Leaf area index measurements in a tropical moist forest: A case study from Costa Rica (original) (raw)
Related papers
Biotropica, 2005
We compared plant area index (PAI) and canopy openness for different successional stages in three tropical dry forest sites: Chamela, Mexico; Santa Rosa, Costa Rica; and Palo Verde, Costa Rica, in the wet and dry seasons. We also compared leaf area index (LAI) for the Costa Rican sites during the wet and dry seasons. In addition, we examined differences in canopy structure to ascertain the most influential factors on PAI/LAI. Subsequently, we explored relationships between spectral vegetation indices derived from Landsat 7 ETM+ satellite imagery and PAI/LAI to create maps of PAI/LAI for the wet season for the three sites. Specific forest structure characteristics with the greatest influence on PAI/LAI varied among the sites and were linked to climatic differences. The differences in PAI/LAI and canopy openness among the sites were explained by both the past land-use history and forest management practices. For all sites, the best-fit regression model between the spectral vegetation indices and PAI/LAI was a Lorentzian Cumulative Function. Overall, this study aimed to further research linkages between PAI/LAI and remotely sensed data while exploring unique challenges posed by this ecosystem.
International Journal of Biometeorology, 2013
Leaf area index (LAI) is a key driver of forest productivity and evapotranspiration; however, it is a difficult and labor-intensive variable to measure, making its measurement impractical for large-scale and long-term studies of tropical forest structure and function. In contrast, satellite estimates of LAI have shown promise for large-scale and long-term studies, but their performance has been equivocal and the biases are not well known. We measured total, overstory, and understory LAI of an Amazon-savanna transitional forest (ASTF) over 3 years and a seasonal flooded forest (SFF) during 4 years using a light extinction method and two remote sensing methods (LAI MODIS product and the Landsat-METRIC method), with the objectives of (1) evaluating the performance of the remote sensing methods, and (2) understanding how total, overstory and understory LAI interact with micrometeorological variables. Total, overstory and understory LAI differed between both sites, with ASTF having higher LAI values than SFF, but neither site exhibited year-to-year variation in LAI despite large differences in meteorological variables. LAI values at the two sites have different patterns of correlation with micrometeorological variables. ASTF exhibited smaller seasonal variations in LAI than SFF. In contrast, SFF exhibited small changes in total LAI; however, dry season declines in overstory LAI were counteracted by understory increases in LAI. MODIS LAI correlated weakly to total LAI for SFF but not for ASTF, while METRIC LAI had no correlation to total LAI. However, MODIS LAI correlated strongly with overstory LAI for both sites, but had no correlation with understory LAI. Furthermore, LAI estimates based on canopy light extinction were correlated positively with seasonal variations in rainfall and soil water content and negatively with vapor pressure deficit and solar radiation; however, in some cases satellite-derived estimates of LAI exhibited no correlation with climate variables (METRIC LAI or MODIS LAI for ASTF). These data indicate that the satellite-derived estimates of LAI are insensitive to the understory variations in LAI that occur in many seasonal tropical forests and the micrometeorological variables that control seasonal variations in leaf phenology. While more groundbased measurements are needed to adequately quantify the performance of these satellite-based LAI products, our data indicate that their output must be interpreted with caution in seasonal tropical forests.
Forest Ecology and Management, 2007
Leaf area index (LAI) is one of the most frequently used parameters for the analysis of canopy structure and it has also been shown to be an important structural characteristic of the forest ecosystem and forest productivity. The objectives of this study were: (1) to calibrate optical estimates of PAI (plant area index) from the LAI-2000 using leaf area index derived from allometric models for six different tropical tree species and (2) to explore the corresponding relationship of calibrated LAI-2000 with stand productivity indices and environmental factors along a strong environmental gradient in the southern region of Costa Rica. From sixteen 6-year-old pure stand plantations (trees spaced 3 m  3 m) of four fast growing native species (Terminalia amazonia, Vochysia ferruginea, Vochysia guatemalensis and Hieronyma alchorneoides) and two introduced species (Pinus caribaea var hondurensis and Gmelina arborea), the plant area index (PAI) was estimated indirectly using the LAI-2000 plant canopy analyzer (LI-COR, Lincoln, NE), under cloudy sky conditions at a fixed height of 1.3 m above the ground with a 458 view cap. In addition, leaf area index (LAI) was estimated allometrically by felling four selected trees and measuring the area and biomass of leaves. The specific leaf area (SLA) showed typical values for each tree species that ranged between 81 cm 2 g À1 (V. ferruginea) and 106 cm 2 g À1 (G. arborea). Based on the characteristic SLA, for all tree species, the leaf area per tree could be estimated by allometric equations using the dbh (diameter at 1.3 m) as the independent variable. The calibration of the LAI-2000 PAI data versus the allometric estimate of leaf area showed strong and unbiased relationship for the species: T. amazonia, V. guatemalensis, H. alchorneoides and P. caribaea. In the case of G. arborea and V. ferruginea the LAI-2000 PAI values underestimated and overestimated the allometric measurements of LAI. For all tree species, calibrated LAI-2000 values were used as an independent variable in highly significant regression equations to estimate dominant tree height in each stand (in m) and stand yield in m 3 ha À1 year À1 , implying that calibrated LAI-2000 can be used to evaluate site quality and stand productivity. The generalized relationships for all species, between average calibrated LAI-2000 with stands yield or dominant tree height among four Eco-regions, indicated that as soil nutrient and water supply become optimal for tree growth, maximum LAI, dominant stand height and yield values are obtained. #
Anais XI Simpósio …, 2003
Neotropical dry forests have been understudied not only from the ecological point of view but also by means of remote sensing techniques. Characteris tics of Neotropical dry forest such as seasonality, high historical anthropogenic disturbance and patchiness through the landscape have made them a difficult ecosystem to map using remotely sensed imagery. In addition, an even harder task is to map secondary succession in dry forests, something that has been fairly successful in Neotropical rain forests (i.e. Amazonian forest). In this paper we propose a technique to detect four different successional stages of tropical dry forest in an area of 49 km 2 in the Santa Rosa National Park (Guanacaste, Costa Rica). We combined very high-resolution imagery (IKONOS) with high-resolution imagery (Landsat ETM+) and tested four vegetations indices: single ratio (SR), normalized difference vegetation index (NDVI), infrared index (IRI) and, mid-infrared index (MIRI). This allowed us to define thresholds for the successional stages based on the tested indices.
