1] Data from spaceborne light detection and ranging (lidar) opens the possibility to map forest vertical structure globally. We present a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite). A challenge in the use of GLAS data for global vegetation studies is the sparse coverage of lidar shots (mean = 121 data points/degree 2 for the L3C campaign). However, GLAS-derived canopy height (RH100) values were highly correlated with other, more spatially dense, ancillary variables available globally, which allowed us to model global RH100 from forest type, tree cover, elevation, and climatology maps. The difference between the model predicted RH100 and footprint level lidar-derived RH100 values showed that error increased in closed broadleaved forests such as the Amazon, underscoring the challenges in mapping tall (>40 m) canopies. The resulting map was validated with field measurements from 66 FLUXNET sites. The modeled RH100 versus in situ canopy height error (RMSE = 6.1 m, R 2 = 0.5; or, RMSE = 4.4 m, R 2 = 0.7 without 7 outliers) is conservative as it also includes measurement uncertainty and sub pixel variability within the 1-km pixels. Our results were compared against a recently published canopy height map. We found our values to be in general taller and more strongly correlated with FLUXNET data. Our map reveals a global latitudinal gradient in canopy height, increasing towards the equator, as well as coarse forest disturbance patterns.

angroves are forested wetlands that represent a functional link between the terrestrial and oceanic carbon cycles 1 , storing up to four times as much carbon per unit area in comparison to terrestrial forest ecosystems 2 . Mangroves contribute an estimated 10-15% of the global carbon storage in the coastal ocean, with ~50% of mangrove litterfall production being transported to adjacent coastal zones and accounting for 10-11% of the global export of particulate terrestrial carbon to the ocean 3,4 . Furthermore, mangrove forests provide a wealth of ecosystem services to coastal communities, including habitat for fisheries, firewood and timber, all valuable resources in local markets 5 . Despite this, mangroves are impacted by anthropogenically driven disturbances such as deforestation, conversion to aquaculture and urban development [6][7][8] , and coastline transgression due to relative sea level rise [9][10][11] . Recent estimates of global mangrove loss rates range between 0.16% and 0.39% annually, and may be up to 8.08% in Southeast Asia 12 . As a consequence, large amounts of previously stored carbon may be released into the atmosphere, contributing substantially to net global carbon emissions [13][14][15] .Global mangrove carbon stocks 2 and aboveground biomass (AGB) 16,17 have been estimated previously, providing AGB values derived from climate-based 16 or latitudinal relationships 17 . The spatially explicit distribution in forest structural attributes such as mangrove canopy height is rarely considered in these estimates. Mangrove canopy height is highly correlated with carbon turnover via leaf or litterfall production 18 and is therefore an important variable in quantifying contemporary global aboveground productivity and carbon sequestration rates. Productivity and forest structure are controlled by local environmental gradients (for example, nutrient availability and salinity) and hydrology 19,20 , along with regional climate and geomorphology 17,[19][20][21][22] , resulting in a range of mangrove ecotypes, from scrub (< 3 m) to tall (> 15 m) forest stands [23][24][25] . Here, we produce global maps of mangrove canopy height and AGB derived from space-borne remote sensing data and in situ measurements, to perform a global analysis of the spatial patterns and variability in mangrove forest structure.

For the period 1996-2010, we provide the first indication of the drivers behind mangrove land cover and land use change across the (pan-)tropics using time-series Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) and Advanced Land Observing Satellite (ALOS) Phased Array-type L-band SAR (PALSAR) data. Multi-temporal radar mosaics were manually interpreted for evidence of loss and gain in forest extent and its associated driver. Mangrove loss as a consequence of human activities was observed across their entire range. Between 1996-2010 12% of the 1168 1°x1° radar mosaic tiles examined contained evidence of mangrove loss, as a consequence of anthropogenic degradation, with this increasing to 38% when combined with evidence of anthropogenic activity prior to 1996. The greatest proportion of loss was observed in Southeast Asia, whereby approximately 50% of the tiles in the region contained evidence of mangrove loss, corresponding to 18.4% of the global mangrove forest tiles. Southeast Asia contained the greatest proportion (33.8%) of global mangrove forest. The primary driver of anthropogenic mangrove loss was found to be the conversion of mangrove to aquaculture/agriculture, although substantial advance of mangroves was also evident in many regions.

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO₂ seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO₂), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks