Andrew K. Skidmore

Effects of fire and herbivory on the stability of savanna ecosystems

Savanna ecosystems are characterized by the co-occurrence of trees and grass- es. In this paper, we argue that the balance between trees and grasses is, to a large extent, determined by the indirect interactive effects of herbivory and fire. These effects are based on the positive feedback between fuel load (grass biomass) and fire intensity. An increase in the level of grazing leads to reduced fuel load, which makes fire less intense and, thus, less damaging to trees and, consequently, results in an increase in woody vegetation. The system then switches from a state with trees and grasses to a state with solely trees. Similarly, browsers may enhance the effect of fire on trees because they reduce woody biomass, thus indirectly stimulating grass growth. This consequent increase in fuel load results in more intense fire and increased decline of biomass. The system then switches from a state with solely trees to a state with trees and grasses. We maintain that the interaction between fire and herbivory provides a mechanistic explanation for observed discontinuous changes in woody and grass biomass. This is an alternative for the soil degradation mechanism, in which there is a positive feedback between the amount of grass biomass and the amount of water that infiltrates into the soil. The soil degradation mechanism predicts no discontinuous chang- es, such as bush encroachment, on sandy soils. Such changes, however, are frequently ob- served. Therefore, the interactive effects of fire and herbivory provide a more plausible explanation for the occurrence of discontinuous changes in savanna ecosystems.

Spectral discrimination of vegetation types in a coastal wetland

Abstract Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra. For this purpose, the field reflectance spectra of 27 saltmarsh vegetation types of the Dutch Waddenzee wetland were analysed in three steps. Prior to analysis, the spectra were smoothed with an innovative wavelet approach. In the first stage of the analysis, the reflectance spectra of the vegetation types were tested for differences between type classes. It was found that the reflectance spectra of saltmarsh vegetation types are statistically significantly different for various spectral regions. Secondly, it was tested whether this statistical difference could be enhanced by using continuum removal as a normalisation technique. For vegetation spectra, continuum removal improves the statistical difference between vegetation types in the visible spectrum, but weakens the statistical difference of the spectra in the near-infrared and shortwave infrared part of the spectrum. Thirdly, after statistical differences were found, it was determined how distant in spectral space the vegetation type classes were from each other, using the Bhattacharyya (BH) and the Jeffries–Matusita (JM) distance measures. We selected six wavelengths for this, based on the statistical analysis of the first step. The potential of correct classification of the saltmarsh vegetation types using hyperspectral remote sensing is predicted by these distance measures. It is concluded that the reflectance of vegetation types is statistically different. With high quality radiometric calibration of hyperspectral imagery, it is anticipated that vegetation species may be identified from imagery using spectral libraries that were measured in the field during the time of image acquisition.

Modelling topographic variation in solar radiation in a GIS environment

Clear sky shortwave solar radiation varies in response to altitude and elevation, surface gradient (slope) and orientation (aspect), as well as position relative to neighbouring surfaces. While the...

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350 nm and 2500 nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R 2=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750 nm) and longer wavelengths of the red edge (750–780 nm), yielded higher correlations with biomass (mean R 2=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R 2=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.

A comparison of techniques for calculating gradient and aspect from a gridded digital elevation model

Abstract Digital elevation data spaced on a regular 30 m grid were generated over a region of moderate topography in south-east Australia. Six algorithms for calculating gradient and aspect from these data were compared. General linear regression models and the third-order finite difference methods were the most accurate.

Next-generation Digital Earth

A speech of then-Vice President Al Gore in 1998 created a vision for a Digital Earth, and played a role in stimulating the development of a first generation of virtual globes, typified by Google Earth, that achieved many but not all the elements of this vision. The technical achievements of Google Earth, and the functionality of this first generation of virtual globes, are reviewed against the Gore vision. Meanwhile, developments in technology continue, the era of “big data” has arrived, the general public is more and more engaged with technology through citizen science and crowd-sourcing, and advances have been made in our scientific understanding of the Earth system. However, although Google Earth stimulated progress in communicating the results of science, there continue to be substantial barriers in the public’s access to science. All these factors prompt a reexamination of the initial vision of Digital Earth, and a discussion of the major elements that should be part of a next generation.

Digital Earth 2020: towards the vision for the next decade

Abstract This position paper is the outcome of a brainstorming workshop organised by the International Society for Digital Earth (ISDE) in Beijing in March 2011. It argues that the vision of Digital Earth (DE) put forward by Vice-President Al Gore 13 years ago needs to be re-evaluated in the light of the many developments in the fields of information technology, data infrastructures and earth observation that have taken place since. The paper identifies the main policy, scientific and societal drivers for the development of DE and illustrates the multi-faceted nature of a new vision of DE grounding it with a few examples of potential applications. Because no single organisation can on its own develop all the aspects of DE, it is essential to develop a series of collaborations at the global level to turn the vision outlined in this paper into reality.

An explanation of enhanced radar backscattering from flooded forests

Abstract A simpie structural backscatter model for a forest stand, suitable for use with L-band HH polarized radar imagery, is used to explain the increased level of backscattering observed from flooded forests. Measurements made of relative levels of backscatter from SIR-B image data of a flooded Australian forest are consistent with an interpretation based upon scattering mechanisms involving both the tree components and the understorey or forest floor. The change in Fresnel power reflection coefficient of the ground with flooding is advanced as the cause of the enhancement in backscattered power levels.

Spatial Heterogeneity and Irreversible Vegetation Change in Semiarid Grazing Systems

Recent theoretical studies have shown that spatial redistribution of surface water may explain the occurrence of patterns of alternating vegetated and degraded patches in semiarid grasslands. These results implied, however, that spatial redistribution processes cannot explain the collapse of production on coarser scales observed in these systems. We present a spatially explicit vegetation model to investigate possible mechanisms explaining irreversible vegetation collapse on coarse spatial scales. The model results indicate that the dynamics of vegetation on coarse scales are determined by the interaction of two spatial feedback processes. Loss of plant cover in a certain area results in increased availability of water in remaining vegetated patches through run‐on of surface water, promoting within‐patch plant production. Hence, spatial redistribution of surface water creates negative feedback between reduced plant cover and increased plant growth in remaining vegetation. Reduced plant cover, however, results in focusing of herbivore grazing in the remaining vegetation. Hence, redistribution of herbivores creates positive feedback between reduced plant cover and increased losses due to grazing in remaining vegetated patches, leading to collapse of the entire vegetation. This may explain irreversible vegetation shifts in semiarid grasslands on coarse spatial scales.

INTERANNUAL VARIABILITY OF NDVI AND SPECIES RICHNESS IN KENYA

Ecologists have long recognized the spatial variability of species richness. In an attempt to identify the factors responsible for this variability, ecologists have traditionally used environmental data obtained from sparse point samples (such as meteorological stations). However, remotely sensed data also provide a means of estimating relevant environmental factors and thereby improving predictions of species richness. The Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index (AVHRR NDVI) has been shown to be related to net primary productivity (NPP) and actual evapotranspiration (AET) for many vegetation types. NPP and AET have frequently been used as surrogate measures for species richness. Local spatial variability of NPP and AET, indicating habitat heterogeneity, is hypothesized as another influence on species richness. We examined the relationship between interannual maximum NDVI parameters and species richness of vascular plants and mammals. The study was done at a landscape scale, which matches the scale of data collection. Statistical analyses revealed that higher average NDVI results in lower species richness, whereas standard deviation and coefficient of variation correlated positively with species richness. Thus, NDVI parameters appear to represent environmental factors influencing species richness. Hence, by utilizing remote sensing, our understanding of the spatial variability of species richness was improved.