Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m², 400 m² and 1000 m²) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m² to 1000 m² (R² = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.

Litterfall production plays a fundamental role in the dynamics and function of tropical forest ecosystems, as it supplies 70–80% of nutrients entering the soil. This process varies annually and seasonally, depending on multiple environmental factors. However, few studies spanning several years have addressed the combined effect of climate variables, successional age, topography, and vegetation structure in tropical dry forests. In this study, we evaluated monthly, seasonal, and annual litterfall production over a five-year period in semideciduous dry forests of different successional ages growing on contrasting topographic conditions (sloping or flat terrain) in Yucatan, Mexico. Its relationship with climate and vegetation structural variables were also analyzed using multiple linear regression and generalized linear models. Litterfall was measured monthly in 12 litterfall traps of 0.5 m² in three sampling clusters (sets of four 400 m² sampling plots) established in forests of five successional age classes, 3–5, 10–17, 18–25, 60–79, and >80 years (in the latter two classes either on slopping or on flat terrain), for a total of 15 sampling clusters and 180 litterfall traps. Litterfall production varied between years (negatively correlated with precipitation), seasons (positively correlated with wind speed and maximum temperature), and months (negatively correlated with relative humidity) and was higher in flat than in sloping sites. Litterfall production also increased with successional age until 18–25 years after abandonment, when it attained values similar to those of mature forests. It was positively correlated with the aboveground biomass of deciduous species but negatively correlated with the basal area of evergreen species. Our results show a rapid recovery of litterfall production with successional age of these forests, which may increase with climate changes such as less precipitation, higher temperatures, and higher incidence of hurricanes.

In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R² = 0.56 to R² = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery.