Land-use changes assessed by overlay or mosaic methods: Which method is best for management planning?

Environmental planning must determine management practices for a given territory based on the landscape processes that have occurred over time and their consequences. Therefore, environmental planning decisions must be based on strong empirical evidence that can be easily understood by all involved parties. Several studies have highlighted the methodological deficiencies that occur when obtaining and interpreting such issues, particularly in heterogeneous landscapes with complex interactions. In this paper, we evaluated two methodological approaches that are used in management planning, land use/cover change (LUCC) and mosaic change (MC) to compare their effectiveness and suitability for supporting decision-making. We applied these methods to the coastal landscape of São Sebastião Island, Brazil, which has undergone many changes in the last 50 years. For two years, land use/cover maps were produced using GIS and assessed according to changes in landscape elements (LUCC) and boundaries (MC). Overall, the LUCC failed to identify sets with similar structural heterogeneities in the landscape. However, the LUCC is easier for stakeholders to understand and apply than the MC. The MC method better presented the evolution of the relationship between the landscape elements and heterogeneity.     

Determinants of Land Degradation and Fragmentation in Semiarid Vegetation at Landscape Scale

The objective of this paper was to investigate the sensitivity to degradation of semiarid plant communities in terms of plant cover and fragmentation, quantifying relationships between landscape characteristic (physical, socio-economical and historical) and vegetation degradation. The degradation of vegetation was measured as the degree of coverage of the two dominant vegetation types (i.e. tall arid brush and tall grass steppe), while fragmentation was measured as patch size and isolation. Data were obtained using GIS tools, and analyzed by logistic regression and linear multivariate regression. Results showed denser coverage at more elevated, gradual slopes that were not sea-oriented. Historical elements of the landscape had a significant effect on current natural vegetation. According to the fragmentation patterns, the vegetation is in fairly good condition (medium coverage had the largest patches but dense coverage was less isolated) but attention must be given to preserve vegetation, due to the relationships between fragmentation and human activities. Moreover, the protection plan under way in the area appeared to favour denser vegetation cover, while human activities had a measurable effect on vegetation degradation.     

Amazonian biodiversity: assessing conservation priorities with taxonomic data

Data from 3991 records of museum collections representing 421 species of plants, arthropods, amphibians, fish, and primates were analyzed with GIS to identify areas of high species diversity and endemism in Amazonia. Of the 472 1 × 1° grid cells in Amazonia, only nine cells are included in the highest species diversity category (43–67 total species) and nine in the highest endemic species diversity category (4–13 endemic species). Over one quarter of the grid cells have no museum records of any of the organisms in our study. Little correspondence exists between the centers of species diversity identified by our collections-based data and those areas recommended for conservation in an earlier qualitative study of Amazonian biodiversity. Museum collections can play a vital role in identifying species-rich areas for potential conservation in Amazonia, but a concerted and structured effort to increase the number and distribution of collections is needed to take maximum advantage of the information they contain.     

Zoning eco-environmental vulnerability for environmental management and protection

Eco-environmental vulnerability assessment is crucial for environmental and resource management. However, evaluation of eco-environmental vulnerability over large areas is a difficult and complex process because it is affected by many variables including hydro-meteorology, topography, land resources, and human activities. The Thua Thien – Hue Province and its largest river system, the Perfume River, are vital to the social-economic development of the north central coastal region of Vietnam, but there is no zoning system for environmental protection in this region. An assessment framework is proposed to evaluate the vulnerable eco-environment in association with 16 variables with 6 of them constructed from Landsat 8 satellite image products. The remaining variables were extracted from digital maps. Each variable was evaluated and spatially mapped with the aid of an analytical hierarchy process (AHP) and geographical information system (GIS). An eco-environmental vulnerability map is assorted into six vulnerability levels consisting of potential, slight, light, medium, heavy, and very heavy vulnerabilities, representing 14%, 27%, 17%, 26%, 13%, 3% of the study area, respectively. It is found that heavy and very heavy vulnerable areas appear mainly in the low and medium lands where social-economic activities have been developing rapidly. Tiny percentages of medium and heavy vulnerable levels occur in high land areas probably caused by agricultural practices in highlands, slash and burn cultivation and removal of natural forests with new plantation forests. Based on our results, three ecological zones requiring different development and protection solutions are proposed to restore local eco-environment toward sustainable development. The proposed integrated method of remote sensing (RS), GIS, and AHP to evaluate the eco-environmental vulnerability is useful for environmental protection and proper planning for land use and construction in the future.     

Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise?

ABSTRACT Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field-based studies. We tested 5 techniques in 10 virtual environments and in one real-world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate-weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point-based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small-patch habitat relationships.     

GIS interpolations of witness tree records (1839–1866) for northern Wisconsin at multiple scales

To construct forest landscape of pre‐European settlement periods, we developed a GIS interpolation approach to convert witness tree records of the U.S. General Land Office (GLO) survey from point to polygon data, which better described continuously distributed vegetation. The witness tree records (1839–1866) were processed for a 3‐million ha landscape in northern Wisconsin, U.S.A. at different scales. We provided implications of processing results at each scale. Compared with traditional GLO mapping that has fixed mapping scales and generalized classifications, our approach allows presettlement forest landscapes to be analysed at the individual species level and reconstructed under various classifications. We calculated vegetation indices including relative density, dominance, and importance value for each species, and quantitatively described the possible outcomes when GLO records are analysed at three different scales (resolution). The 1 × 1‐section resolution preserved spatial information but derived the most conservative estimates of species distributions measured in percentage area, which increased at coarser resolutions. Such increases under the 2 × 2‐section resolution were in the order of three to four times for the least common species, two to three times for the medium to most common species, and one to two times for the most common or highly contagious species. We mapped the distributions of hemlock and sugar maple from the pre‐European settlement period based on their witness tree locations and reconstructed presettlement forest landscapes based on species importance values derived for all species. The results provide a unique basis to further study land cover changes occurring after European settlement.     

Assessment of ecological vulnerability and decision-making application for prioritizing roadside ecological restoration: A method combining geographic information system,Delphi survey and Monte Carlo simulation

Study experience of ecologist plays an important role in assessing the contribution of different influencing factors to ecological vulnerability, helping policy makers to target measures for ecological restoration. However, uncertainty is unavoidable due to variation of study experience among experts. In this study, a new method that combines Delphi survey, geographic information system and Monte Carlo simulation was proposed to assess regional ecological vulnerability and to quantify the uncertainty of assessing result. We illustrated the capacity of this method by using a case study in northeastern Inner Mongolia, China. An index system for 13 spatial variables was established to calculate an ecological vulnerability index (EVI) from the three aspects of ecological sensitivity (ES), ecological resilience (ER) and natural-social pressure (NSP). The assessment shows that the southwestern region of the study area, especially in the counties of Sonid Left and Right, was seriously threatened by a high ES and a low ER. Onguiud county in the Greater Hinggan Mountains had a high EVI due to an intensive NSP. Based on the assessing result and regional road distribution, an EVI cost curve was created to facilitate the prioritization of allocating limited funds among the various counties for roadside ecological restoration.