02932naa a2200373 a 450000100080000000500110000800800410001902400520006010000230011224501170013526000090025252017710026165000120203265000250204465000290206965000110209865000190210965000210212865000210214965000220217065000290219265000220222165000160224365000250225965300190228465300310230370000210233470000220235570000300237770000190240770000200242670000250244677300870247121406832022-03-15       2021    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s10098-021-02052-92DOI1 aSILVA, J. L. de S.  aAnalysis of vegetation recovery in areas impacted by bauxite mining in the Amazon Forest.h[electronic resource]  c2021  aAbstract: Mining activity is essential for the Brazilian economy, representing 21% of exports in 2018, but it causes several environmental impacts, including deforestation. To minimize these impacts, replanting techniques are applied for environmental recovery and regulatory compliance. In this context, this study aims to monitor vegetation recovery in decommissioned bauxite mining areas located in the Amazon rainforest. The case study was carried out in the municipality of Paragominas (state of Pará). The monitoring used the series of a newly defined spectral index, called Biomass Composite Index (BCI), and the Enhanced Vegetation Index (EVI), both from Landsat images between 1986 and 2017. The analysis was complemented by interpretation of false-color images and by in situ photographs. The method was applied for periods before mining, during exploitation, and after decommissioning, when the traditional planting technique was implemented. BCI demonstrated greater sensitivity to forest disturbance in areas affected by forest degradation and less influence of canopy homogeneity where the forest was substituted by more open formations. The height homogeneity of recovered areas also affected the EVI data more than the BCI data, since these areas presented higher values of EVI, and some areas had index values close to those detected before mine operation. The analysis could have benefited from sampling intensification, with more images analyzed per year, to overcome the presence of clouds, which block the acquisition of surface data by the sensor. Nevertheless, the method proved to be very promising and can improve the evaluation of forest recomposition even in remote areas, besides facilitating large-scale monitoring of recovered forests.  aBauxite  aEnvironmental impact  aEnvironmental monitoring  aMining  aRemote sensing  aVegetation cover  aVegetation index  aCobertura Vegetal  aFloresta Tropical Úmida  aImpacto Ambiental  aMineração  aSensoriamento Remoto  aBauxite mining  aRecovery of impacted areas1 aARAUJO, R. A. de1 aESTEVES, V. P. P.1 aLOEBMANN, D. G. dos S. W.1 aVICENTE, L. E.1 aPASCHOAL. J. P.1 aMORGADO, C. do R. V.  tClean Technologies and Environmental Policygv. 23, n. 5, p. 1617-1640, July 2021.