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Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using object-based Approach: A Case Study from Almeria (Spain)

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REMOTE SENSING 2018年第11期10卷 1751-1751页

作者： Nemmaoui, Abderrahim Aguilar, Manuel A. Aguilar, Fernando J. Novelli, Antonio Garcia Lorca, Andres Univ Almeria Dept Engn Ctra Sacramento S-N La Canada De San Urbano 04120 Almeria Spain Eurac Res Inst Renewable Energy Via A Volta 13-A I-39100 Bolzano Italy Univ Almeria Dept Geog Ctra Sacramento S-N Almeria 04120 Spain

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (F around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

关键词： Landsat 8 Sentinel-2 WorldView-2 time series object-based classification greenhouse mapping crop types classification

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Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar

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REMOTE SENSING 2021年第16期13卷 3303页

作者： Anderson, Connor J. Heins, Daniel Pelletier, Keith C. Bohnen, Julia L. Knight, Joseph F. Univ Minnesota Dept Forest Resources 1530 Cleveland Ave N St Paul MN 55108 USA Univ Minnesota Dept Fisheries Wildlife & Conservat Biol 2003 Upper Buford Circle St Paul MN 55108 USA

Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue;RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer's accuracy for the Phragmites class ranged from 3 to 76%, and the user's accuracies were above 90%. The Not Phragmites class had user's and producer's accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM.

关键词： Phragmites australis UAS invasive species object-based classification OBIA

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Utility of Landsat Image in the Study of Land Cover and Land Surface Temperature Change

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Procedia Environmental Sciences 2011年 10卷 1287-1292页

作者： Zhi-qiang Lv Qi-gang Zhou Tourism and Land Department Chongqing Technology and Business University Chongqing China Chongqing Engineering Technology Research Center for Information Mangement in Developent Chongqing Technology and Business University Chongqing China

Scientists have aimed at exploring land use and land cover (LULC) and their environmental influence in order to improve our understanding of the causes and consequences of these phenomena. This study addresses and use and land cover change (LUCC) in the upper reaches of Pearl River Delta, China, from 1990 to 2008. based on remotely sensed images of Landsat, LUCC and land surface temperature were assessed. Results showed that land surface temperature was highly influenced by the LUCC from 1990 to 2008. An examination of the relationship between the LULC and LST maps was finally conducted to comprehension their interactions. Results indicated that Landsat image was effective for quantifying the heat environment and providing reliable measurements of land use change. LST was found to be positively correlated with impervious surface but negatively correlated with vegetated land. Each temperature zone was associated with a dominant LULC category.

关键词： Land use land surface temperature object-based classification TM/ETM+ Fast developing region

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Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area

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REMOTE SENSING 2019年第3期11卷 333-333页

作者： Hoa Thi Tran Campbell, James B. Wynne, Randolph H. Shao, Yang Son Viet Phan Virginia Polytech Inst & State Univ Virginia Tech Blacksburg VA 24061 USA Hanoi Univ Min & Geol Hanoi Vietnam Virginia Tech Dept Geog Blacksburg VA 24061 USA

Drought is a dry-weather event characterized by a deficit of water resources in a period of year due to less rainfall than normal or overexploitation. This insidious hazard tends to occur frequently and more intensively in sub-humid regions resulting in changes in the landscape, transitions in agricultural practices and other environmental-social issues. The study area is in the sub-humid region of the northern coastal zone of Binh Thuan province, Vietnam-Tuy Phong district. This area is indicated as a subject of prolonged droughts during 6-month dry seasons, which have occurred more frequently in recent years. Associated with economic transitions in agricultural practicing, urbanization, and industrialization, prolonged droughts have resulted in rapid changes in land use and land cover (LULC) in Tuy Phong, especially in three coastal communes: Binh Thanh, Lien Huong, and Phuoc The. A bi-temporal analysis using high-resolution data, the 2011 WorldView2 and the 2016 GeoEye1, was examined to assess LULC changes from observed severe droughts in those three communes. Results showed a dramatic reduction in the extent of hydrological systems (about 20%), and significant increases of tree canopies in urban areas and near the coastal areas (approximately 76.8%). Paddy fields declined by 51% in 2016;such areas transitioned to inactive status or were alternated for growing drought-tolerant plants. Shrimp farming experienced a recognizable decrease by approximately 44%. The 2014 map and field observations during summer 2016 provide references for object-based classification and validation. Overall agreement of results is about 85%.

