Spectral and human sensors : hyperspectral remote sensing and participatory GIS for mapping livestock grazing intensity and vegetation in transhumant Mediterranean conservation areas

Increasing shortage of pasture resources due to land use conversion constitutes a major challenge to traditional transhumance systems. Reduction of transhumance and related activities leaves the non converted areas abandoned. This may lead to change in grazing intensity, which might result into change in species composition and vegetation pattern. A reduction in grazing intensity might thus influence the biodiversity and forage quality of previously more intensively grazed areas. Proper management of Mediterranean grasslands would require insight on how grazing intensity varies across a landscape and how it influences the distribution and abundance of plant species. The aim of this study was to investigate methods for mapping of livestock grazing intensity and vegetation, using hyperspectral remote sensing, geographic information systems (GIS) and participatory GIS (PGIS). Investigations were undertaken at two main levels. A greenhouse experiment was used to investigate the effects of defoliation and defoliation time for two species grown in mono and mixed culture on the height and dry matter yield as measures of regrowth and competitive ability of two livestock forage grasses selected from a transhumant Mediterranean area. Narrow band hyperspectral reflectance, indices and the red-edge position were investigated to see if they may be used to study these effects. At field landscape level, we tested the use of local people’s knowledge in mapping grazing intensity through the application of PGIS. The results from the greenhouse experiment showed that the species with higher dry matter yield (Lolium multiflorum) had a significantly higher relative regrowth rate and possibly higher competitive ability than its competitor Dactylis glomerata (P < 0.05). Increase in dry matter yield was shown as the trait that determines competitive ability in the early established stage of the two grass species (period of 13 to 18 weeks after sowing). The experiment also provided insight on the persistence of forage species that are of grazing preference. Selective clipping did not alter the competitive ability of D. glomerata to surpass that of L. multiflorum when the former was clipped at lower clipping intensity to simulate selective grazing. The hyperspectral remote sensing variables that may be used to estimate the effect of species types, cultures and defoliation treatments were: the physiological reflectance index (PRI), the Carter index, R694, the ration of the Transformed Chlorophyll Absorption in Reflectance Index to the Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) and the red-edge position. The PRI was found to be the most sensitive index. A significant increase (p < 0.001) in PRI was associated with the higher competitive ability of L. multiflorum than D. glomerata when the two were mixed. The response of the PRI from negative to positive over the measurement time in relation to height and dry matter yield suggest that the PRI may be used to study competitive ability because the related growth characteristics are indicators of competitive ability. This encourages further investigation of this method as a potential simpler and quicker alternative to the existing canopy height and pasture growth models. This may lead to efficient assessment and improved understanding of the condition and spatial patterns of forage vegetation species at field level. At field landscape level, using participatory GIS (PGIS), spatial knowledge on grazing intensity from pastoralists and local range ecology experts was elicited and relevant criteria generated and used to classify grazing intensity. Local pastoralists appeared to be more knowledgeable than local range ecology experts, possibly because of the pastoralists’ superior familiarity with the rangeland and better perceptions about the distribution of palatable species but the experts represented the grazing intensity better on a map. Local pastoralists have potential to contribute better to this process if the PGIS includes adequate training in the map making process. The local experts showed the capability to produce data and synthesize spatial variables, but it was also shown that the expert-based PGIS maps may not always be reliable. Using a proposition that “This area or pixel belongs to the high, medium, or low grazing intensity class because the local expert(s) says (say) so”, we tested for uncertainty in the PGIS-maps produced by different local experts using spatial tools such as evidential belief functions (EBFs). Evaluating the classification uncertainty in the different grazing intensity maps revealed that the maps with the lowest uncertainty were based on the composition of palatable vegetation species as the mapping criterion. This criterion may be used for mapping grazing intensity because it relates to measures of forage condition such as ground cover and quality, but it may be limited in use if other parameters such as vegetation composition and quantity are not integrated. If the definition of grazing intensity also includes these parameters and also livestock vegetation use factor and impacts on vegetation, then the proposition for EBF evaluation would be that: “This pixel or area is a specific grazing intensity class because of the level of livestock grazing use and its impacts on species composition, ground cover, quantity and quality. These parameters may be efficiently estimated using hyperspectral remote sensing. In order to include local knowledge in such an evaluation, research should establish how local pastoralists and experts may process the various parameters and how they may apply such a proposition. Since more than one criterion proved cumbersome for the local experts as evidenced by a weak correlation between the grazing intensity map and a grazing suitability index (r =0.35 (p < 0.01)), spatial multiple criteria tools may be useful for synthesizing the different mapping criteria. Overall, this study showed that high spectral resolution sensors can detect the effect of grazing and competitive interactions among forage plants through narrow band channels across the spectrum, while the local people perceive a few broad grazing intensity classes and spatially represent them using a few criteria. The two are complementary. The spectral sensor provides detailed information on the status and spatial patterns of vegetation, while local participants provide the spatial information on a more general coarse scale that may be used as baseline for hyperspectral remote sensing research

Representation of uncertainty and integration of PGIS-based grazing intensity maps using evidential belief functions

In a project to classify livestock grazing intensity using participatory geographic information systems (PGIS), we encountered the problem of how to synthesize PGIS-based maps of livestock grazing intensity that were prepared separately by local experts. We investigated the utility of evidential belief functions (EBFs) and Dempster's rule of combination to represent classification uncertainty and integrate the PGIS-based grazing intensity maps. These maps were used as individual sets of evidence in the application of EBFs to evaluate the proposition that "This area or pixel belongs to the high, medium, or low grazing intensity class because the local expert(s) says (say) so". The class-area-weighted averages of EBFs based on each of the PGIS-based maps show that the lowest degree of classification uncertainty is associated with maps in which "vegetation species" was used as the mapping criterion. This criterion, together with local landscape attributes of livestock use may be considered as an appropriate standard measure for grazing intensity. The maps of integrated EBFs of grazing intensity show that classification uncertainty is high when the local experts apply at least two mapping criteria together. This study demonstrates the usefulness of EBFs to represent classification uncertainty and the possibility to use the EBF values in identifying and using criteria for PGIS-based mapping of livestock grazing intensity

Mapping the Potential for Hay Making in Rangelands: A Methodological Proposition

We present information useful to various stakeholders, including land managers, agency personnel, practitioners, and researchers, as it presents methodology for ○ Determining the best period for hay harvest corresponding to peak productivity of the vegetation in rangelands;○ Estimating the amount of hay available (biomass) at peak productivity, using commonly available satellite imagery; and○ Highlighting the best areas for hay production based on grassland availability. All of this is done by employing the readily available tools of remote sensing and geographical information system. © 2017 The Society for Range ManagementThe Rangelands archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information