David A.J. Ripley

Bio: David A.J. Ripley is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Normalized Difference Vegetation Index & Regional planning. The author has an hindex of 2, co-authored 2 publications receiving 2052 citations.

Land Use Dynamics of Chester County, Pennsylvania, from a Satellite Remote Sensing Perspective

Abstract: The potential for using satellite data in regional planning is suggested by analyses of land cover change for a rapidly urbanizing county in Pennsylvania. Land classification maps can be generated from satellite imagery with relative ease on an annual basis, enabling communities to track the loss of their forested and agricultural lands, and the growth of residential areas and urban centers in a timely manner. Additionally, the satellite enables planners to compare spatially and temporally such environmental indicators of urbanization as surface temperature, vegetation fraction and impervious coverage. Estimates of changes in a region's evaporative energy losses, which can be related to stormwater runoff, are also possible if the satellite data is combined with a surface climate model. Urban planners and environmental agencies can use the demonstrated techniques to monitor their region's microclimate ‐ bearing in mind its implications for human comfort and the creation of sustainable living condi...

A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system

Abstract: This work presents a new dynamic global vegetation model designed as an extension of an existing surface-vegetation-atmosphere transfer scheme which is included in a coupled ocean-atmosphere general circulation model. The new dynamic global vegetation model simulates the principal processes of the continental biosphere influencing the global carbon cycle (photosynthesis, autotrophic and heterotrophic respiration of plants and in soils, fire, etc.) as well as latent, sensible, and kinetic energy exchanges at the surface of soils and plants. As a dynamic vegetation model, it explicitly represents competitive processes such as light competition, sapling establishment, etc. It can thus be used in simulations for the study of feedbacks between transient climate and vegetation cover changes, but it can also be used with a prescribed vegetation distribution. The whole seasonal phenological cycle is prognostically calculated without any prescribed dates or use of satellite data. The model is coupled to the IPSL-CM4 coupled atmosphere-ocean-vegetation model. Carbon and surface energy fluxes from the coupled hydrology-vegetation model compare well with observations at FluxNet sites. Simulated vegetation distribution and leaf density in a global simulation are evaluated against observations, and carbon stocks and fluxes are compared to available estimates, with satisfying results.

Lidar Remote Sensing for Ecosystem Studies

Abstract: Articles R emote sensing has facilitated extraordinary advances in the modeling, mapping, and understanding of ecosystems. Typical applications of remote sensing involve either images from passive optical systems, such as aerial photography and Landsat Thematic Mapper (Goward and Williams 1997), or to a lesser degree, active radar sensors such as RADARSAT (Waring et al. 1995). These types of sensors have proven to be satisfactory for many ecological applications , such as mapping land cover into broad classes and, in some biomes, estimating aboveground biomass and leaf area index (LAI). Moreover, they enable researchers to analyze the spatial pattern of these images. However, conventional sensors have significant limitations for ecological applications. The sensitivity and accuracy of these devices have repeatedly been shown to fall with increasing aboveground biomass and leaf area index (Waring et al. 1995, Carlson and Ripley 1997, Turner et al. 1999). They are also limited in their ability to represent spatial patterns: They produce only two-dimensional (x and y) images, which cannot fully represent the three-dimensional structure of, for instance, an old-growth forest canopy.Yet ecologists have long understood that the presence of specific organisms, and the overall richness of wildlife communities, can be highly dependent on the three-dimensional spatial pattern of vegetation (MacArthur and MacArthur 1961), especially in systems where biomass accumulation is significant (Hansen and Rotella 2000). Individual bird species, in particular, are often associated with specific three-dimensional features in forests (Carey et al. 1991). In addition, other functional aspects of forests, such as productivity, may be related to forest canopy structure. Laser altimetry, or lidar (light detection and ranging), is an alternative remote sensing technology that promises to both increase the accuracy of biophysical measurements and extend spatial analysis into the third (z) dimension. Lidar sensors directly measure the three-dimensional distribution of plant canopies as well as subcanopy topography, thus providing high-resolution topographic maps and highly accurate estimates of vegetation height, cover, and canopy structure. In addition , lidar has been shown to accurately estimate LAI and aboveground biomass even in those high-biomass ecosystems where passive optical and active radar sensors typically fail to do so. The basic measurement made by a lidar device is the distance between the sensor and a target surface, obtained by determining the elapsed time between the emission of a short-duration laser pulse and the arrival of the reflection of that pulse (the return signal) at the sensor's receiver. Multiplying this …