关键词： Vietnam Binh Thuan sub-humid region drought-human impacts LULC high-resolution images object-based classification

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Contribution of time-series data cubes to classify urban vegetation types by remote sensing

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URBAN FORESTRY & URBAN GREENING 2023年 79卷

作者： Adorno, Bruno Vargas Korting, Thales Sehn Amaral, Silvana Natl Inst Space Res INPE Earth Observat & Geoinformat Div Sao Jose Dos Campos Brazil Natl Inst Space Res INPE Grad Program Remote Sensing Ave Astronautas 1757 BR-12227010 Sao Jose Dos Campos SP Brazil

Mapping urban vegetation types is important for urban planning and assessing environmental justice. Nowadays, despite data cubes projects are providing Analysis Ready Data to facilitate time-series analysis, we did not found studies employing these data for improving urban vegetation mapping. By relying solely on open data and software, this work proposes and evaluates the integration of time-series data cubes in a hybrid image classifi-cation method to map the intra-urban space, differentiating Tree cover and Herb-shrub. The urban area of Goiania, Goias, Brazil, is the study area. The hybrid method combined object-based classification of a pan -sharpened CBERS-4A WPM image (spatial resolution of 2 m) with the pixel-based classification of Sentinel-2 MSI time-series data cubes (10 m). Both approaches used the Random Forest algorithm. objects from the CBERS-4A segmentation composed the spatial unit of analysis and the class assignment depended on the Sentinel-2 time-series urban land cover probabilities. based on both Maps probabilities, Shannon entropy was calculated to attribute the final urban land cover to the objects. Urban land cover probabilities presented similar spatial distribution patterns for both classification approaches. Regarding the thematic maps, the Herb-shrub cover area was 35% higher in Sentinel-2 time-series classification than in GEOBIA classification, but Tree cover was 21% lower. In general, 75% of the study area was equally classified by the initial approaches. However, for 9% of the remaining area, the hybrid classification improved vegetation classes accuracies by 35%, contributing to the vegetation covers identification. Thus, this study contributes to methodological procedures for urban land cover study and demonstrates that hybrid maps based on open data are effective to reduce classification mistakes, allowing more accurate monitoring, planning, and designing of different urban vegeta-tion types. Future research efforts should focus on

关键词： CBERS-4A WPM Multisource image analysis object-based classification Per-pixel classification Sentinel-2 MSI

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Comparison of machine learning algorithms for mangrove species identification in Malad creek, Mumbai using WorldView-2 and Google Earth images

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JOURNAL OF COASTAL CONSERVATION 2022年第5期26卷 44-44页

作者： Nagarajan, Padmageetha Rajendran, Lakshana Pillai, Nithin D. Lakshmanan, Gnanappazham Anna Univ Coll Engn Guindy Dept Civil Engn Chennai 600025 Tamil Nadu India Indian Inst Space Sci & Technol Dept Earth & Space Sci Valiamala 695547 Kerala India

This study focuses on the comparison of machine learning algorithms for classifying the mangrove species of Malad creek, Mumbai, India using pixel-based and object-based approaches. As mangrove forests are complex in structure and require high resolution remotely sensed images for classifying them at the species level, traditional classification methods of remote sensing may not be suitable. In order to find an appropriate method, pixel-based random forest and K Nearest Neighbor classifiers and object-based random forest and K Nearest Neighbor classifiers were tested and compared using WorldView - 2 images. In addition, the best resolution RGB images from Google Earth were classified using random forest and K Nearest Neighbor algorithms in object-based approach and compared. The classification results showed that both object-oriented and pixel-based methods could identify the major mangrove species at the community level. However, the performance of object-based random forest classifier was better than other classification approaches in both WorldView-2 and Google Earth images with overall accuracies of 92.53% and 80.72% and kappa coefficients of 0.89 and 0.73. The overall results showed the potential of random forest classifier in object-based method for classifying the mangroves at the species level.

关键词： Random forest K Nearest Neighbor Pixel-based classification object-based classification Malad creek

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Probability of glacial lake outburst flooding in the Himalaya

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RESOURCES ENVIRONMENT AND SUSTAINABILITY 2021年 5卷

作者： Mohanty, Litan Maiti, Sabyasachi Indian Inst Technol Kharagpur Dept Geol & Geophys Kharagpur India

The present study aims to determine the probability of glacial lake outburst flooding (GLOF) in the Himalaya. Here, a total of 4198 glacial lakes with larger dimensions (>0.01 km2) were considered. A fusion between object-based classification (OBC) and Analytical Hierarchy Process (AHP) was applied for unbiased results, involving the fifteen most essential predictors. The previously studied threshold values of predictors were used in OBC to determine GLOF prone lakes. On the other hand, earthquake, lake freeboard, moraine dam steepness, avalanches, and lake size change rate were prioritized for the AHP method. Individually, OBC and AHP methods identified 107 and 224 critical lakes. However, as a fusion of the above techniques (GIS overlay analysis), our study identified 60 critical lakes, followed by 164 and 3974 glacial lakes with medium and lower probability of GLOF. 95% of the higher GLOF-probable lakes are concentrated in the Kosi (33), Pelkhu (7), and Tista (5) river basins;sub-regionally coincide with Sikkim, Everest, Bhutan and Langtang region.

