Land-Use Mapping in a Mixed Urban-Agricultural Arid Landscape Using Object-Based Image Analysis: A Case Study from Maricopa, Arizona

Land-use mapping is critical for global change research. In Central Arizona, U.S.A., the spatial distribution of land use is important for sustainable land management decisions. The objective of this study was to create a land-use map that serves as a model for the city of Maricopa, an expanding urban region in the Sun Corridor of Arizona. We use object-based image analysis to map six land-use types from ASTER imagery, and then compare this with two per-pixel classifications. Our results show that a single segmentation, combined with intermediary classifications and merging, morphing, and growing image-objects, can lead to an accurate land-use map that is capable of utilizing both spatial and spectral information. We also employ a moving-window diversity assessment to help with analysis and improve post-classification modifications

Delphi 2 Creative Technologies - Definiens AG: eine kurze Geschichte ueber den langen Kampf um ein hohes Ziel Abschlussbericht

Available from TIB Hannover: F04B448 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDeutsche Bundesstiftung Umwelt, Osnabrueck (Germany)DEGerman

Informationsvermittlung und Ausbildung fuer ein innovatives Verfahren der Umweltkartierung (eCognition) Endbericht

Available from TIB Hannover: F03B1419 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDeutsche Bundesstiftung Umwelt, Osnabrueck (Germany)DEGerman

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area

An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

Rice is the staple food for half of the world’s population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China’s Environmental Satellite (HJ-1A/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%–4%. The comparison between the estimated rice areas and the State’s statistics shows underestimated values with a percentage difference of −34.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area