Wendell P. Cropper

Simulation of the carbon dynamics of a Florida slash pine plantation

Abstract A model of the carbon dynamics of a Florida slash pine ( Pinus elliottii ) plantation was designed to use as a tool to scale up physiological data to the stand level, and to address questions about the dynamics of the labile carbon pool (starch + sugars) and fertilization responses. The model adequately simulated labile carbon dynamics, stem growth, and root respiration over a 2-year period. Measured labile carbon pool dynamics were consistent with the simulation based on a daily balance between inputs of net canopy assimilation and outputs of respiration and growth. Increased stem growth following fertilization can be simulated as a consequence of increased foliage mass; no other changes in allocation patterns or physiological responses to fertilization were evident.

Computer simulation of long-term carbon storage patterns in Florida slash pine plantations

Abstract We developed a computer model to simulate carbon storage in managed slash pine plantations in Florida. The model is based on ecosystem level carbon dynamics and land use data structured by age class of planted trees. Based on a scenario of constant planting at the average level (1952–1979), Florida slash pine forests would store carbon at the rate of 2.81 × 10 6 t year −1 . However, simulated carbon storage is sensitive to the age class distribution of the forests. There will be a 10 6 t year −1 decrease in carbon storage between 1980 and 1987, due to reduced levels of planting during the past decade. This trend is relatively independent of current planting levels.

Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States

Understanding ecosystem processes and the influence of regional scale drivers can provide useful information for managing forest ecosystems. Examining more local scale drivers of forest biomass and water yield can also provide insights for identifying and better understanding the effects of climate change and management on forests. We used diverse multi-scale datasets, functional models and Geographically Weighted Regression (GWR) to model ecosystem processes at the watershed scale and to interpret the influence of ecological drivers across the Southeastern United States (SE US). Aboveground forest biomass (AGB) was determined from available geospatial datasets and water yield was estimated using the Water Supply and Stress Index (WaSSI) model at the watershed level. Our geostatistical model examined the spatial variation in these relationships between ecosystem processes, climate, biophysical, and forest management variables at the watershed level across the SE US. Ecological and management drivers at the watershed level were analyzed locally to identify whether drivers contribute positively or negatively to aboveground forest biomass and water yield ecosystem processes and thus identifying potential synergies and tradeoffs across the SE US region. Although AGB and water yield drivers varied geographically across the study area, they were generally significantly influenced by climate (rainfall and temperature), land-cover factor1 (Water and barren), land-cover factor2 (wetland and forest), organic matter content high, rock depth, available water content, stand age, elevation, and LAI drivers. These drivers were positively or negatively associated with biomass or water yield which significantly contributes to ecosystem interactions or tradeoff/synergies. Our study introduced a spatially-explicit modelling framework to analyze the effect of ecosystem drivers on forest ecosystem structure, function and provision of services. This integrated model approach facilitates multi-scale analyses of drivers and interactions at the local to regional scale.

Estimating GPS Signal Loss in a Natural Deciduous Forest Using Sky Photography

ABSTRACT Understanding how Global Positioning System (GPS) signals are influenced by vegetation structure allows for the determination of how specific technologies might be affected in certain forest environments. This study presents three different models that predict signal loss in a natural deciduous forest using fisheye photography. Relationships between terrestrial-based hemispherical sky-oriented photo (HSOP) measurements and GPS signal-to-noise ratios (SNRs) are explored. ArcGIS is used for image processing of HSOPs to rapidly estimate canopy closure (CC) at particular angles from zenith in forested areas. The difference between the observed SNR of GPS L-band signals under forest canopies to those observed in the open determines signal loss. CC values at different zenith angles inside the forest during four seasons are used to model signal attenuation. This article presents a canopy closure predictive model (CCPM), a model that includes the CCPM and incorporates the difference between the CC value in any season minus the CC in the winter, and a model that includes a seasonal component. The three models presented in this article yield adjusted R2 values between 0.60 and 0.62 and root mean square error range of 3.21 to 3.28 dB.