Abstract
The southeastern Amazon region has been intensively occupied by human settlements over the past three decades. To evaluate the effects of human settlements on land-cover and land-use (LCLU) changes over time in the study site, we evaluated multitemporal Landsat images from the years 1984, 1994, 2004, 2013 and Sentinel to the year 2017. Then, we defined the LCLU classes, and a detailed “from-to” change detection approach based on a geographic object-based image analysis (GEOBIA) was employed to determine the trajectories of the LCLU changes. Three land-cover (forest, montane savanna and water bodies) and three land-use types (pasturelands, mining and urban areas) were mapped. The overall accuracies and kappa values of the classification were higher than 0.91 for each of the classified images. Throughout the change detection period, ~47% (19,320 km2) of the forest was preserved mainly within protected areas, while almost 42% (17,398 km2) of the area was converted from forests to pasturelands. An intrinsic connection between the increase in mining activity and the expansion of urban areas also exists. The direct impacts of mining activities were more significant throughout the montane savanna areas. We concluded that the GEOBIA approach adopted in this study combines the advantages of quality human interpretation and the capacities of quantitative computing.
Highlights
Digital image classification of remote sensing data has been intensified over the past few decades with the advancement of computer science technology, accessibility of satellite-based earth observations and availability of software to process digital images
geographic object-based image analysis (GEOBIA) has been able to be applied due to the advent of very high-resolution images that show mainly land-cover and land-use (LCLU) changes within urban areas [6], forests [7], and agriculture areas [8], where image objects are digitally constructed from dozens to hundreds of pixels [9,10]
We investigate LCLU changes in the context of a tropical region, the Itacaiúnas River watershed (IRW) that encompasses the Carajás Mineral Province (CMP), which is one of the largest mining provinces in the world and is located in an arc of deforestation caused by large-scale human settlements over the last 30 years in the southeastern Amazon region (Figure 1)
Summary
Digital image classification of remote sensing data has been intensified over the past few decades with the advancement of computer science technology, accessibility of satellite-based earth observations and availability of software to process digital images. In 2000, the first software using the geographic object-based image analysis (GEOBIA) approach was commercialized [3]. The GEOBIA approach has many advantages over the pixel-based classification This approach can remove “salt-and-pepper” effects, and a large set of features (e.g., objects generated from the spectral, spatial and textural properties of a group of pixels) can be produced as additional information to improve image classification accuracy [5]. GEOBIA has been able to be applied due to the advent of very high (spatial)-resolution images that show mainly land-cover and land-use (LCLU) changes within urban areas [6], forests [7], and agriculture areas [8], where image objects are digitally constructed from dozens to hundreds of pixels [9,10]. The use of GEOBIA has been increasingly expanded to include moderate-resolution images if a higher hierarchical image-object level is applied [11,12,13,14]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
