Abstract
Deciduousness in dry tropical forests results in substantial seasonal changes to canopy gap fractions. The characterization of such structural properties over large areas is necessary for understanding energy and nutrient distribution within forest ecosystems. However, a spatial extrapolation of measurements from relatively few, spatially-concentrated field observations can yield estimated values that have questionable accuracy and precision at regional scales. This paper uses linear regression models to compare measurements of canopy gap fraction from in situ digital cover photography in the dry tropical forest of the Southern Yucatán, Mexico, to measurements of seasonal vegetation change based on three vegetation indices—the Normalized Difference Vegetation Index (NDVI), two-band Enhanced Vegetation Index (EVI2), and the Normalized Difference Water Index (NDWI)—derived from Landsat-7 ETM+ and Landsat-8 Operational Land Imager (OLI) data to gauge the ability of standardized combinations of multispectral reflectance data to accurately describe the intensity of deciduousness that occurs during the dry season. Discrete observations are compared, as well as spatially summarized values at coarser spatial scales. Model R2 values are greater at coarse spatial scales for all vegetation indices. Models of in situ measurements of gap fraction and Landsat NDWI normalized seasonal change exhibit stronger correlation than do models that feature NDVI or EVI2 (R² = 0.751 and Mean Absolute Error = 0.04 after aggregation, R² = 0.552 and MAE = 0.07 for observation-level data). Based on its comparatively strong correlation with field observations, NDWI is adapted to a Moderate Resolution Imaging Spectroradiometer (MODIS) time series and used for spatial extrapolation and the monitoring of canopy conditions. NDWI values derived from MODIS data are regressed against Tropical Rainforest Measuring Misson (TRMM) rainfall data over the period 2000–2011, and the regression results are compared to those of a prior study that used regression to explain the variation of a MODIS EVI using TRMM rainfall data. A MODIS NDWI time series reveals stronger correlation (R² = 0.48 in deciduous forests) with TRMM accumulated (three-month) rainfall data than a MODIS EVI time series. The results indicate that an NDWI time series can accurately describe a variability of canopy leaf abundance during the dry season and could be an alternative basis of long-term monitoring of season phenology in a dry tropical forest.
Highlights
The quantification of forest structural properties such as aboveground biomass, canopy height, and canopy gap fraction is key to understanding the role of these ecosystems in a host of processes [1]; from the global carbon cycle [2], to national-scale natural resource policy [3], and plans for the financial commodification of avoided deforestation [4,5,6]
This paper examines the strength of correlation between in situ measurements of canopy gap fraction at the end of the 2015 dry season and normalized seasonal change (2014–2015) of three Landsat vegetation indices in the dry tropical forest of the Southern Yucatán
The findings suggest that digital cover photography observations can provide measurements of gap fraction aggregated at the site level (100s to 1000s m) that correspond sufficiently closely to metrics of Landsat reflectance to allow the spatial scaling of discrete measurements of canopy gap fraction, and adaptation to spatially-coarse and temporally frequent Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data
Summary
The quantification of forest structural properties such as aboveground biomass, canopy height, and canopy gap fraction is key to understanding the role of these ecosystems in a host of processes [1]; from the global carbon cycle [2], to national-scale natural resource policy [3], and plans for the financial commodification of avoided deforestation [4,5,6]. Satellite remote sensing approaches permit large areal extent forest structural measurement given their ability to observe remote locations, and produce large amounts of observations using standardized data capture techniques and information formats [7] The integration of both in situ and remotely sensed measurements is an ideal way to complement each type of observation, especially for large-area forest assessments [9,10,11]. Many spectral indices of surface reflectance are sensitive to the chemical and biophysical properties of photosynthetic vegetation, and are robust indicators of the abundance and condition of leafy material in the forest canopy [13] Indices such as the Normalized Difference Vegetation Index (NDVI) [14] and two-band Enhanced Vegetation Index (EVI2) [15] that contrast visible and near-infrared reflectance to provide a composite measurement of photosynthetic production and leaf abundance [16]. For applications in deciduous tropical forests, reflectance-based measurements of biophysical greenness can be used to describe canopy conditions, but would not distinguish between greenness of canopy foliage versus that of sub-canopy vegetation [22]
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