We applied remote sensing techniques on a TM Landsat 5 image (1:50,000) to map land use and vegetation cover of the Restinga de Jurubatiba National Park and surroundings. The thematic map generated from the digital classification of the image allowed us to spatially characterize and quantify the different land uses and soil covers of the area. Thirteen classes were identified. The most representative classes in the park were the Clusia (31.99%) and Ericaceae formations (29.14%). More than 90% of the park is occupied by native vegetation and coastal lagoons. The surroundings are very much altered by human activities (e.g. 70.64% is used for agriculture and/or cattle raising). Two hundred and three forest fragments (0.3 to 235 ha) were identified, of which 45.3% are less than 5 ha. Most fragments (56.7%) have a very elongated shape, and are thus submitted to an intense edge effect. The intense fragmentation of the surroundings and the isolation of this protected area can imply, in the long run, the loss of genetic diversity.
Swamp forests are associated with hydromorphic soils and are naturally fragmented in their distribution. Several local phytosociological surveys on the woody flora of these forests have been conducted in southeastern and southern Brazil. We present here a comprehensive floristic list based on these surveys, including 77 families, 211 genera and 518 native species. The richest families were Myrtaceae (78 species), Fabaceae (47) and Lauraceae (38). The richest genera were Eugenia (24), Myrcia (24), Miconia (21) and Ocotea (20). The woody flora of these swamp forests has great heterogeneity, with most species occurring in one or few sites. Their flora is formed by a few flooding specialist or tolerant species, common in many sites, and many other species that come from the surrounding vegetation. Considering the high degree of deforestation in southeastern and southern Brazil, including swamp forests, the floristic patterns presented here can be useful for the future efforts of conservation, management and restoration of these forests.
Queimada Grande (QGI) is a small, legally protected island off the southeastern coast of Brazil that harbors two endemic and critically endangered herpetofauna species: the Golden Lancehead viper (Bothrops insularis) and a hylid frog (Scinax peixotoi); its vegetation, however, has been little studied. We integrated remote sensing and phytosociology of the Atlantic Forest on QGI to characterize the habitat of those two species and support their in situ conservation. QGI retains a mosaic of Atlantic Forest, rock outcrop and anthropogenic vegetation, including invasive alien species, and bare rock surfaces. Mature Atlantic Forest, the preferential habitat of B. insularis and S. peixotoi, currently covers ~28 ha (~50%) of QGI and shows very low tree richness and an oligarchic structure. The most important species are Guapira opposita, Rudgea minor and Aspidosperma australe. Anthropogenic formations cover ~9% of the island and do not seem to have expanded in recent years. Based on local conditions, we recommend permanent monitoring of QGI and the use of local tree species in projects to restore the habitat of those two endangered species.
We evaluated the accuracies of Digital Terrain Models (DTMs) generated from LiDAR data in an area of montane Atlantic Forest in the municipality of Rio de Janeiro, Brazil, and explored the use of those data to estimate tree heights. We employed the interpolation of data on a regular grid and a Triangular Irregular Network (TIN) using two softwares (ArcGis 10.3 and Fusion), with 0.4 and 1 m pixels, at scales of 1:2,000 and 1:5,000. The DTMs were evaluated using statistical inferences, considering 165 points surveyed using a geodetic Total Station (TS) in a 20x50 m plot. The models were classified according to the standard regulatory instructions of National Cartography technical norms. Accuracy varied from 0.59 m to 0.63 m among the specifications use for scales of 1:2,000 and 1:5,000. The maximum tree crown heights encountered with the eight Canopy Height Models (CHMs) employed were very similar to field measurements. When our data were analyzed by height classes, however, differences were encountered between the different softwares and interpolation methods used, indicating the influence of DTMs on tree height estimates based on LiDAR data.
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