A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and...A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.展开更多
This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive ...This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.展开更多
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/dist...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·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) 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.展开更多
文摘A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.
文摘This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.
基金supported by the Natural Science Foundation of Zhejiang Province(No.ZCLQN25C0301)the National Key Research and Development Program of China(No.2016YFC0502700)the General Program of Education Department of Zhejiang(No.23056209-F).
文摘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·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) 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.
