumptions of uniform species distribution. Grasslands, particularly those with shallow groundwater tables, are highly sensitive to water availability, undergoing rapid species composition changes. We hypothesised that predicting above-ground biomass (AGB) remains challenging due to these dynamic responses. Ten years of data from four lysimeters at a German wet grassland site, with varying water table treatments, was utilised to test this hypothesis. Correlation analysis revealed a strong positive indirect effect of the water regime on AGB, with a one-year time lag (r = 0.97). The MONICA model initially exhibited fair agreement (d = 0.69) in simulating Leaf-Area-Index (LAI) but performed poorly in replicating AGB (d = 0.3). After removing the species composition change effect from the LAI and AGB datasets, the simulation notably improved, with the overall relative root mean square error (rRMSE) of AGB decreasing from 1.55 to 0.90 between the first and second simulations. This demonstrates MONICA’s ability to predict grass growth patterns amidst changing water supply levels for constant species composition. However, it needs a competition model to capture biomass growth changes with varying water supply.

LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa

Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth’s carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).

Effects of climate and plant functional types on forest above-ground biomass accumulation

BackgroundForest above-ground biomass (AGB) accumulation is widely considered an important tool for mitigating climate change. However, the general pattern of forest AGB accumulation associated with age and climate gradients across various forest functional types at a global scale have remained unclear. In this study, we compiled a global AGB data set and applied a Bayesian statistical model to reveal the age-related dynamics of forest AGB accumulation, and to quantify the effects of mean annual temperature and annual precipitation on the initial AGB accumulation rate and on the saturated AGB characterizing the limit to AGB accumulation.ResultsThe results of the study suggest that mean annual temperature has a significant positive effect on the initial AGB accumulation rate in needleleaf evergreen forest, and a negative effect in broadleaf deciduous forest; whereas annual precipitation has a positive effect in broadleaf deciduous forest, and negative effect in broadleaf evergreen forest. The positive effect of mean annual temperature on the saturated AGB in broadleaf evergreen forest is greater than in broadleaf deciduous forest; annual precipitation has a greater negative effect on the saturated AGB in deciduous forests than in evergreen forests. Additionally, the difference of AGB accumulation rate across four forest functional types is closely correlated with the forest development stage at a given climate.ConclusionsThe contrasting responses of AGB accumulation rate to mean annual temperature and precipitation across four forest functional types emphasizes the importance of incorporating the complexity of forest types into the models which are used in planning climate change mitigation. This study also highlights the high potential for further AGB growth in existing evergreen forests.

A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data

Abstract. The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m2) was successfully upscaled to the MODIS pixel scale (62 500 m2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m−2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.272587; H. Zhang et al., 2022).

Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure

The backscattered power recorded by a spaceborne scatterometer operating at C-band is sensitive to land surface parameters and is operationally used by some global remote sensing services, e.g., to estimate soil moisture. The estimation of forest variables, in particular above-ground biomass (AGB), from scatterometer data instead was seldom explored. Given the availability of multi-decadal sets of scatterometer observations from space, it is of interest to address the contribution of C-band scatterometer data to the quantification of carbon stocks stored in forests even if the spatial resolution of spaceborne scatterometers is very coarse. In this paper, we investigated the prospects of AGB estimation using backscatter observations by the MetOp Advanced SCATterometer (ASCAT) with a spatial resolution of 0.25°. For this study, ASCAT observations acquired in 2010 were used to be contemporary with AGB datasets selected to benchmark the performance of the estimation. A Water Cloud Model that integrates two allometric equations derived from spaceborne LiDAR data reproduced the relationship between observations of radar backscatter as a function of AGB. Estimates of AGB from individual observations were then combined with a weighted average to reduce uncertainties. Finally, a correction was introduced to compensate for the offset introduced by sloped terrain and surfaces not covered by woody vegetation on the AGB estimate of a pixel. Uncertainties associated with the scatterometer observations, and the modelling framework were propagated to obtain per-pixel values of the standard deviation of an AGB estimate. The proposed method explains much of the variance in AGB estimates when compared to measurements from inventory data (R2 = 0.72) and generated unbiased estimates globally (bias: −3.3 Mg⋅ha−1). Nonetheless, the discrepancy between estimated and plot-based AGB values tended to increase for decreasing biomass level from 20% to 60% of the reference AGB level. A further assessment related to global stocks indicated that the value estimated from the scatterometer dataset (596 Pg, 95% of which 563 Pg stored in forest land) was in line with two published estimates based on forest inventory data only (571 Pg and 600 Pg, respectively). Despite the coarse spatial resolution, our results indicate that C-band scatterometer observations from space can contribute to the characterization of terrestrial biomass pools. The record of observations starting in the early 1990s may provide an unprecedented way to look at long-term forest dynamics as well as to constrain the strength of carbon-climate cycle feedback simulated by Earth System models.

