Abstract. 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+. 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 (AGB) at spatial grains ranging from 5 to 250 m (0.025–6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100 m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, 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.

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+. 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 (AGB) at spatial grains ranging from 5 to 250 m (0.025-6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100 m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, 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. (Résumé d'auteur

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Question How reliable is the process of delimiting plant species by morphotyping sterile specimens from a highly diverse Amazonian forest plot? Location Biological Dynamics of Forest Fragments Project (BDFFP), Central Amazon, Manaus, Brazil. Methods A taxonomic exercise was conducted during a Center for Tropical Forest Science (CTFS) Taxonomy Workshop held in Manaus in April 2011, using specimens collected in a 25‐ha forest plot. The plant species from this plot had been previously delimited by morphotyping of ca. 80 000 sterile specimens, a process that resulted in the recognition of 115 cases (accounting for 38% of all trees) in which species delimitation was problematic. For the workshop, we selected a subsample of specimens for eight of these difficult cases (taxonomic groups/complexes) and asked 14 participants with different levels of botanical training to independently sort these specimens into morphospecies. We then compared the classifications made by all participants and explored correlations between botanical training and plant classification. Results The classification of specimens into morphospecies was highly variable among participants, except for one taxonomic group/complex, for which the median pair‐wise similarity was 95%. For the other seven taxonomic groups/complexes, median pair‐wise similarity values ranged from 52% to 67%. Training did not increase the similarity in the definition of morphospecies except for two taxonomic groups/complexes, for which there was higher congruence between the classifications made by participants with a high level of botanical training than in comparisons that included less‐experienced participants. The total number of morphospecies defined by participants was highly variable for all taxonomic groups/complexes, with the total number varying from 12 to 46 (a 383% difference). Conclusions Local plant species delimitation by morphotyping sterile specimens is prone to large uncertainties, and botanical training may not reduce them. We argue that uncertainty in species delimitation should be explicitly considered in plant biodiversity inventories as diversity estimates may be strongly affected by such uncertainties. We recommend that species delimitation and identification be treated as separate processes and that difficulties be explicitly recorded, so as to permit error estimates and the refinement of taxonomic data.

Distribution patterns of canopy and understory tree species at local scale in a Tierra Firme forest, the Colombian Amazonia. The effect of environmental variation on the structure of tree communities in tropical forests is still under debate. There is evidence that in landscapes like Tierra Firme forest, where the environmental gradient decreases at a local level, the effect of soil on the distribution patterns of plant species is minimal, happens to be random or is due to biological processes. In contrast, in studies with different kinds of plants from tropical forests, a greater effect on floristic composition of varying soil and topography has been reported. To assess this, the current study was carried out in a permanent plot of ten hectares in the Amacayacu National Park, Colombian Amazonia. To run the analysis, floristic and environmental variations were obtained according to tree species abundance categories and growth forms. In order to quantify the role played by both environmental filtering and dispersal limitation, the variation of the spatial configuration was included. We used Detrended Correspondence Analysis and Canonical Correspondence Analysis, followed by a variation partitioning, to analyze the species distribution patterns. The spatial template was evaluated using the Principal Coordinates of Neighbor Matrix method. We recorded 14 074 individuals from 1 053 species and 80 families. The most abundant families were Myristicaceae, Moraceae, Meliaceae, Arecaceae and Lecythidaceae, coinciding with other studies from Northwest Amazonia. Beta diversity was relatively low within the plot. Soils were very poor, had high aluminum concentration and were predominantly clayey. The floristic differences explained along the ten hectares plot were mainly associated to biological processes, such as dispersal limitation. The largest proportion of community variation in our dataset was unexplained by either environmental or spatial data. In conclusion, these results support random processes as the major drivers of the spatial variation of tree species at a local scale on Tierra Firme forests of Amacayacu National Park, and suggest reserve´s size as a key element to ensure the conservation of plant diversity at both regional and local levels. Rev. Biol. Trop. 62 (1): 373-383. Epub 2014 March 01.

<p><strong>Abstract.</strong> An assessment on the amount and spatial distribution of forest aboveground biomass (AGB) for the forests in Colombia was generated using in-situ national forest inventory data (IDEAM, 2018), in combination with multispectral optical data and synthetic aperture radar (SAR) satellite imagery. ALOS-2 PALSAR-2 gamma-0 backscatter annual mosaics (2015&amp;ndash;2017) provided by JAXA were normalised and corrected using previous ALOS PALSAR annual mosaics (2007&amp;ndash;2010) as reference. A multi-temporal Landsat 7 &amp;amp; 8 composite over the whole of Colombia was used for the year 2016&amp;thinsp;&amp;plusmn;&amp;thinsp;1. The national forest inventory in-situ plots used to train our model consisted of 5-subplots each and were collected during the period 2015&amp;ndash;2017 in the main biomes of the country. A sample of permanent 1ha plots (PPMs) were also measured. Nationally developed allometries (Alvarez et al., 2012) were used to estimate AGB. A non-parametric random forests (RF) algorithm was used within a k-fold framework to retrieve AGB at 30&amp;thinsp;m spatial resolution for the whole of Colombia. The algorithm was trained using forest inventory plots and validated at plot (0.35&amp;thinsp;ha) and PPM level (1&amp;thinsp;ha). The accuracy assessment found coefficients of determination (R²) of 0.68 and 0.61, and relative root mean square errors (Rel. RMSE) of 49% and 34% at plot and at PPM level, respectively. The results showed that the average AGB for the country was 118.1&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 (45.6&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 for Caribe, 75.4&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 Andes, 122.5&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 Pacifico, 32.7&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 Orinoquia, and 200.5&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 for the Amazonia, regionally), and that the total carbon stocks for the country were 6.7&amp;thinsp;Pg C for the period 2015&amp;ndash;2017.</p>

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