# Analysis of a Landscape Intensely Modified by Agriculture in the Tiete-Jacare Watershed, Brazil
###### Abstract  
Anthropogenic actions influence landscapes, and the resulting mosaic is a mix of natural and anthropogenic elements that vary in size, shape, and pattern. Considering this, our study aimed to analyse the land use and land cover changes in the Tiete-Jacare watershed (Sao Paulo state, Brazil), using the random forest (RF) algorithm and Sentinel-2 satellite data from 2016 to 2018 to detect landscape changes. By overlapping the environmental data and the proposed model evaluation, it was possible to observe the landscape structure, produce information about the state of this region, and assess the environmental responses to anthropic impacts. The land use and land cover analysis identified eight classes: exposed soil, citriculture, pasture, silviculture, sugar cane, urban area, vegetation, and water. The RF classification for the three years reached high accuracy with a kappa index of 0.87 in 2016, 0.85 in 2017, and 0.85 in 2018. The model developed was essential for the temporal analysis since it allowed us to comprehend the driving forces that act in this landscape and contribute to the discussions about their impacts over time. The results showed a predominance of agricultural activities over the three years, with approximately 900.000 ha (76% of the area), mainly covered by sugarcane cultivation. watershed management; landscape analysis; environmental planning; land use; land cover 10.3904.10.10.  
national territory. Interestingly, it contains 72% of the Brazilian population and 70% of the national gross domestic product (GDP) [1,2]. The landscapes are usually studied as spatial units whose heterogeneity is mainly modified by the interaction between nature and society. Thus, it is characterized by the connection or fragmentation among its different elements. Nevertheless, anthropic actions can affect and modify most landscapes, causing impacts such as deforestation, fragmentation, and loss of biodiversity. The resulting mosaic is a blend of natural and anthropogenic features that differ in size, shape, and structure [3, 4, 5, 6]. Planning and decision-making involve choosing a course of action among several alternatives. They are based on the quantification of environmental conditions, such as land use and land cover, conservation status, and resilience of the landscape [7,8]. More recently, landscape indicators have been used to provide insights into conservation practices. The demand for results on small timescales has led scientists to search for measures calculated based on accessible indicators in order to serve as a basis for decision-making, meanwhile trying to encompass some of the most important ecosystems on Earth [7,8]. Authors such as [PERSON] [9], [PERSON] et al. [10], [PERSON] and [PERSON] [11], [PERSON] and [PERSON] [12], [PERSON] [13], [PERSON] [14], and [PERSON] et al. [15] studied and developed a variety of indices and descriptive measures to analyse spatial landscape patterns. Thus, modelling is considered to be an established method for obtaining information and generating hypotheses in landscape ecology and population issues [9, 10, 11, 12, 13, 14, 15]. From this perspective, geographical information systems (GIS) and remote sensing have facilitated the characterization process of diagnostic analyses and activities. Additionally, they help simulate geographic space and natural processes, besides integrating spatial information [16]. The efficiency of satellite imagery for monitoring land use and land cover changes and the consequent impact of anthropic activities on the environment contribute to governmental planning and action regarding the conservation of natural ecosystems [17]. An example is the work developed by [PERSON] and [PERSON] [18], whose objective was to identify the land use and the landscape configuration of the Ceroula Environmental Protection Area in the Angico Sub-basin, MS, Brazil. In such study, remote sensing imagery and field measurements were integrated into a GIS.