A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation

As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha−1. Compared to county-level statistics during 2007–2015, the ranges of R2, RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha−1, and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.

Satellite-derived NDVI underestimates the advancement of alpine vegetation growth over the past three decades.

Satellite-derived normalized difference vegetation index (NDVI) data are increasingly relied on to reveal the growth responses of vegetation to climate change, yet the vegetation growth tracking accuracy of these data remains unclear due to a lack of long-term field data. Here, we adopted a unique field-measured seasonal aboveground biomass dataset from 1982-2014 to assess the potential of using satellite-derived NDVI data to match field data in regard to the interannual variability in seasonal vegetation growth in a Tibetan alpine grassland. We revealed that Global Inventory Monitoring and Modeling System (GIMMS) NDVI data captured the advancement of field-measured vegetation growth throughout the entire study period but not from 2000-2014, while MODIS NDVI data still observed this advancing trend after 2000 to a limited extent. However, satellite-derived NDVI data consistently underestimated the advancement degree of field-measured vegetation growth, regardless of whether GIMMS or MODIS NDVI data were considered. We tentatively attribute this underestimation to an increased ratio of grass biomass to forb biomass, which could delay the advancement of NDVI development but not affect that of field-measured biomass development. Our results suggest that satellite-derived NDVI data may miss critical responses of vegetation growth to global climate change, potentially due to long-term shifts in plant community composition.

Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

The real potential of current passive satellite data to map aboveground biomass in tropical forests

AbstractForest biomass estimation at large scale is challenging and generally entails large uncertainty in tropical regions. With their wall‐to‐wall coverage ability, passive remote sensing signals are frequently used to extrapolate field estimates of forest aboveground biomass (AGB). However, studies often use limited reference data and/or flawed validation schemes and thus report unreliable extrapolation error estimates. Here, we compared the ability of three medium‐ to high‐resolution passive satellite sensors, Landsat‐8 (L8), Sentinel‐2B (S2) and Worldview‐3 (WV3), to map AGB in a forest landscape of Thailand. We used a large airborne LiDAR‐derived AGB dataset as a reference to train and validate a random forest algorithm and conducted robust error assessments and variable selection using spatialized cross‐validations. Our results indicate that the selected predictors strongly varied among the three sensors and between analyses restricted to low (≤200 Mg ha−1) and high (>200 Mg ha−1) AGB areas. WV3 and S2 data outperformed L8 data to extrapolate AGB (RMSE of 68 and 72 against 84 Mg ha−1, respectively) due to the inclusion of the red‐edge band and, probably, to their higher spatial and spectral resolution. Sensitivity to large AGB values was higher for WV3 than for S2 and L8 with saturation point of 247 Mg ha−1 against 204 and 192 Mg ha−1. AGB values above these saturation points remained poorly predictable, especially for L8, indicating that several tropical forest AGB maps should be interpreted with extreme caution. However, predicted gradients of lower AGB values (≤200 Mg ha−1), i.e., in early forest successional stages, were fairly consistent among sensors (r > 0.70), even if the mean absolute difference between estimates was large when AGB predictions were extrapolated out of the calibration area at regional level (34%). We finally showed that calibrating the model only within the sensitivity AGB domain (e.g., <200 Mg ha−1) minimizes the risk of induced bias for estimating small AGB values. These results provide important benchmarks for interpreting previously published maps and to improve future validation schemes.

Prediction of Biomass in Dry Tropical Forests: An Approach on the Importance of Total Height in the Development of Local and Pan-tropical Models

ABSTRACT Dry tropical forests in arid lands cover large areas in Brazil, but few studies report the total biomass stock showing the importance of height measurements, in addition to applying and comparing local and pan-tropical models. Here, we use a biomass data set of 500 trees and shrubs, covering 15 species harvested in a management plan in the state of Pernambuco, in Brazil. We seek to develop local models and compare them with the equations traditionally applied to dry forests – showing the importance of tree height measurements. Due to the non-linear relationships with the independent variables of the tree, we used a nonlinear least squares modeling technique when adjusting models, we adopted the cross-validation procedure. In summary, our above-ground biomass data set is best represented by the Schumacher-Hall equation: exp [3.5336 + 1.9126 × log (D) + 1.2438 × log (Ht)], which shows that height measurements are essential to estimate accurately biomass. The biggest prediction errors observed when testing pan-tropical models in our data demonstrated the importance of developing new local models and indicated that careful considerations should be made if generic “pan-tropical” models without height measurements are planned for application in dry forests in Brazil.

Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.

Mapping data gaps to estimate biomass across Brazilian Amazon forests

BackgroundTropical forests play a fundamental role in the provision of diverse ecosystem services, such as biodiversity, climate and air quality regulation, freshwater provision, carbon cycling, agricultural support and culture. To understand the role of forests in the carbon balance, aboveground biomass (AGB) estimates are needed. Given the importance of Brazilian tropical forests, there is an urgent need to improve AGB estimates to support the Brazilian commitments under the United Nations Framework Convention on Climate Change (UNFCCC). Many AGB maps and datasets exist, varying in availability, scale and coverage. Thus, stakeholders, policy makers and scientists must decide which AGB product, dataset or combination of data to use for their particular goals. In this study, we assessed the gaps in the spatial AGB data across the Brazilian Amazon forests not only to orient the decision makers about the data that are currently available but also to provide a guide for future initiatives.ResultsWe obtained a map of the gaps in the forest AGB spatial data for the Brazilian Amazon using statistics and differences between AGB maps and a spatial multicriteria evaluation that considered the current AGB datasets. The AGB spatial data gap map represents areas with good coverage of AGB data and, consequently, the main gaps or priority areas where further biomass assessments should focus, including the northeast of Amazon State, Amapá and northeast of Pará. Additionally, by quantifying the variability in both the AGB maps and field data on multiple environmental factors, we provide valuable elements for understanding the current AGB data as a function of climate, soil, vegetation and geomorphology.ConclusionsThe map of AGB data gaps could become a useful tool for policy makers and different stakeholders working on National Communications, Reducing Emissions from Deforestation and Degradation (REDD+), or carbon emissions modeling to prioritize places to implement further AGB assessments. Only 0.2% of the Amazon biome forest is sampled, and extensive effort is necessary to improve what we know about the tropical forest.

Dynamic Changes of Aboveground Biomass of Vegetation in Qaidam Basin

In order to monitor the temporal and spatial dynamics of vegetation in the Qaidam Basin, the ground-based vegetation biomass model of the basin was established by using basin ground measurements data during 2016-2018 and NDVI of MOD13Q1: Y=0.0002X2-0.863X+2052.4(R2=0.7656, P<0.01), and using this model to estimate the long-term aboveground biomass data set of the vegetation of basin. Above-ground biomass of vegetation is mainly low-yield vegetation of less than 1500 kg/hm2, followed by vegetation of 1500-3000 kg/hm2, the area of which accounts for less than 10% of the total vegetation area, and the vegetation of the other levels is very small, only scattered, High-yield vegetation which is greater than 6000 kg/hm2 is distributed in the southeast of the basin and in the alpine mountains around the basin

Development of aboveground mangrove forests’ biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic

Southeast Asia (SEA) has the largest mangrove forest area in the world, which plays an important role in the global carbon cycle and is helping to mitigate climate change. In order to manage the mangrove forests in SEA, their total biomass needs to be determined. However, development of a biomass dataset based on field survey is time consuming. An aboveground biomass (AGB) dataset of mangrove forests was developed for SEA based on ALOS PALSAR 25-m mosaic. Specifically, ALOS-PALSAR 25-m images were first retrieved for SEA from the Kyoto and Carbon Initiative projects and then converted from a digital number to a normalized radar cross-section format in decibels. Samples of mangrove forests in SEA were collected as regions of interest from ALOS PALSAR data based on visual interpretation using Landsat data and Google Earth imagery. A rule-based classification method based on mangrove backscattering characteristics was then used to classify mangroves and nonmangroves in the region. Subsequently, an empirical model was adopted to estimate the AGB of the mangrove forests and an AGB dataset was developed. The results indicate that the spatial distribution of mangrove forests over SEA is 5.1 million hectares, and the estimated average AGB is 140.5 ± 136.1 Mg / ha.

