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Predicting Ailanthus altissima presence across a managed forest landscape in southeast Ohio

Forest Ecosystems 2019年第4期6卷 261-273页

作者： Louis R.Iverson Joanne Rebbeck Matthew P.Peters Todd Hutchinson Timothy Fox Northern Research Station USDA Forest Service

Background: The negative impacts of the exotic tree, Ailanthus altissima(tree-of-heaven, stink tree), is spreading throughout much of the Eastern United States. When forests are disturbed, it can invade and expand quickly if seed sources are ***: We conducted studies at the highly dissected Tar Hollow State Forest(THSF) in southeastern Ohio USA,where Ailanthus is widely distributed within the forest, harvests have been ongoing for decades, and prescribed fire had been applied to about a quarter of the study area. Our intention was to develop models to evaluate the relationship of Ailanthus presence to prescribed fire, harvesting activity, and other landscape characteristics, using this Ohio location as a case study. Field assessments of the demography of Ailanthus and other stand attributes(e.g., fire, harvesting, stand structure) were conducted on 267 sample plots on a 400-m grid throughout THSF,supplemented by identification of Ailanthus seed-sources via digital aerial sketch mapping during the dormant season. Statistical modeling tools Random Forest(RF), Classification and Regression Trees(CART), and Maxent were used to assess relationships among attributes, then model habitats suitable for Ailanthus ***: In all, 41 variables were considered in the models, including variables related to management activities, soil characteristics, topography, and vegetation structure(derived from LiDAR). The most important predictor of Ailanthus presence was some measure of recent timber harvest, either mapped harvest history(CART) or LiDARderived canopy height(Maxent). Importantly, neither prescribed fire or soil variables appeared as important predictors of Ailanthus presence or absence in any of the models of the ***: These modeling techniques provide tools and methodologies for assessing landscapes for Ailanthus invasion, as well as those areas with higher potentials for invasion should seed sources become available. Though a case study on

关键词： Ohio Random Forest CART Maxent landscape model Non-native invasive species

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Vegetation monitoring using different scale of remote sensing data

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Journal of Environmental Sciences 1999年第2期11卷 89-93页

作者： Junko Kunitomo, Yukihiro Morimoto Department of Regional Environmental Science, Osaka Prefecture University, 1 1 Gakuen cho Sakai, Osaka 599 8531, Japan

This work sets out to simulate landscape model of Mu Us Desert in Inner Mongolia Autonomous Region of China at different spatial resolution using remote sensing images and distinguished landscape heterogeneity among different spatial resolutions. landscape models were created from classification image of SPOT satellite data with 20m resolution and NOAA data with 1 km resolution. This study created landscape models of different scales by resampling the SPOT classified image using majority rule. The pixel resolution was increased from the finest scale of 20m by 20m up to 1000m by 1000m that was the coarsest spatial resolution. The Shannon diversity index was used to compare landscape models between different scales. At the finer scale the verify small patches such as deciduous forest, shrub and reedswamp with high vegetation coverage set on matrices with low vegetation cover (moving sand dune and sparse grassland) were verified. Broadening of scale resulted to the loss of small patches and at 1000m resolution, matrix classes were dominant. At 1km resolution of NOAA data, the matrix classes which greatly related to the topography of Mu Us Desert were detected. Diversity index decreased during scale broadening and the difference between SPOT 1km scale model and AVHRR data was not significant. The results showed that SPOT 20m model is good for the use of ecotone oriented revegetation planning, and NOAA 1km model is good for the seasonal and annual monitoring of each landscape unit, and revegetation planning at the regional level.

关键词： vegetation monitoring landscape model remote sensing Mu Us Desert.

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模拟中国四级行政尺度下耕地位移对粮食生产潜力的影响

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Journal of Geographical Sciences 2023年第1期33卷 18-36页

作者：杨柏寒盛思雨柯新利代先华卢新海 College of Public Administration Central China Normal UniversityWuhan 430079China College of Public Administration Huazhong Agricultural UniversityWuhan 430070China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China

Cropland displacement, as an important characteristic of cropland change, places more emphasis on changes in spatial location than on quantity. The effects of cropland displacement on global and regional food production are of general concern in the context of urban expansion. Few studies have explored scale-effects, however, where cropland is displaced not only within, but also outside, the administrative boundary of a certain region. This study used a spatially explicit model(landscape) to simulate the potential cropland displacement caused by urban land expansion from 2020 to 2040 at four scales of the Chinese administration system(national, provincial, municipal, and county levels). The corresponding changes in potential cereal production were then assessed by combining cereal productivity data. The results show that 4700 km^(2) of cropland will be occupied by urban expansion by 2040, and the same amount of cropland will be supplemented by forest, grassland, wetland, and unused land. The potential loss of cropland will result in the loss of 3.838×10^(6) tons of cereal production, and the additional cropland will bring 3.546×10^(6) tons, 3.831×10^(6) tons, 3.836×10^(6) tons, and 3.528×10^(6) tons of potential cereal production in SN(national scale), SP(provincial scale), SM(municipal scale), and SC(county scale), respectively. Both SN and SC are observed to make a huge difference in cereal productivity between the lost and the supplemented cropland. We suggest that China should focus on the spatial allocation of cropland during large-scale displacement, especially at the national level.

关键词： cropland displacement potential cereal production landscape model scale-effects food security

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The ideal landscape and its ecological planning approach in Mount Lushan National Park, China

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Journal of Forestry Research 2009年第A3期20卷 279-284页

作者： XU Cong-rong HU Hai-sheng WU Zhang-wen ZHENG Yan-ping ZHONG Lin-sheng Center for Forest Recreation Research of Central South University of Forestry and Technology Changsha 410004 China. Tourism College Jiangxi University of Finance ＆ Economic Nanchang 330013 China. Art and Communication College Jiangxi University of Finance ＆ Economic Nanchang 330013 China. Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China

This paper described the main four basic models of ideal landscape in the Mount Lushan National Park as Kunlunshan model, Xumishan model, Peach flower land model, Christian model. Five typical places in Mount Lushan with the ideal landscape characteristics like the East Grove Temple, White Deer Cave Academy, Simplicity and Tranquility Monastery, Lushan Valley, and Villa-cluster of the East Valley were analyzed, and the integration of the basic models for each place was discussed to make the realistic landscape conform to the ideal needs. Moreover, the discussions also involved the characteristics of ideal landscape that are featured in resource, culture, structure, function, process, scale and region. In conclusion, ideal landscape in the Mount Lushan National Park was the product of the interaction between human and nature. Mount Lushan inherits the essence of Chinese traditional culture with a history of more than 1000 years; and it also absorbs the manna of the Western culture as well as the exchanges between nature and humanities together with the conflict and adaptation among different cultures. The natural landscape could be satisfied with the survival demand and cultural taste of mankind by the way of ecological planning.

关键词： ecological planning ideal landscape landscape model Mount Lushan National Park

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