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Co-Evolution of Emerging Multi-Cities: Rates, Patterns and Driving Policies Revealed by continuous change detection and classification of Landsat Data

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REMOTE SENSING 2020年第18期12卷 2905-2905页

作者： Liu, Maochou Liu, Shuguang Ning, Ying Zhu, Yu Valbuena, Ruben Guo, Rui Li, Yuanyuan Tang, Wenxi Mo, Dengkui Rosa, Isabel M. D. Kutia, Mykola Hu, Wenmin Cent South Univ Forestry & Technol Natl Engn Lab Appl Technol Forestry & Ecol South Changsha 410004 Peoples R China Cent South Univ Forestry & Technol Coll Biol Sci & Technol Changsha 410004 Peoples R China Bangor Univ Sch Nat Sci Thoday Bldg Bangor LL57 2UW Gwynedd Wales Cent South Univ Forestry & Technol Coll Forestry Changsha 410004 Peoples R China Bangor Univ Joint Unit Bangor Coll China Bangor Gwynedd Wales Cent South Univ Forestry & Technol Changsha 410004 Peoples R China

The co-evolution of multi-cities has emerged as the primary form of urbanization in China in recent years. However, the processes, patterns, and coordination are not well characterized and understood, which hinders the understanding of the driving forces, consequences, and management of polycentric urban development. We used the continuous change detection and classification (CCDC) algorithm to integrate all available Landsat 5, 7, and 8 images and map annual land use and land cover (LULC) from 2001 to 2017 in the Chang-Zhu-Tan urban agglomeration (CZTUA), a typical urban agglomeration in China. Results showed that the impervious surface in the study area expanded by 371 km(2)with an annual growth rate of 2.25%, primarily at the cost of cropland (169 km(2)) and forest (206 km(2)) during the study period. Urban growth has evolved from infilling being the dominant type in the earlier period to mainly edge-expansion and leapfrogging in the core cities, and from no dominant type to mainly leapfrogging in the satellite cities. The unfolding of the "cool center and hot edge" urban growth pattern in CZTUA, characterized by higher expansion rates in the peripheral than in the core cities, may signify a new form of the co-evolution of multi-cities in the process of urbanization. Detailed urban management and planning policies in CZTUA were analyzed. The co-evolution of multi-cities principles need to be studied in more extensive regions, which could help policymakers to promote sustainable and livable development in the future.

关键词： Landsat urban expansion time series continuous change detection and classification Chang-Zhu-Tan urban agglomeration landscape dynamics

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Near real-time monitoring of carbon effects from continuous forest change in rapidly urbanizing region of China from 2000 to 2020

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FOREST ECOSYSTEMS 2025年 13卷

作者： Zhang, Dou Tang, Xiaojing Lu, Shuaizhi Geng, Xiaolei Yu, Zhaowu Xie, Yujing Peng, Si Wang, Xiangrong Zhejiang Sci Tech Univ Sch Civil Engn & Architecture Hangzhou 310018 Peoples R China Fudan Univ Dept Environm Sci & Engn Shanghai 200438 Peoples R China James Madison Univ Sch Integrated Sci Harrisonburg VA 22807 USA Huzhou Univ Inst Sustainable Dev Huzhou 313000 Peoples R China Jiangsu Univ Sch Environm & Safety Engn Zhenjiang 212013 Peoples R China

Forest carbon sinks are crucial for mitigating urban climate change. Their effectiveness depends on the balance between gross carbon losses and gains. However, quantitative and continuous monitoring of forest change/ disturbance carbon fluxes is still insufficient. To address this gap, we integrated an improved spatial carbon bookkeeping (SBK) model with the continuous change detection and classification (CCDC) algorithm, long-term Landsat observations, and ground measurements to track carbon emissions, uptakes, and net changes from forest cover changes in the Yangtze River Delta (YRD) of China from 2000 to 2020. The SBK model was refined by incorporating heterogeneous carbon response functions. Our results reveal that carbon emissions (-3.88 Tg C & sdot;year-1) were four times greater than carbon uptakes (0.93 Tg C & sdot;year-1) from forest cover changes in the YRD during 2000-2020, despite a net forest cover gain of 10.95 x 104 ha. These findings indicate that the carbon effect per hectare of forest cover loss is approximately 4.5 times that of forest cover gain. The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change. Furthermore, carbon uptakes from forest gains in the YRD during 2000-2020 could only offset 0.28% of energy-related carbon emissions from 2000 to 2019. Urban and agricultural expansions accounted for 37% and 10% of carbon emissions, respectively, while the Grain for Green Project contributed to 45% of carbon uptakes. Our findings underscore the necessity of understanding the asymmetric carbon effects of forest cover loss and gain to accurately assess the capacity of forest carbon sinks.

关键词： continuous forest cover change Asymmetric carbon effects continuous change detection and classification (CCDC) algorithm Improved spatial carbon bookkeeping (SBK) model Google Earth Engine (GEE)

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OBSERVING THE SPATIOTEMPORAL DYNAMICS OF TEA PLANTATIONS IN A TROPICAL MOUNTAINOUS REGION USING MACHINE LEARNING 44

OBSERVING THE SPATIOTEMPORAL DYNAMICS OF TEA PLANTATIONS IN ...