关键词： Glacial lake Himalaya object-based classification AHP GLOF

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Adaptive scale selection in multiscale segmentation based on the segmented object complexity of GF-2 satellite image

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ARABIAN JOURNAL OF GEOSCIENCES 2019年第22期12卷 699-699页

作者： Wang, Fang Yang, Wunian Ren, Jintong Chengdu Univ Technol Key Lab Geosci Spatial Informat Technol Minist Land & Resources Chengdu 610059 Sichuan Peoples R China Neijiang Normal Univ Coll Geog & Resources Sci Neijiang 641100 Peoples R China

Image segmentation is the premise and key step of object-oriented classification, but scale selection remains a challenge in image segmentation. Over the years, scale selection methods of image segmentation have been extensively explored and developed. When the scale is chosen, all the features are generally extracted from images in these methods. In addition, in these methods, an optimal scale is generally selected based on the pre-estimation of the statistical variance of remote sensing images or determined by the post-segmentation evaluation results, rather than during the segmentation. In this study, with the central and eastern parts of Longchang City in Mid-Sichuan Hilly Region as the study area, based on the Gaofen-2 (GF-2) image, an adaptive scale selection method was proposed to determine the optimal scale of each segmentation object during the segmentation. First, the single-scale optimal image segmentation is determined by an unsupervised evaluation method which uses weighted variance and Moran's I to measure global intra-segment homogeneity and inter-segment heterogeneity, respectively. Then, spectral, texture, and shape features of each segmented object were extracted to construct the complexity index function, and the optimal scale of the segmentation objects was determined through the iterative calculation according to the threshold value. The proposed method was applied to process GF-2 image to obtain the segmentation results and classification map, compared with the methods of determining the optimal scale through estimation local statistic variances of image and post-evaluation of segmentation results. The experimental results showed that the proposed method is of practical helpfulness and effectiveness for generating the optimal scale matching the actual ground objects.

关键词： object features complexity Multiscale segmentation Scale selection object-based classification

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Accurate mapping of seaweed farms with high-resolution imagery in China

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GEOCARTO INTERNATIONAL 2023年第1期38卷

作者： Jin, Runjie Ye, Zhanjiang Chen, Shuangshuang Gu, Jiali He, Junyu Huang, Lei Christakos, George Agusti, Susana Duarte, Carlos M. M. Wu, Jiaping Zhejiang Univ Ocean Coll Zhoushan Peoples R China Minist Nat Resources Applicat Ctr Ocean Satellites Beijing Peoples R China King Abdullah Univ Sci & Technol Red Sea Res Ctr Thuwal Saudi Arabia

Seaweed aquaculture is vital in protecting the marine eco-environment and mitigating climate change. China generates more than half of the world's total seaweed production. However, despite multiple local studies, accurate and reliable information on broad-scale seaweed farms is still scarce. Using an object-based method to classify 3 m spatial resolution Planet Scope images along offshore China, a total of 129,494 ha of cultured seaweed was identified and delineated with an overall accuracy of 95.70% and a KAPPA index of 0.912, respectively. Then, a seaweed map in offshore China in 2018-2019 was developed. The results provided basic information about seaweed farms in China. The approach reported in this work is accurate and efficient, which can be used to replace the conventional method to obtain the culture seaweed information. This study can be of reference for mapping seaweed on a broader or global scale.

关键词： Cultured seaweed areal extent spatial distribution object-based classification China

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The Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey

The Attribute Accuracy Assessment of Land Cover Data in the ...

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“地理空间数据库与位置服务”国际学术会议

作者： Xiaole Ji Xiao Niu Shandong Provincial Institute of Land Surveying and Mapping Jinan Shandong ProvinceChina

With the widespread national survey of geographic conditions,object-based data has already became the most common data organization pattern in the area of land cover *** the accuracy of object-based land cover data is related to lots of processes of data production,such like the efficiency of inside production and the quality of final land cover ***,there are a great deal of requirements of accuracy assessment of object-based classification *** approaches for accuracy assessment in surveying and mapping are not aimed at land cover *** is necessary to employ the accuracy assessment in imagery *** traditional pixel-based accuracy assessing methods are inadequate for the *** measures we improved are based on error matrix and using objects as sample units,because the pixel sample units are not suitable for assessing the accuracy of object-based classification *** to pixel samples,we realize that the uniformity of object samples has *** order to make the indexes generating from error matrix reliable,we using the areas of object samples as the weight to establish the error matrix of object-based image classification *** compare the result of two error matrixes setting up by the number of object samples and the sum of area of object *** error matrix using the sum of area of object sample is proved to be an intuitive,useful technique for reflecting the actual accuracy of object-based imagery classification result.

关键词： Land cover Accuracy assessment National geographic survey Error matrix object-based classification Weight High-resolution remotely sensed imagery

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