利用三维激光扫描技术进行涉案伐倒木材积测定     

张民侠  张慧娟  高瑞金  王瑞 《测绘通报》2019,(4):152-154

利用三维激光扫描系统,获得伐倒木的整体点云数据,利用点云应用微积填充的方式计算出伐倒木的材积,将计算结果与根据伐倒木求积式计算出的材积结果进行比较。结果表明,激光扫描仪测得的伐倒木材积数据精度、测量效率均比较高,可以满足林业调查精度的要求,为快速、高效地测定涉案伐倒木材积提供了技术支持。  相似文献   

Integrating TM and ancillary geographical data with classification trees for land cover classification of marsh area   总被引：5，自引：1，他引：4  

Xiaodong Na  Shuqing Zhang  Huaiqing Zhang  Xiaofeng Li  Huan Yu  Chunyue Liu 《中国地理科学(英文版)》2009,19(2):177-185

The main objective of this research is to determine the capacity of land cover classification combining spectral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM image texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS information (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to implement and should be applicable in other settings and over larger extents.  相似文献   

Spatial autocorrelation of West Nile virus vector mosquito abundance in a seasonally wet suburban environment     

P. R. Trawinski  D. S. Mackay 《Journal of Geographical Systems》2009,11(1):67-87

The objective of this study is to quantify and model spatial dependence in mosquito vector populations and develop predictions for unsampled locations using geostatistics. Mosquito control program trap sites are often located too far apart to detect spatial dependence but the results show that integration of spatial data over time for Cx. pipiens-restuans and according to meteorological conditions for Ae. vexans enables spatial analysis of sparse sample data. This study shows that mosquito abundance is spatially correlated and that spatial dependence differs between Cx. pipiens-restuans and Ae. vexans mosquitoes.   相似文献   

重新审视珠江三角洲海面升降问题   总被引：1，自引：1，他引：0  

李平日 《热带地理》2011,31(1):34-38,51

针对近年学界和传媒对全球气候变化和海面升降的关注与争议,特别是对珠江三角洲海平面上升的预估,认为必须严肃对待,重新审视.通过分析广东地下埋藏古树蕴含的古气候信息,认为存在400年左右的冷暖周期;现代进入暖周期,加上工业化温室气体增加而引起全球升温.根据珠江三角洲典型潮位站长期和新近19年的观测记录,对珠江三角洲未来数十...  相似文献   

Comparison of data-driven modelling techniques for river flow forecasting     

Shreenivas Londhe  Shrikant Charhate 《水文科学杂志》2013,58(7):1163-1174

Abstract

Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alterative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events.

Citation Londhe, S. & Charhate, S. (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol. Sci. J. 55(7), 1163–1174.  相似文献   

Linking satellite-based spring phenology to temperate deciduous broadleaf forest photosynthesis activity     

《International Journal of Digital Earth》2013,6(11):881-896

Satellite-based remote sensed phenology has been widely used to assess global climate change. However, it is constrained by uncertain linkages with photosynthesis activity. Two dynamic threshold methods were employed to retrieve spring phenology metrics from four Moderate Resolution Imaging Spectroradiometer (MODIS) products, including fraction of Absorbed Photosynthetically Active Radiation (fAPAR), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) for three temperate deciduous broadleaf forests in North America between 2001 and 2009. These MODIS-based spring phenology metrics were subsequently linked to the photosynthetic curves (daily gross primary productivity, GPP) measured by an eddy covariance flux tower. The 20% dynamic threshold spring onset metrics from MODIS products were closer to the photosynthesis onset metrics at the date of 2% GPP increase for NDVI and fAPAR, and closer to the date of 5% and 10% increase of GPP for EVI and LAI, respectively. The 50% dynamic threshold onset metrics were closer to the photosynthesis onset metrics at the date of 10% GPP increase for NDVI, and closer to the date of 20% GPP increase for fAPAR, LAI and EVI, respectively. These results can improve our knowledge on the photosynthesis activity status of remotely sensed spring phenology metrics.  相似文献   

Forestry between land use intensification and sustainable development: Improving landscape functions with forests and trees     

Jørgen Bo Larsen 《Geografisk tidskrift / udgivet af Bestyrelsen for Det Kongelige danske geografiske selskab》2013,113(2):191-195

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Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure     

《International Journal of Applied Earth Observation and Geoinformation》2017

Mangrove forests grow in intertidal zones in tropical and subtropical regions and have suffered a dramatic decline globally over the past few decades. Remote sensing data, collected at various spatial resolutions, provide an effective way to map the spatial distribution of mangrove forests over time. However, the spectral signatures of mangrove forests are significantly affected by tide levels. Therefore, mangrove forests may not be accurately mapped with remote sensing data collected during a single-tidal event, especially if not acquired at low tide. This research reports how a decision-tree −based procedure was developed to map mangrove forests using multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM). Three indices, including the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI) and NDVI_L·NDMI_H (the multiplication of NDVI_L by NDMI_H, L: low tide level, H: high tide level) were used in this algorithm to differentiate mangrove forests from other land-cover and land-use types in Fangchenggang City, China. Additionally, the recent Landsat 8 OLI (Operational Land Imager) data were selected to validate the results and compare if the methodology is reliable. The results demonstrate that short-term multi-tidal remotely-sensed data better represent the unique nearshore coastal wetland habitats of mangrove forests than single-tidal data. Furthermore, multi-tidal remotely-sensed data has led to improved accuracies using two classification approaches: i.e. decision trees and the maximum likelihood classification (MLC). Since mangrove forests are typically found at low elevations, the inclusion of elevation data in the two classification procedures was tested. Given the decision-tree method does not assume strict data distribution parameters, it was able to optimize the application of multi-tidal and elevation data, resulting in higher classification accuracies of mangrove forests. When using multi-source data of differing types and distributions to map mangrove forests, a decision-tree method appears to be superior to traditional statistical classifiers.  相似文献   

Landmark‐based summarized messages for flood warning          下载免费PDF全文

Ya‐Hui Chang  Yu‐Te Liu  Yi‐Yuan Tan 《Transactions in GIS》2017,21(5):847-861

In order to evacuate residents in time, flood warning systems must have rapid data processing algorithms to translate detailed numerical data into simple warnings and navigation aids. Although it is common to display warning messages by directly drawing red circles on a map embedded in a Web page to indicate the regions about to be inundated, such an approach has some drawbacks. We propose an alternative way by summarizing the warning messages based on landmarks, so that the messages can be short and convey even more information. We have designed two approaches to output such messages. They are the nearest landmark approach and the threshold approach, which differ in the way of determining which landmarks will be influenced by a certain flooded region. These two methods are implemented and we compare their performance through real and synthetic datasets. Experimental results show that the threshold approach usually takes less execution time than the nearest landmark approach. Its severity ranking of landmarks is also better suited to human behaviour.  相似文献   

Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants     

Solomon G. Tesfamichael  Solomon W. Newete  Elhadi Adam  Bambo Dubula 《地理信息系统科学与遥感》2018,55(3):417-436

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.  相似文献