Knowing the state of the art on research related to post-mining active revegetation can help to improve revegetation success and identify research gaps. We performed a systematic review about active revegetation after mining and identified 203 relevant studies. Most studies were performed in the USA (34%), in regions with a temperate climate (59%) and in abandoned coal mines (45%). The studies were focused on the plantation of woody species (59%) or sowing of herbaceous species (39%). The most widely evaluated treatments were the addition of amendments (24%) and fertilizers (21%), mainly with positive and neutral effects; in general, organic amendments presented more positive effects than inorganic amendments and fertilizers. We also identified studies on the effects of plowing, inoculation of microorganisms, nurse plants, herbivore exclusion and watering. The results of these treatments should be taken with caution, because they can vary according to the functional strategies of the introduced species and the local context, such as the degree of nutrient limitation in the mining area and abiotic conditions. Further research is needed in non-temperate climates, involving long-term monitoring and with detailed descriptions of the interventions to better interpret results and general implications of active revegetation of mining areas.

Uncertainties in the timing and quality of rainy season are a threat for food and water security, and also in terms of fire vulnerability. Then, understanding features associated to rainfall allows a climate characterization useful for climate and fire risk management. We used rainfall data series (1983-2018) from 15 meteorological stations to characterize the greatest conservation area of Brazilian-unique seasonally dry tropical forest Caatinga (northeastern Brazil). Accumulated anomalies in daily series were used to determine onset and end of rainy seasons. We also determined seasonal and annual rainfall (quality) and rain rate, and performed a dry season sub-classification. Results showed greater variability for end dates as compared to onset dates for rainy season. Droughts in the region are becoming more severe. We found a significant decreasing tendency of 7 mm/year on annual rainfall, of 0.3 mm/day per decade on rain rate, and an increase of 12 days per decade on consecutive dry days. Dry season length presented a 14-year periodicity and is related with previous but uncorrelated from posterior rainy season length. The complexity of the rainfall patterns is evidenced by the weak correlation we found between the amount of rainfall and the rainy season length.

The use of remote sensing to determine land-use and land-cover (LULC) dynamics is often applied to assess the levels of natural forest conservation and monitor deforestation worldwide. This study examines the loss of native vegetation in the Campo Maior Complex (CMC), in the Brazilian Caatinga dry tropical forest, from 2016 to 2020, considering the temporal distribution of rainfall and discussing the trends and impacts of forest-degradation vectors. The Google Earth Engine (GEE) platform is used to obtain the rainfall data from the CHIRPS collection and to create the LULC maps. The random forest classifier is used and applied to the Landsat 8 collection. The QGIS open software and its SPC plugin are used to visualize the LULC dynamics. The results show that the months from June to October have the lowest average rainfall, and that 2019 is the year with the highest number of consecutive rainy days below 5 mm. The LULC maps show that deforestation was higher in 2018, representing 20.19%. In 2020, the proportion of deforestation was the lowest (11.95%), while regeneration was the highest (20.33%). Thus, the characterization of the rainfall regime is essential for more accurate results in LULC maps across the seasonally dry tropical forests (SDTF).