Short communicationIntensive forest clearing in Rondonia, Brazil, as detected by satellite remote sensing
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Cited by (110)
Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000‐2017 Landsat time-series
2019, Remote Sensing of EnvironmentCitation Excerpt :Periodic monitoring of such key environmental parameters as tree canopy cover, composition, and disturbance are feasible with Landsat data (Iverson et al., 1989; Westman et al., 1989; Cohen and Goward, 2004). Early approaches based on discrete land cover classification and bi-temporal change detection (Tucker et al., 1984; Skole and Tucker, 1993) evolved toward modeling of continuous vegetation structure (DeFries et al., 1998; Hansen et al., 2002a, 2002b, Cohen et al., 2003) and function (Running et al., 2004). Change detection advanced through the use of dense time-series analyses (Huang et al., 2010; Kennedy et al., 2010; Verbesselt et al., 2010; Hansen et al., 2013) and integration of directly detected changes with annual land cover information (Potapov et al., 2015; Wulder et al., 2018).
Trend shifts in satellite-derived vegetation growth in Central Eurasia, 1982–2013
2017, Science of the Total EnvironmentCitation Excerpt :Using remote sensing perspectives to identify land surface dynamics over long time scales is necessary to discern the spatial and temporal patterns of vegetation response to climate variability (Weiss et al., 2004). The Normalized Difference Vegetation Index (NDVI) is widely used as a robust indicator of vegetation growth as it is linked with green leaf biomass and vigor (Tucker et al., 1984). The Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset has proven to be the most complete and longest remotely sensed measurements appropriate for long term studies of vegetation activities and climate impacts on vegetation at regional and global scales (Fensholt and Proud, 2012).
Remote Sensing and Measuring Deforestation
2016, Land Surface Remote Sensing: Environment and RisksBoolean and fuzzy methods for identifying lateritic regoliths in the Brazilian Amazon using gamma-ray spectrometric and topographic data
2016, GeodermaCitation Excerpt :Therefore, the identification and discrimination of lateritic crusts is essential in mineral exploration and useful for the improvement of geological mapping. The use of the airborne gamma-ray spectrometry and altimetric data in regolith studies must be investigated, especially when applied with mathematical techniques, raster image algebra, and in the evaluation of multi-source data (e.g., Darnley and Grasty, 1971, Tucker et al., 1984, Duval, 1990, Burrough et al., 1992, Graham and Bonham-Carter, 1993, Wilford et al., 1997, Dickson and Scott, 1997, McKenzie & Ryan 1999, Zhu et al., 2010, Carrino et al., 2011, Wilford, 2012 and Dent et al., 2013). Airborne gamma-ray spectrometry measures the concentration of potassium (K) and series of uranium (U) and thorium (Th) radioisotopes in rocks and soils at depths of 30–45 cm (Gregory and Horwood, 1961; Dickson and Scott, 1997).
Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems
2014, Remote Sensing of EnvironmentCitation Excerpt :Many studies have utilized Landsat and SPOT high-resolution data in concert with aerial photographs to identify logging activities by their distinctive geometry (Asner et al., 2005; Skole & Tucker, 1993). Forest degradation has been characterized empirically from Advanced Very High Resolution Radiometer (AVHRR)-derived metrics such as NDVI, leaf-area index, land cover, land- surface temperature, albedo, and productivity (Achard et al., 2002; Lambin, 1999; Tucker, Holben, & Goff, 1984) that are not always tied to physical vegetation properties (Lambin, 1999). The widely researched interactions between climate, fire, and logging have also led to several studies using remote sensing to characterize patterns of area burned and forest land use in the Amazon.
Policy for robust space-based earth science, technology and applications
2013, Space PolicyCitation Excerpt :Responding to users' needs, ESA's Living Planet Programme [20] comprises a science and research element [21,22], which includes the Earth Explorer missions (with missions such as GOCE, SMOS, CRYOSAT and SWARM) and an ‘Earth Watch’ element, designed to facilitate the delivery of Earth observation data for use in operational services. Earth Watch includes the well-established meteorological missions developed with Eumetsat [23,24]. The space component of GMES is composed of satellite missions called Sentinels, specifically developed by ESA for the needs of GMES, and of “GMES Contributing Missions”, owned or operated by European national or multinational organizations that are already providing data to GMES.