Ecology Letters, 2007
Leaf Area Index (leaf area per unit ground area, LAI) is a key driver of forest productivity but has never previously been measured directly at the landscape scale in tropical rain forest (TRF). We used a modular tower and stratified random sampling to harvest all foliage from forest floor to canopy top in 55 vertical transects (4.6 m 2 ) across 500 ha of old growth in Costa Rica. Landscape LAI was 6.00 ± 0.32 SEM. Trees, palms and lianas accounted for 89% of the total, and trees and lianas were 95% of the upper canopy. All vertical transects were organized into quantitatively defined strata, partially resolving the long-standing controversy over canopy stratification in TRF. Total LAI was strongly correlated with forest height up to 21 m, while the number of canopy strata increased with forest height across the full height range. These data are a benchmark for understanding the structure and functional composition of TRF canopies at landscape scales, and also provide insights for improving ecosystem models and remote sensing validation.
Remote Sensing
Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R 2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.
Relationships between forest structure and vegetation indices in Atlantic Rainforest
Forest Ecology and Management, 2005
The alliance between remote sensing techniques and biophysical indicators can be valuable to studies on diagnosis and monitoring, especially in threatened habitats, such as the Atlantic Rainforest. This approach may improve monitoring through diagnosing forest fragments instead of quantifying only forest area reduction. This paper aims to evaluate relationships between forest structure and vegetation indices in Atlantic Rainforest fragments, in southeastern Brazil. Two Landsat 7 ETM+ images acquired in humid and dry seasons were used, and measurements of forest structure in nine forest fragments and in a continuous forest area in the Guapiaçú River Basin, in Rio de Janeiro State were taken. Three vegetation indices (normalized difference vegetation index (NDVI), moisture vegetation index using Landsat's band 5 (MVI5) and moisture vegetation index using Landsat's band 7 (MVI7)) were correlated with measurements of forest structure (frequency of multiple-stemmed trees, density of trees, mean and range of tree diameter, mean and range of tree height and average of basal area). Models describing the relationships between forest structure and vegetation indices using linear regression analysis were also developed. MVI5 and MVI7 showed the best performances in dense humid forests, whereas NDVI seems to be a good indicator of green biomass in deciduous and dry forests. Moreover, the saturation matter in vegetation indices and the transferability of relationships between biophysical characteristics and vegetation indices to other sites and times were discussed.
Biotropica, 2005
There is a growing emphasis on developing methods for quantifying the structure and composition of tropical forests that can be applied over large landscapes, especially for tropical dry forests that are severely fragmented and have a high conservation priority. This study investigates the relationships between various measures of forest structure (annual woody increment, canopy closure, stand density, stand basal area) and composition (tree species diversity, tree community composition) measured in semi-deciduous tropical dry forests on islands in Lago Guri, Venezuela and three spectral indices derived from Landsat ETM+: Normalized Difference Vegetation Index (NDVI), Infrared Index (IRI), and Mid-Infrared Index (MIRI). Even though there were significant autocorrelations among spectral indices, there were significant differences in the relationships between spectral indices and forest attributes. IRI was not significantly correlated with any of the structural variables while MIRI was correlated with canopy closure and NDVI values were correlated with canopy closure as well as annual woody increment. NDVI and MIRI were both related to relative tree diversity and all three indices were associated with aspects of tree species composition. Based on the results of this study, it appears that spectral indices, and in particular NDVI, may be useful indicators of forest attributes in tropical dry forest habitats. Further research needs to be undertaken to identify if the results of this study can be applied to other tropical dry forests at a global spatial scale.
Estimating the leaf area index in Indian tropical forests using Landsat 8 OLI data.pdf
Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρ red ), near-infrared (ρ NIR ), shortwave infrared (ρ SWIR1 , ρ SWIR2 ) reflectance bands (R 2 > 0.6), and all SVIs (R 2 > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R 2 , low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R 2 = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.