What is the role of perennial plants in semi-steppe rangelands? Direct and indirect effects of perennial on annual plant species

Biodiversity–productivity relationships have widely been studied in natural herbaceous communities but remain debated. Here, we hypothesized that species richness of perennial plants would increase species richness of annual plants but aboveground biomass of perennial plants would restrict the species richness and aboveground biomass of annual plants across plant functional types (i.e. shrubs, forbs and graminoids) in natural rangelands. We quantified the species richness of annual and perennial plants across plant functional types, and then measured aboveground biomass destructively in 735 quadrats of semi-steppe rangelands. We used linear path analysis model to test for the multivariate effects of perennial on annual plant species using species richness and aboveground biomass datasets, in addition to the influences of grazing disturbances and soil textural properties. Our results showed the species richness of perennial plants (forbs and graminoids) generally promoted the species richness of annual plants (forbs and graminoids). However, the aboveground biomass of perennial species (shrubs, forbs and graminoids) imposed negative or negligible (i.e. nonsignificant negative) influences on the species richness and aboveground biomass of annual species (forbs and graminoids). Hence, perennial species have probably restricted the aboveground biomass of annual plant species due to the resource acquisition, but protected annual plants from grazing and other anthropogenic disturbances. This study suggests that the relationship between species richness and aboveground biomass is positive in general, regardless of the confounding effects of perennial on annual species across plant functional types. This study highlights that simply by integrative modelling of perennial and annual plant species across plant functional types for the associations between species richness and aboveground biomass, better insights can be gained into ecological mechanisms playing a role in the biodiversity conservation of natural rangelands and other natural ecosystems in general.

Investigating spatial error structures in continuous raster data

The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks the possible spatial variation in error because such diagnostics report only average error or deviation between predicted and reference values. In this respect, this work uses a moving window (kernel) approach to generate geographically weighted (GW) versions of the mean signed deviation, the mean absolute error and the root mean squared error and to quantify their spatial variations. Such approach computes local error diagnostics from data weighted by its distance to the centre of a moving kernel and allows to map spatial surfaces of each type of error. In addition, a GW correlation analysis between predicted and reference values provides an alternative view of local error. These diagnostics are applied to two earth observation case studies. The results reveal important spatial structures of error and unusual clusters of error can be identified through Monte Carlo permutation tests. The first case study demonstrates the use of GW diagnostics to fractional impervious surface area datasets generated by four different models for the Jakarta metropolitan area, Indonesia. The GW diagnostics reveal where the models perform differently and similarly, and found areas of under-prediction in the urban core, with larger errors in peri-urban areas. The second case study uses the GW diagnostics to four remotely sensed aboveground biomass datasets for the Yucatan Peninsula, Mexico. The mapping of GW diagnostics provides a means to compare the accuracy of these four continuous raster datasets locally. The discussion considers the relative nature of diagnostics of error, determining moving window size and issues around the interpretation of different error diagnostic measures. Investigating spatial structures of error hidden in conventional diagnostics of error provides informative descriptions of error in continuous raster data.

An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa

Abstract. The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R &gt; 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse ( ∼ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass.

Upscaling coniferous forest above-ground biomass based on airborne LiDAR and satellite ALOS PALSAR data

Forest above-ground biomass (AGB) is an important indicator for understanding the global carbon cycle. It is hard to obtain a geographically and statistically representative AGB dataset, which is limited by unpredictable environmental conditions and high economical cost. A spatially explicit AGB reference map was produced by airborne LiDAR data and calibrated by field measurements. Three different sampling strategies were designed to sample the reference AGB, PALSAR backscatter, and texture variables. Two parametric and four nonparametric models were established and validated based on the sampled dataset. Results showed that random stratified sampling that used LiDAR-evaluated forest age as stratification knowledge performed the best in the AGB sampling. The addition of backscatter texture variables improved the parametric model performance by an R2 increase of 21% and a root-mean-square error (RMSE) decrease of 10 Mg ha−1. One of the four nonparametric models, namely, the random forest regression model, obtained comparable performance (R2=0.78, RMSE=14.95 Mg ha−1) to the parametric model. Higher estimation errors occurred in the forest stands with lower canopy cover or higher AGB levels. In conclusion, incorporating airborne LiDAR and PALSAR data was proven to be efficient in upscaling the AGB estimation to regional scale, which provides some guidance for future forest management over cold and arid areas.