Evaluating the quality of the Digital Elevation Models produced from ASTER stereoscopy for topographic mapping in the Brazilian Amazon Region

Abstract

Brazilian Amazon is a vast territory rich in natural renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be up-dated or re-mapped. In this paper, the feasibility of using elevation generated from orbital ASTER stereo-pairs images for topographic mapping was investigated for the mountainous relief in the Serra dos Carajás, Pará. The quality of information derived from these optical images was evaluated regarding field altimetric measurements. Precise topographic field information acquired from Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic Digital Elevation Models (DEMs) and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two approaches: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of trend analysis and accuracy. The investigation has shown that the altimetric accuracy from ASTER fulfilled the Brazilian Map Accuracy Standards elevation requirements for 1:100,000 A Class. In addition, ASTER can provide up-dated planimetric information that is also necessary for cartographic production. Thus, when the environment condition allows the acquisition of stereo-pairs, the use of ASTER can be considered an alternative for semi-detailed topographic mapping in similar environments of the Brazilian Amazon.

Assessment of Flooded Areas Projections and Floods Potential Impacts Applying Remote Sensing Imagery and Demographic Data

“…Errors in topography elevation of Aster's DEM has been reported to be around 26 meters (Oliveira et al, 2009), which is large enough to introduce large uncertainty in flooded area estimations and, then, in the estimated number of affected people. In addition to the uncertainties of climate change scenarios generated from different model, it is clear that the absence of an accurate digital elevation model could introduce similar or bigger uncertainty than climate scenarios in the results.…”

Levantamento Dos Campos Limpos Úmidos No Parque Estadual Do Jalapão, To, Por Meio De Fotografias Aéreas Analógicas: Abordagem Metodológica

“…De acordo com os resultados obtidos pelos autores, o PEC atingiu a classe B na escala 1:30.000 para a altimetria e classe A para a escala 1:30.000 para a planimetria. Já Oliveira & Paradella (2009), em estudo realizado em relevo montanhoso na Serra dos Carajás/PA, usando imagens ASTER, obtiveram acurácia altimétrica do modelo digital de elevação (MDE) que atendeu os requisitos para enquadramento na Classe A para a escala 1:100.000 do PEC. Embora esses dois últimos estudos tenham utilizado imagens ASTER com mesma resolução espacial, os resultados obtidos na qualidade posicional não atingiram a mesma escala fi nal de classifi cação exigida pelo PEC.…”

“…A área de estudo abriga diferentes fi tofi sionomias, com predominância da formação campestre (SANTIAGO e PEREIRA JÚNIOR, 2010;SANO et al, 2010), onde se destaca a ocorrência de capim dourado (Syngonanthus nitens (Bong.) Ruhland (Eriocaulaceae), importante produto de exploração não-fl orestal da região (PACIFICO et al, 2011;SCHMIDT et al, 2011;SCHMIDT & TICKTIN, 2012).…”

Robust methods for assessing the accuracy of linear interpolated DEM

“…Much attention has been paid to exploring the effects of all sorts of interpolation models upon various kinds DEM data with different spatial resolutions (Chaplot et al, 2006). It is well-known that the common indicator for assessing the DEM accuracy can be root mean squared error (RMSE), which has been employed together with trend analysis to evaluate the quality of the DEM produced from ASTER stereoscopy (de Oliveira and Paradella, 2009). Basically, the following three factors are mainly perceived to account for the interpolated DEM uncertainty: (1) source data errors from the spatial data acquisition stage; (2) the employed DEM interpolation models; (3) the complexity level of terrain variation (Shi et al, 2014).…”