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44th Asian Conference on Remote Sensing, ACRS 2023

作者： Huang, Cho-Ying Shen, Zih-Yu Chen, Tzu-Hsin Karen Department of Geography National Taiwan University Taipei10617 Taiwan Research Center for Future Earth National Taiwan University Taipei10617 Taiwan Department of Urban Design and Planning University of Washington SeattleWA98195 United States

ISBN: (纸本)9781713893646

Tea is a popular drink worldwide and a major cash crop in mountainous agricultural regions in Taiwan. However, due to the rugged terrain, these areas are difficult to manage, and frequent fog, cloud cover, and spectral complexity hinder remote sensing applications. With only 92 observations in 221 satellite images (2019-2021) on average for each pixel in the Greater Ali-Mountain tea plantation region of Southern Taiwan (the study site), a systematic method may be necessary to effectively map tea farms. This study aims to evaluate the feasibility of classifying tea plantations in the study region using satellite imagery with machine learning. We utilized Sentinel-2 surface reflectance images to generate annual, seasonal, and continuous change detection and classification land use and land cover (LULC) maps. Two machine learning methods, Random Forests (RF), and U-net with Resnet-18 backbone, were employed to classify five types of LULC. After validating the results, we found that U-net had higher accuracy than RF with significantly higher efficiency in identifying tea farms compared to RF. Using U-net with the seasonal approach resulted in the highest overall accuracy of 0.949, with the tea farm producer's accuracy and user's accuracy being 0.916 and 0.939, respectively. Our findings suggest that U-net is suitable for identifying tea farms due to its ability to augment training data, to use an encoder-decoder structure, and to incorporate skip-connections, which capture image features more effectively and prevent the loss of critical information. This approach offers significant advantages in image interpretation. Moreover, the method shows promising potential for mapping other mountain evergreen crops, such as fruit and coffee trees. © 2023 ACRS. All Rights Reserved.

关键词： continuous change detection and classification deep learning satellite images tea farm time series

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Quantifying Urban Vegetation Dynamics from a Process Perspective Using Temporally Dense Landsat Imagery

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REMOTE SENSING 2021年第16期13卷 3217-3217页

作者： Yu, Wenjuan Zhou, Weiqi Dawa, Zhaxi Wang, Jia Qian, Yuguo Wang, Weimin Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Chinese Acad Sci Beijing Urban Ecosyst Res Stn Res Ctr Ecoenvironm Sci Beijing 100085 Peoples R China Shenzhen Environm Monitoring Ctr State Environm Protect Sci Observat & Res Stn Eco Shenzhen 518049 Peoples R China

Urban vegetation can be highly dynamic due to the complexity of different anthropogenic drivers. Quantifying such dynamics is crucially important as it is a prerequisite to understanding its social and ecological consequences. Previous studies have mostly focused on the urban vegetation dynamics through monotonic trends analysis in certain intervals, but not considered the process which provides important insights for urban vegetation management. Here, we developed an approach that integrates trends with dynamic analysis to measure the vegetation dynamics from the process perspective based on the time-series Landsat imagery and applied it in Shenzhen, a coastal megacity in southern China, as an example. Our results indicated that Shenzhen was turning green from 2000-2020, even though a large-scale urban expansion occurred during this period. Approximately half of the city (49.5%) showed consistent trends in greening, most of which were located in the areas within the ecological protection baseline. We also found that 35.3% of the Shenzhen city experienced at least a one-time change in urban greenness that was mostly caused by changes in land cover types (e.g., vegetation to developed land). Interestingly, 61.5% of these lands showed trends in greening in the recent change period and most of them were distributed in build-up areas. Our approach that integrates trends analysis and dynamic process reveals information that cannot be discovered by monotonic trends analysis alone, and such information can provide insights for urban vegetation planning and management.

关键词： urban landscape dynamics change process temporally variation continuous change detection and classification vegetation greening

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Evaluation of the Initial Thematic Output from a continuous change-detection Algorithm for Use in Automated Operational Land-change Mapping by the US Geological Survey

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REMOTE SENSING 2016年第10期8卷 811-811页

作者： Pengra, Bruce Gallant, Alisa L. Zhu, Zhe Dahal, Devendra SGT Inc 47914 252nd St Sioux Falls SD 57198 USA US Geol Survey Earth Resources Observat & Sci Ctr 47914 252nd St Sioux Falls SD 57198 USA Inuteq Sioux Falls SD 57198 USA Texas Tech Univ Dept Geosci MS 1053Sci Bldg 125 Lubbock TX 79409 USA

