Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses to global change. Within very large plots (median size 25ha), all stems 1cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25 degrees S-61 degrees N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 degrees C), changes in precipitation (up to +/- 30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8g Nm(-2)yr(-1) and 3.1g Sm(-2)yr(-1)), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS-ForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

/pdf/ctfs-forestgeo-a-worldwide-network-monitoring-forests-in-an-2z2d2x7id0.pdf

CTFS-ForestGEO: A worldwide network monitoring forests in an era of global change

Aim: To examine the contribution of large-diameter trees to biomass, stand structure, and species richness across forest biomes. Location: Global. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: We examined the contribution of large trees to forest density, richness and biomass using a global network of 48 large (from 2 to 60 ha) forest plots representing 5,601,473 stems across 9,298 species and 210 plant families. This contribution was assessed using three metrics: the largest 1% of trees >= 1 cm diameter at breast height (DBH), all trees >= 60 cm DBH, and those rank-ordered largest trees that cumulatively comprise 50% of forest biomass. Results: Averaged across these 48 forest plots, the largest 1% of trees >= 1 cm DBH comprised 50% of aboveground live biomass, with hectare-scale standard deviation of 26%. Trees >= 60 cm DBH comprised 41% of aboveground live tree biomass. The size of the largest trees correlated with total forest biomass (r(2) 5.62, p < .001). Large-diameter trees in high biomass forests represented far fewer species relative to overall forest richness (r(2) = 5.45, p < .001). Forests with more diverse large-diameter tree communities were comprised of smaller trees (r(2) = 5.33, p < .001). Lower large-diameter richness was associated with large-diameter trees being individuals of more common species (r(2) =5.17, p=5.002). The concentration of biomass in the largest 1% of trees declined with increasing absolute latitude (r(2) = 5.46, p < .001), as did forest density (r(2) = 5.31, p < .001). Forest structural complexity increased with increasing absolute latitude (r(2) = 5.26, p < .001). Main conclusions: Because large-diameter trees constitute roughly half of the mature forest biomass worldwide, their dynamics and sensitivities to environmental change represent potentially large controls on global forest carbon cycling. We recommend managing forests for conservation of existing large-diameter trees or those that can soon reach large diameters as a simple way to conserve and potentially enhance ecosystem services.

https://www.fs.fed.us/rm/pubs_journals/2018/rmrs_2018_lutz_j001.pdf

Global importance of large‐diameter trees

The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was −0.05 petagrams of C per year (Pg C yr−1) associated with drying trends, and a net change of −0.02 Pg C yr−1 was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area.

/pdf/satellite-passive-microwaves-reveal-recent-climate-induced-bawwykighq.pdf

Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands.

https://hal.archives-ouvertes.fr/hal-03272369/document

An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR

The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80–120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

/pdf/local-spatial-structure-of-forest-biomass-and-its-28kzvmpxrj.pdf

R. H. S. Fernando

Papers

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks