Rapid changes in land use and cover in the urban environment have a major impact on the relationships between energy and hydrological cycles over the surface. The municipality of Recife, through the Soil Use and Occupancy Act 1996 (Law No 16.176/96) defines green area as ‘any area of public or private land, on natural soil, where any form of vegetation predominates, distributed in its different strata: tree, shrub and herbaceous/Forrageira, native or exotic’. The purpose of this article is to analyse the spatial variation of the green areas available in Recife municipality and the spatial and temporal evolution of environmental quality in RPA 4 through the umity Index Computing (NDWI), Foliar Area Index (IAF) and Area Temperature in TM Landsat images. A supervised classification was carried out on the Recife orthophoto card where the green areas were exported to polygons, allowing for their quantification. For TM images part of the Sebal methodology has been applied. Green areas occupy 45.58 % of Recife. Linear transects and profiles have made it easier to view the spatial and temporal changes in RPA-4. There was a large difference in temperature between the planted and the most urbanised areas. Keywords: Land use and land cover; urban area, vegetated areas, remote sensory; MAXVER. B S T R A C T The rapid change of use and land cover in urban environment poses great impact on relations between energy and hydrological cycles on the surface. The municipality of Recife, through the Land Use Legislation from 1996 (Law No 16.176/96) defines green area as "; any public or private domain area, in natural soil, where overlogs any form of vegetation, distributed in its different layers: Arboreal, shrubby and herbaceous Forage, native or Exotic”; The goal of this paper is to analyse the spatial variation of available green areas in the city of Recife and the spatio-temporal evolution of environmental quality in the Political Administrative Region 4, known as RPA-4, through the calculation of moisture content (NDWI), leaf area index (LAI) and the surprise temperature from Landsat TM images. Supervised classification was performed on orthophoto Reef where the green areas were exported to polygons, Allowing its quantification. For the TM images, it has had applied the methodology Sebal. The green areas occupy 45.58 % of Recife. The linear transects and profiles allowed to show more easily space-time changes occurring in the RPA-4. Large temperature differences have been displayed between the most cultivated areas and more urbanised areas. Key-words: Land use; urban areas; vegetated area, remote sensing; MAXVER.