Avalia？？o de técnicas de classifica？？o digital de imagens landsat em diferentes padr？es de cobertura da terra em Rond？nia

remotely sensed imagery has been widely used for land use/cover classification thanks to the periodic data acquisition and the widespread use of digital image processing systems offering a wide range of classification algorithms. the aim of this work was to evaluate some of the most commonly used supervised and unsupervised classification algorithms under different landscape patterns found in rond？nia, including (1) areas of mid-size farms, (2) "fish-bone" settlements and (3) a gradient of forest and cerrado (brazilian savannah). comparison with a reference map based on the kappa statistics resulted in good to superior indicators (best results - k-means: k=0.68; k=0.77; k=0.64 and maxver: k=0.71; k=0.89; k=0.70 respectively for three areas mentioned). results show that choosing a specific algorithm requires to take into account both its capacity to discriminate among various spectral signatures under different landscape patterns as well as a cost/benefit analysis considering the different steps performed by the operator performing a land cover/use map. it is suggested that a more systematic assessment of several options of implementation of a specific project is needed prior to beginning a land use/cover mapping job.