Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA
There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an
... esent an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km 2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in OPEN ACCESS Remote Sens. 2014, 6 11373 image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods. Keywords: object-based image analysis; OBIA; orthophoto; land cover classification; urban landscape mapping; Indiana Introduction Detailed land-cover mapping is an important research topic in land change science and landscape planning nowadays. Human activities are constantly changing land cover patterns and influencing biophysical processes    . In turn, human behaviors evolve over time as a result of such human-nature interaction in social-ecological systems [4, 5] . To estimate urban sprawl and population so as to plan transportation and infrastructures accordingly, detailed and accurate Land-Use and Land-Cover (LULC) maps generated from high-resolution images are desired in the decision-making process to manage sustainable land resources [6, 7] . Complexity of heterogeneous land systems and the increasing demands for fine-scale land cover mapping challenges classification approaches and techniques for detailed land mapping, to support research communities in land-use change [8, 9] , urban planning [10,11], urban environment and ecology    , vegetation managements [2,15-17], impervious surfaces mapping [18, 19] , and urban heat island effects [20, 21] . Advances in the remote sensing-based data acquisition reveal opportunities for land-cover mapping at fine resolution. However, increased sophistication in image processing should be incorporated to achieve high mapping/classification accuracy from high-resolution images. Traditional pixel-based classification approaches barely suffice the requirement of accurate and detailed land-cover classification [14,    due to not accounting for meaningful image objects at different scales and resulting in the "salt and pepper" effect/noise (speckles) [3,    . To address the challenges of classifying high-resolution remote sensing imagery, researchers are switching from traditional pixel-based methods to alternative approaches in image processing, namely the Object-Based Imagery Analysis (OBIA) [14, 24] . The OBIA approach, advancing in its image segmentation, groups pixels into image objects as its basic unit to avoid or minimize "noise" within ground objects. In addition, it integrates characteristics within the spectral domain of the high-resolution imagery      . Among all currently available LULC data, the National Land Cover Database (NLCD) and National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) represent the highest spatial resolution data that cover the State of Indiana. The NLCD has 30-meter resolution and is generated from the Landsat Thematic Mapper (TM) data. NLCD map products are released every five years, including NLCD 1992including NLCD , 2001including NLCD , 2006including NLCD and 2011. The NASS CDL images with 30-meter resolution were produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS). The images were collected during the growing season by USDA-NASS  and for any area that is classified as non-agriculture types, using the NLCD data, its original image was masked and replaced with the NLCD data. Therefore, although the NASS CDL provides more detailed crop information Author Contributions Xiaoxiao Li and Guofan Shao conceived the research; Xiaoxiao Li carried out the research; Xiaoxiao Li and Guofan Shao wrote the paper.