Główne kierunki oraz historyczne i krajobrazowe uwarunkowania przemian pokrycia terenu okolic Pińczowa w latach 1839–2000

The Amazon region has experienced a rapid rate of deforestation and land use change as a result of establishment of agricultural settlements, resulting from public policies designed to promote rural development. We analyzed land use patterns and changes in the central region of Roraima, northern Brazil, testing the hypothesis that the anthropic pressure based on the conversion of natural vegetation (forest ecotone zone and open areas of savanna and campinaranas) on agriculture and pasture, has led to the decline of forest resilience, and has not promoted development in lands converted in agricultural colonization projects, a process exacerbated by practices of burning. Satellite images from between 1984 to 2017, with field-collected data and geoprocessing techniques, allowed interpretation and analysis of seven land-use classes. Agriculturally-based human impacts were greatest in forest areas, with forest loss rates being 6.4 times greater than regeneration rates. The 39.3% reduction in natural non-forest vegetation types exceeded that of forest loss (23.8%). Repeated fires resulted in a 627.1% increase in forest fragmentation in areas heavily impacted by fire. Our study revealed that, over 33 years, deforestation and transitions of land to non-conservation uses did not lead to a system with highly productive agricultural practices, but to extensive impoverished, and degraded subsistence. The main reason was the basic unsuitability of the region´s extremely acidic/dystrophic soils on which settlements have been founded, and the predominance of low-tech, family-based, agriculture and the absence of the required technology for attaining better results.

We used temporally consistent patterns in the spatial distribution of returning adult coho salmon (Oncorhynchus kisutch) to explore relationships between salmon abundance, landscape characteristics, and land use patterns in the Snohomish River watershed, Wash. The proportion of total adult coho salmon abundance supported by a specific stream reach was consistent among years, even though interannual adult coho salmon abundance varied substantially. Wetland occurrence, local geology, stream gradient, and land use were significantly correlated with adult coho salmon abundance. Median adult coho salmon densities in forest-dominated areas were 1.5–3.5 times the densities in rural, urban, and agricultural areas. Relationships between these habitat characteristics and adult coho salmon abundance were consistent over time. Spatially explicit statistical models that included these habitat variables explained almost half of the variation in the annual distribution of adult coho salmon. Our analysis indicates that such models can be used to identify and prioritize freshwater areas for protection and restoration.

Selecting an effective archaeological survey takes careful consideration given the interaction of several variables, such as the survey's goals, nature of the data base, and budget constraints. This article provides justification for a “siteless survey” using evidence from a project on Potowomut Neck in Rhode Island whose objective was not to locate sites but to examine the distribution and density of prehistoric remains to test an hypothesis related to land use patterns. The survey strategy, random walk, was chosen because it possessed the advantages of probabilistic testing, as well as the ease of locating sample units. The results were within the limits of statistical validity and were found unable to reject the hypothesis. “Siteless survey” may be successfully applied in similar contexts where the distribution and density of materials, as opposed to ambiguously defined sites, are sought as evidence of land use patterns, in particular, and human adaptation, in general.

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.