Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem services. Although previous studies have focused on predicting future changes, there is a growing need to determine past scenarios using new assessment tools. This study proposes a methodology for LULC past scenario generation based on transition analysis. Aiming to hindcast LULC scenario in 1970 based on the transition analysis of the past 35 years (from 1985 to 2020), two machine learning algorithms, multilayer perceptron (MLP) and similarity weighted (SimWeight), were employed to determine the driver variables most related to conversions in LULC and to simulate the past. The study focused on the Aristida spp. grasslands in the Uruguayan savannas, where native grasslands have been extensively converted to agricultural areas. Land use and land cover data from the MapBiomas project were integrated with spatial variables such as altimetry, slope, pedology, and linear distances from rivers, roads, urban areas, agriculture, forest, forestry, and native grasslands. The accuracy of the predicted maps was assessed through stratified random sampling of reference images from the Multispectral Scanner (MSS) sensor. The results demonstrate a reduction of approximately 659 934 ha of native grasslands in the study area between 1985 and 2020, directly proportional to the increase in cultivable areas. The MLP algorithm exhibited moderate performance, with notable errors in classifying agriculture and grassland areas. In contrast, the SimWeight algorithm displayed better accuracy, particularly in distinguishing grassland and agriculture classes. The modeled map using SimWeight accurately represented the transitions between grassland and agriculture with a high level of agreement. By modeling the 1970s scenario using the SimWeight model, it was estimated that the Aristida spp. grasslands experienced a substantial reduction in grassland coverage, ranging from 9982.31 to 10 022.32 km2 between 1970 and 2020. This represents a range of 60.8%-61.07% of the total grassland area in 1970. These findings provide valuable insights into the driving factors behind land use change in the Aristida spp. grasslands and offer useful information for land management, conservation, and sustainable development in the region. The study's main contribution lies in the hindcasting of past LULC scenarios, utilizing a tool used primarily for forecasting future scenarios. Integr Environ Assess Manag 2023;00:1-16. © 2023 SETAC.

Identification of foot and mouth disease risk areas using a multi-criteria analysis approach.

Foot and mouth disease (FMD) is a highly infectious disease that affects cloven-hoofed livestock and wildlife. FMD has been a problem for decades, which has led to various measures to control, eradicate and prevent FMD by National Veterinary Services worldwide. Currently, the identification of areas that are at risk of FMD virus incursion and spread is a priority for FMD target surveillance after FMD is eradicated from a given country or region. In our study, a knowledge-driven spatial model was built to identify risk areas for FMD occurrence and to evaluate FMD surveillance performance in Rio Grande do Sul state, Brazil. For this purpose, multi-criteria decision analysis was used as a tool to seek multiple and conflicting criteria to determine a preferred course of action. Thirteen South American experts analyzed 18 variables associated with FMD introduction and dissemination pathways in Rio Grande do Sul. As a result, FMD higher risk areas were identified at international borders and in the central region of the state. The final model was expressed as a raster surface. The predictive ability of the model assessed by comparing, for each cell of the raster surface, the computed model risk scores with a binary variable representing the presence or absence of an FMD outbreak in that cell during the period 1985 to 2015. Current FMD surveillance performance was assessed, and recommendations were made to improve surveillance activities in critical areas.