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Forest type identification by random forest classification combined with SPOT and multitemporal SAR data

Journal of Forestry Research 2018年第5期29卷 1407-1414页

作者： Ying Yu Mingze Li Yu Fu School of Forestry Northeast Forestry University

We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.

关键词： Random forest classification multitemporal Multisource remote sensing data Polarization decomposition

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Land cover classification of remotely sensed image with hierarchical iterative method

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Progress in Natural Science:Materials International 2005年第5期15卷 442-447页

作者： LI Peijun* and HUANG Yingduan(Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China) Inst. of Remote Sensing and GIS Peking Univ. Beijing 100871 China

Based on the analysis of the single-stage classification results obtained by the multitemporal SPOT 5 and Landsat 7 ETM+ multispectral images separately and the derived variogram texture, the best data combinations for each land cover class are selected, and the hierarchical iterative classification is then applied for land cover mapping. The proposed classification method combines the multitemporal images of different resolutions with the image texture, which can greatly improve the classification accuracy. The method and strategies proposed in the study can be easily transferred to other similar applications.

关键词： multitemporal iterative classification texture land cover image classification.

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Analysis of Urban Change in Shenzhen City Based on Landsat Archived Data

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Journal of Computer and Communications 2018年第11期6卷 146-154页

作者： Tingting Chen Hermann Josef Kaufmann Navigation and Remote Sensing Group Institute of Space Science Shandong University Weihai China

With the ongoing development of economy and urbanization in China, the change of land use types has attracted more and more attention. In this study we focused on the urban development of Shenzhen City, Guangdong Province, analyzing Landsat 5 TM and Landsat 8 OLI data. We used an SVM based classification, a land transfer matrix approach, a directional growth analysis method and we calculated the inversion of land surface temperature to derive information of land cover changes that occurred in the time period between 1987 and 2017. The results are combined with Shenzhen’s economy, transportation policy and other aspects to find the driving forces of the urban development. The results show that during the observed 30 years, the area of construction land has increased significantly. Most of it is converted from other lands, and some of them are reclaimed. Most rapidly developing are areas west and northwest of the Bao’an, Nanshan and Longhua. The vegetated areas decreased slightly. Caused by the continuous increase of the construction land, the so-called heat island effect emerges slightly but continuously.

关键词： Change Detection Urbanization Heat Island multitemporal Analysis Landsat Data

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Impact of Land Use/Land Cover Change on Surface Temperature Condition of Awka Town, Nigeria

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Journal of Geographic Information System 2017年第6期9卷 763-776页

作者： Chukwudi P. Nzoiwu Emmanuel I. Agulue Samuel Mbah Chidera P. Igboanugo Department of Geography and Meteorology Nnamdi Azikiwe University Awka Nigeria Department of Geography Nwafor Orizu College of Education Nsugbe Nigeria

This paper is aimed at identifying the land use/cover types in Awka in relation to their temporal dynamics, the extent of land use change in the city and effects of land use change on surface temperature. multitemporal Landsat TM, ETM+ and OLI imageries were obtained at 15 years interval for 1986, 2000 and 2015 respectively. Image classification was conducted using supervised classification method. The result showed that built-up area has been on the increase, expanding from 9.55 sqkm in 1986 to 21.3 sqkm in 2000 and 21.45 sqkm in 2015. Cultivated lands have maintained a steady decline since 2000 having lost about 3.29 sqkm of its area. Similarly, vegetation, made up of dense, savanna and riparian, has been on a decline from a total of 33.69sqkm in 1986 to 21.407 sqkm losing about 12.29 sqkm of its area and increased by a mere 4.07 sqkm in 2015. These alterations had given rise to an average increase of 2.2°C in surface radiant temperature. This study recommends that relevant government planning agencies (ACTDA, ASHDC, etc.) should factor in the concept of greening and green spaces into their development policies and strategies to ensure that fair, conducive microclimate and sustainable environment is maintained in the Awka urban area.

关键词： Landsat Land Use/Cover Supervised Classification multitemporal Land Surface Temperature

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Land Cover Classification and Forest Change Analysis, Using Satellite Imagery-A Case Study in Dehdez Area of Zagros Mountain in Iran

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Journal of Geographic Information System 2011年第1期3卷 1-11页

作者： Ali Asghar Torahi Suresh Chand Rai 不详

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in mountainous areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) and ASTER data in Zagros mountains of Iran for 1990, 1998, and 2006.Land-use/cover mapping is achieved through interpreta-tion of Landsat TM satellite images of 1990, 1998 and TERRA-ASTER image of 2006 using ENVI 4.3. Based on the Anderson land-use/cover classification system, the land-use and land-covers are classified as forest land, rangeland, water bodies, agricultural land and residential land. The unsupervised image classifi-cation method carried out prior to field visit, in order to determine strata for ground truth. Fieldwork carried out to collect data for training and validating land-use/cover interpretation from satellite image of 2006, and for qualitative description of the characteristics of each land-use/cover class. The land-use/cover maps of 1990, 1998 and 2006 were produced by using supervised image classification technique based on the Maxi-mum Likelihood Classifier (MLC) and 132 training samples. Error matrices as cross-tabulations of the mapped class vs. the reference class were used to assess classification accuracy. Overall accuracy, user’s and producer’s accuracies, and the Kappa statistic were then derived from the error matrices. A multi-date post-classification comparison change detection algorithm was used to determine changes in land cover in three intervals, 1990–1998, 1998–2006 and 1990–2006. To evaluate the change maps for the 1990 to 2006 interval, we randomly sampled the areas that classified as change and no-change and determined whether they were correctly classified. The maps showed that between 1990 and 2006 the amount of forest land de-creased from 67% to 38.5% of the total area, while rangelands, agriculture, settlement and surface water in-creased from 30.8% to 45%, 1.2% to 7.0%, 0.3% to 7.

关键词： Land Cover Classification multitemporal Change Detection Landsat ASTER

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