Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors.

Atlantic forest fragmentation is considered a serious threat to biodiversity since this biome is considered the hottest hotspot. Due to this reason, many environmental strategies are being developed in order to support its, one of them being the prioritization of forest remnants using landscape ecology metrics. Thus, the main objective of this study is the development of a patches prioritization index (PPI) in order to support conservation actions and research. Firstly, a diagnosis of forest remnants in the study area was performed using landscape ecology metrics. Secondly, by literature review and expert consulting, were selected the adequate landscape ecology metrics, next, their importance was determined for PPI composition. Selected landscape metrics (AREA, SHAPE, and NEARD) composed the PPI. Finally, using a rapid ecological assessment (BII) the PPI was validated in the field. The results showed that the study area has patches able to aid biodiversity maintenance in the landscape. Further, the selection and importance attributed to landscape ecology metrics were demonstrated to be adequate. Also, the index is accurate enough to identify priority patches, classes, and regions for biodiversity conservation. Finally, the validation of PPI in the field showed that PPI is effective to estimate patches integrity in the field. In conclusion, our results suggest that PPI could be used for the prioritization of Atlantic forest remnants in a landscape covered mainly by Atlantic forest remnants and agriculture.