The U.S. Geological Survey (USGS) has begun the development of operational, 30-m resolution annual thematic land cover data to meet the needs of a variety of land cover data users. The continuous change detection and classification (CCDC) algorithm is being evaluated as the likely methodology following early trials. Data for training and testing of CCDC thematic maps have been provided by the USGS Land Cover Trends (LC Trends) project, which offers sample-based, manually classified thematic land cover data at 2755 probabilistically located sample blocks across the conterminous United States. These samples represent a high quality, well distributed source of data to train the Random Forest classifier invoked by CCDC. We evaluated the suitability of LC Trends data to train the classifier by assessing the agreement of annual land cover maps output from CCDC with output from the LC Trends project within 14 Landsat path/row locations across the conterminous United States. We used a small subset of circa 2000 data from the LC Trends project to train the classifier, reserving the remaining Trends data from 2000, and incorporating LC Trends data from 1992, to evaluate measures of agreement across time, space, and thematic classes, and to characterize disagreement. Overall agreement ranged from 75% to 98% across the path/rows, and results were largely consistent across time. Land cover types that were well represented in the training data tended to have higher rates of agreement between LC Trends and CCDC outputs. Characteristics of disagreement are being used to improve the use of LC Trends data as a continued source of training information for operational production of annual land cover maps.

关键词： continuous change detection and classification USGS Land Cover Trends training data Landsat high-resolution imagery land cover mapping

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An Object-Based Approach to Map Young Forest and Shrubland Vegetation Based on Multi-Source Remote Sensing Data

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REMOTE SENSING 2022年第5期14卷 1091-1091页

作者： Rittenhouse, Chadwick D. Berlin, Elana H. Mikle, Nathaniel Qiu, Shi Riordan, Dustin Zhu, Zhe Univ Connecticut Dept Nat Resources & Environm Wildlife & Fisheries Conservat Ctr 1376 Storrs RdUnit 4087 Storrs CT 06269 USA Univ Connecticut Dept Nat Resources & Environm 1376 Storrs RdUnit 4087 Storrs CT 06269 USA

Many remote sensing studies have individually addressed afforestation, forest disturbance and forest regeneration, and considered land use history. However, no single study has simultaneously addressed all of these components that collectively constitute successional stages and pathways of young forest and shrubland at large spatial extents. Our goal was to develop a multi-source, object-based approach that utilized the strengths of Landsat (large spatial extent with good temporal coverage), LiDAR (vegetation height and vertical structure), and aerial imagery (high resolution) to map young forest and shrubland vegetation in a temperate forest. Further, we defined young forest and shrubland vegetation types in terms of vegetation height and structure, to better distinguish them in remote sensing for ecological studies. The multi-source, object-based approach provided an area-adjusted estimate of 42,945 ha of young forest and shrubland vegetation in Connecticut with overall map accuracy of 88.2% (95% CI 2.3%), of which 20,953 ha occurred in complexes >= 2 ha in size. Young forest and shrubland vegetation constituted 3.3% of Connecticut's total land cover and 6.3% of forest cover as of 2018. Although the 2018 estimates are consistent with those of the past 20 years, concerted efforts are needed to restore, maintain, or manage young forest and shrubland vegetation in Connecticut.

关键词： afforestation continuous change detection and classification forest disturbance forest succession Landsat time series LiDAR

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Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul

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LANDSCAPE AND URBAN PLANNING 2022年 227卷

作者： Hwang, Yorum Ryu, Youngryel Qu, Sai Seoul Natl Univ Interdisciplinary Program Landscape Architecture Seoul South Korea Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Seoul South Korea Seoul Natl Univ Res Inst Agr & Life Sci Seoul South Korea Wuhan Univ Sch Resources & Environm Sci Wuhan Peoples R China

Many studies have reported that urbanization leads to a decrease in the normalized difference vegetation index (NDVI) due to the expansion of impervious cover. Some studies, however, have reported positive NDVI trends in urban areas due to warming and CO2 fertilization effects, as well as the creation of green space. Thus, we examined spatial and temporal variations of the growing season maximum NDVI in a megacity, Seoul, which has rapidly urbanized over the past decades, by analyzing a 32-year time series (1987 - 2018) of Landsat satellite images in Google Earth Engine. continuous change detection and classification and random forest algorithms were integrated to classify Seoul land cover types annually. We found an overall increasing NDVI trend at the city scale (0.002 yr(-1)). Significant NDVI trends were found for approximately 46 % of Seoul, with greening and browning trends accounting for 39 % and 7 %, respectively. Greening pixels appeared mainly on impervious (23 % with a significant NDVI trend), deciduous (10 %), and evergreen (3 %) land cover as of 2018. Stable impervious, deciduous, and evergreen land cover pixels showed a greening trend over the 32 years (0.002 yr(-1)), which stemmed from the planting of trees in areas with impervious cover, such as streets and residential areas, and vegetation growth in forest areas. Disturbed area pixels showed fluctuating NDVI values, but there were more greening pixels (20 %) than browning pixels (5 %). Our findings indicate that a detailed knowledge of land use and land cover changes is required to understand NDVI trends in urban areas.

关键词： Urban Land cover change continuous change detection and classification Normalized difference vegetation index Landsat Google Earth Engine

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