RESUMO
Developing conservation strategies to mitigate cumulative impacts requires the understanding of historic land use and land cover changes at the regional scale. By using a multisensory and multitemporal approach, we identified the major changes driving cumulative impacts on native vegetation in northeastern Amazon. Comparing two regions, one with mining as the key driver and another where mining is associated with other industrial activities (cellulose), we explore the land use and land cover historic dynamics and derive implications for the assessment of cumulative impacts. Transitions of forest cover to pastureland, silviculture, and urban expansion were mapped in detail over a 20-year period, revealing that silviculture growth cleared more forests than pastureland expansion when associated with pulp mill activities and kaolin mining. In contrast, in a region with gold and iron mining, pastureland expansion was more relevant, clearing mainly areas surrounding new roads. This research shows that the interplay of major mining and industrial investments can produce cumulative losses of native vegetation, depending on the associated industries and infrastructure required for the project development. Our findings emphasize that the definition of spatial and temporal boundaries for the assessment of cumulative impacts must consider different trends in impact accumulation and changes in their spatial distribution over time.
Assuntos
Conservação dos Recursos Naturais , Florestas , Brasil , MineraçãoRESUMO
Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we present a geographic object-based image analysis (GEOBIA) approach to examine a set of very high resolution (VHR) multispectral images obtained by the use of small unmanned aerial vehicles (UAVs), to evaluate plant health states and to generate cropland maps for Capsicum annuum L. The scheme described here integrates machine learning methods with semi-automated training and validation, which allowed us to develop an algorithmic sequence for the evaluation of plant health conditions at individual sowing point clusters over an entire parcel. The features selected at the classification stages are based on phenotypic traits of plants with different health levels. Determination of areas without data dependencies for the algorithms employed allowed us to execute some of the calculations as parallel processes. Comparison with the standard normalized difference vegetation index (NDVI) and biological analyses were also performed. The classification obtained showed a precision level of about 95 % in discerning between vegetation and non-vegetation objects, and clustering efficiency ranging from 79 % to 89 % for the evaluation of different vegetation health categories, which makes our approach suitable for being incorporated at C. annuum crop's production systems, as well as to other similar crops. This methodology can be reproduced and adjusted as an on-the-go solution to get a georeferenced plant health estimation.