Efficient assessment of topographic solar radiation to improve plant distribution models

Piedallu, Christian; Gégout, Jean‐Claude

doi:10.1016/j.agrformet.2008.06.001

Cited by 57 publications

(32 citation statements)

References 47 publications

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“…The last two variables were estimated using the function ''Area Solar Radiation'' in ArcGis 9.2. This tool is generally used to investigate the effects of local topography (altitude, slope, exposure, mask effect) on solar radiation levels available to the vegetation (Batlles et al, 2008;Petersen and Tamzen, 2008;Piedallu and Gé gout, 2008). We used this feature to quantify the ''masking'' effect of the forest canopy surrounding the logging gap and its influence on the quantity of solar radiation available to seedlings.…”

Section: The Availability Of Lightmentioning

confidence: 99%

Enrichment of logging gaps with moabi (Baillonella toxisperma Pierre) in a Central African rain forest

Doucet

Kouadio

Monticelli

et al. 2009

Forest Ecology and Management

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Section: The Availability Of Lightmentioning

confidence: 99%

Enrichment of logging gaps with moabi (Baillonella toxisperma Pierre) in a Central African rain forest

Doucet

Kouadio

Monticelli

et al. 2009

Forest Ecology and Management

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“…For these studies, the availability of accurate environmental descriptors is of crucial importance (Dirnböck et al, 2002). Most of the time, climatic factors are considered alone because of their availability in large climatological datasets or because geographic information systems (GIS) programs allow their calculation (Piedallu and Gegout, 2008). Although soil factors have been recognized for their importance (Coudun et al, 2006), they are poorly used as input in predictive models (Guisan and Zimmermann, 2000), particularly for broad areas, due to the high cost and long duration of fieldwork required to obtain relevant data.…”

Section: Introductionmentioning

confidence: 99%

Mapping soil water holding capacity over large areas to predict potential production of forest stands

et al. 2011

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Ingrid Seynave. Mapping soil water holding capacity over large areas to predict the potential production of forest stands. Geoderma, Elsevier, 2011, 160, pp.355-366. 10 AbstractEcological studies need environmental descriptors to establish the response of species or communities to ecological conditions. Soil water resource is an important factor but is poorly used by plant ecologists because of the lack of accessible data. We explore whether a large number of plots with basic soil information collected within the framework of forest inventories allows the soil water holding capacity (SWHC) to be mapped with enough accuracy to predict tree species growth over large areas. We first compared the performance of available pedotransfer functions (PTFs) and showed significant differences in the prediction quality of SWHC between the PTFs selected.We also showed that the most efficient class PTFs and continuous PTFs compared had similar performance, but there was a significant reduction in efficiency when they were applied to soils different from those used to calibrate them. With a root mean squared error (RMSE) of 0.046 cm 3 cm -3 (n = 227 horizons), we selected the Al Majou class PTFs to predict the SWHC in the soil horizons described in every plot, thus allowing 84% of SWHC variance to be explained in soils free of stone (n = 63 plots). Then, we estimated the soil water holding capacity by integrating the stone content collected at the soil pit scale (SWHC') and both the stone content at the soil pit scale and rock outcrop at the plot scale (SWHC") for the 100.307 forest plots recorded in France within the framework of forest inventories. The SWHC" values were interpolated by kriging to produce a map with 1 km² cell size, with a wider resolution leading to a decrease in map accuracy. The SWHC" given by the map ranged from 0 to 148 mm for a soil down to 1 m depth. The RMSE between map values and plot estimates was 33.9 mm, the best predictions being recorded for soils developed on marl, clay, and hollow silicate rocks, and in flat areas. Finally, the ability of SWHC' and SWHC" to predict 3 height growth for Fagus sylvatica, Picea abies and Quercus petraea is discussed. We show a much better predictive ability for SWHC" compared to SWHC'. The values of SWHC" extracted from the map were significantly related to tree height growth. They explained 10.7% of the height growth index variance for Fagus sylvatica (n = 866), 14.1% for Quercus petraea (n = 877) and 10.3% for Picea abies (n = 2067). The proportions of variance accounted by SWHC" were close to those recorded with SWHC" values estimated from the plots (11.5, 11.7, and 18.6% for Fagus sylvatica, Quercus petraea and Picea abies, respectively). We conclude that SWHC" can be mapped using basic soil parameters collected from plots, the predictive ability of the map and of data derived from the plot being close. Thus, the map could be used just as well for small areas as for large areas, directly or indirectly through water balance indices, to predict forest growth an...

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“…The TWI is a model of potential surface moisture, based on topographic position (Beven and Kirkby, 1979). Both the TRASP (Piedallu and Gégout, 2008;Bright et al, 2012) and the TWI (Thompson et al, 2015) have proven useful for modelling forest conditions.…”

Section: Ancillary Spatial Layersmentioning

confidence: 99%

Integration of Landsat time series and field plots for forest productivity estimates in decision support models

Boisvenue

Smiley

White

et al. 2016

Forest Ecology and Management

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Efficient assessment of topographic solar radiation to improve plant distribution models

Cited by 57 publications

References 47 publications

Enrichment of logging gaps with moabi (Baillonella toxisperma Pierre) in a Central African rain forest

Enrichment of logging gaps with moabi (Baillonella toxisperma Pierre) in a Central African rain forest

Mapping soil water holding capacity over large areas to predict potential production of forest stands

Integration of Landsat time series and field plots for forest productivity estimates in decision support models

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