The southeastern Amazon region has been intensively occupied by human settlements over the past three decades. To evaluate the effects of human settlements on land-cover and land-use (LCLU) changes over time in the study site, we evaluated multitemporal Landsat images from the years 1984, 1994, 2004, 2013 and Sentinel to the year 2017. Then, we defined the LCLU classes, and a detailed “from-to” change detection approach based on a geographic object-based image analysis (GEOBIA) was employed to determine the trajectories of the LCLU changes. Three land-cover (forest, montane savanna and water bodies) and three land-use types (pasturelands, mining and urban areas) were mapped. The overall accuracies and kappa values of the classification were higher than 0.91 for each of the classified images. Throughout the change detection period, ~47% (19,320 km²) of the forest was preserved mainly within protected areas, while almost 42% (17,398 km²) of the area was converted from forests to pasturelands. An intrinsic connection between the increase in mining activity and the expansion of urban areas also exists. The direct impacts of mining activities were more significant throughout the montane savanna areas. We concluded that the GEOBIA approach adopted in this study combines the advantages of quality human interpretation and the capacities of quantitative computing.

Remote sensing technologies can play a fundamental role in the environmental assessment of open-cast mining and the accurate quantification of mine land rehabilitation efforts. Here, we developed a systematic geographic object-based image analysis (GEOBIA) approach to map the amount of revegetated area and quantify the land use changes in open-cast mines in the Carajás region in the eastern Amazon, Brazil. Based on high-resolution satellite images from 2011 to 2015 from different sensors (GeoEye, WorldView-3 and IKONOS), we quantified forests, cangas (natural metalliferous savanna ecosystems), mine land, revegetated areas and water bodies. Based on the GEOBIA approach, threshold values were established to discriminate land cover classes using spectral bands, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and a light detection and range sensor (LiDAR) digital terrain model and slope map. The overall accuracy was higher than 90%, and the kappa indices varied between 0.82 and 0.88. During the observation period, the mining complex expanded, which led to the conversion of canga and forest vegetation to mine land. At the same time, the amount of revegetated area increased. Thus, we conclude that our approach is capable of providing consistent information regarding land cover changes in mines, with a special focus on the amount of revegetation necessary to fulfill environmental liabilities.

The conservation and restoration of native vegetation is vital for providing key hydrological services (i.e., maintaining high water quality, atmospheric humidity, and precipitation patterns). However, this research area lacks fine-scale studies at the watershed level to evaluate opportunities for forest restoration and deficit (the shortfall of forest required to be restored or compensated), as well as the implications for watershed management. We provide the first fine-scale estimation of forest and deficit distribution, integrating permanent preservation areas (APPs, in Portuguese) and legal reserves (RL, in Portuguese), according to Brazilian environmental law, for the 41,300 km² Itacaiúnas watershed in the Brazilian state of Pará, which has lost 50% of its vegetation cover. Using 30 m- and 10 m-resolution imagery, a multi-temporal land use classification was performed by geographic object-based image analysis (GEOBIA). The results were combined with a set of Brazilian regulations on the conservation and restoration of APPs and RL to assess patterns of forest cover and legal compliance. We found that the total RL deficit (4383 km²) was higher than the total forest surplus (above legal obligation) (3241 km²). However, most of this deficit (56%) could be compensated by protecting a forest area in another property within the Amazon biome, while 44% must be legally restored. Only 4% of the total forest surplus can be legally deforested, and the remaining 96% is already protected by law but can be used to compensate for areas under the deficit. We also found that, despite 57% (3017 km²) of the total APP being forested, only 26% (1356 km²) of the APP must be restored and 17% (881 km²) can remain deforested (consolidated areas). The 2012 law revision reduced the obligation to restore RL and APPs. This change could affect hydrological and ecological services. Compensation mechanisms could be used to protect forest within the Itacaiúnas watershed, rather than in the biome, to reduce further deforestation pressure.

Rehabilitation is the key factor for improving soil quality and soil carbon stock after mining operations. Monitoring is necessary to evaluate the progress of rehabilitation and its success, but the use of repeated field surveys is costly and time-consuming at a large scale. This study aimed to monitor the environmental/soil rehabilitation process of an Amazonian sandstone mine by applying spectral indices for predicting soil organic carbon (SOC) stock and comparing them to soil quality index. The studied area has different chronological rehabilitation stages: initial, intermediate, and advanced with 2, 10, and 12 years of onset rehabilitation activities, respectively. Non-rehabilitated (NR) and two native forest areas (RA) were used as controls. Soil samples were analyzed for physical, chemical, and biological attributes. After determination of Normalized Difference Vegetation Index and Bare Soil Index, simple regression analysis comparing these indices with SOC stock showed a good fit (R² = 0.82). Rehabilitated areas presented higher soil quality index (~1.50-fold) and SOC stock (~10.6-fold) than NR; however, they did not differ of RA. The use of spectral indices was effective for monitoring the soil quality in this study, with a positive correlation between the predicted SOC stock and the calculated soil quality index.