共查询到20条相似文献，搜索用时 397 毫秒
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简要介绍了AutoCAD和MapGIS的基本特点,详细分析了两者数据特点和数据转换过程中要注意的问题,重点阐述了AutoCAD的DXF和DWG两种数据格式与MapGIS的MPJ数据格式之间转换的实现过程与方法,最后总结了对AutoCAD数据和其他GIS软件间的数据转换的参考意义,从而达到数据资源的共享。 相似文献
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针对InSAR在数据处理过程中存在对流层延迟误差、解缠误差及处理大范围区域数据需要消耗大量时间和磁盘空间的问题，本文首先利用LiCSBAS和GACOS产品对2016年9月16日至2021年5月5日昆明市134景Sentinel-1升降轨影像进行数据处理，获取昆明市主城区沉降信息，在此基础上得到5个典型地表沉降区并分析其时空分布特征；然后利用深度森林和长短期记忆网络模型进行时序值的预测，引入绝对误差(ε)、均方根误差(RMSE)、纳什系数(NSE)对模型进行评价，深度森林和长短期记忆模型得到的ε均在4 mm以内，RMSE值分别为0.70和3.01,NSE值分别为0.92和0.81。结果表明，深度森林预测模型效果较好，联合LiCSBAS和机器学习模型的城市地表监测和预测的方法可以为今后开展地面沉降监测和灾害预警提供参考。 相似文献
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Sushma Panigrahy S. S. Ray Anil Sood L. B. Patel P. K. Sharma J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2004,32(2):209-216
A study was conducted in the Bathinda district of Punjab state for mapping the cropping pattern and crop rotation, monitoring
long term changes in cropping pattern by using the satellite based remote sensing data along other spatial and non-spatial
collateral data. Multi-date IRS LISS I and IRS WiFS sensor data have been used for this study. Cropping pattern maps and crop
rotation maps were generated for the years 1988-89 and 1998-99. The present study has shown the increase of cropping intensity
significantly, mainly due to increase in rice area. However, crop diversity has decreased mainly due to decline in the area
under the minor crops like pearl millet, gram, rapeseed/ mustard. There is increase in area coverage of cotton-wheat and rice-wheat
rotation, at the expense of the minor crops. 相似文献
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通过软硬变化检测识别冬小麦 总被引：1，自引：0，他引：1
提出一种软硬变化检测的作物识别方法 SHLUCD(Soft and Hard Land Use/Cover Change Detection Method)。该方法利用多期遥感影像能够有效表达作物的生长物候特征,以达到在离散变化区(即纯净像元区,包括完全转换成作物的突变区域和非作物区域)和连续变化区(即渐变区,混合像元区,是部分转化为作物的区域)准确进行作物的识别。在北京市选择一个研究区,以冬小麦为研究对象,选用2011年10月6日(播种期)和2012年4月16日(拔节期)两期环境减灾1号卫星影像,分别采用硬变化检测方法 HLUCD(Hard Land Use/Cover Change Detection Method)、软变化检测方法 SLUCD(Soft Land Use/Cover Change Detection Method)和SHLUCD进行冬小麦的识别。实验结果表明:在不同尺度窗口下,SHLUCD较传统方法表现出较明显的优势,具有更低的均方根误差RMSE(SHLUCD为[0.14,0.07],HLUCD为[0.15,0.07],SLUCD为[0.16,0.08])和偏差bias(SHLUCD为-0.0008,HLUCD为-0.007,SLUCD为0.014)和更高的决定系数R2(SHLUCD为[0.68,0.86],HLUCD为[0.62,0.86],SLUCD为[0.60,0.86])。针对冬小麦突变区域、冬小麦渐变区域和非冬小麦区域分别进行评价,表明SHLUCD识别精度接近各区最佳的识别方法,进一步验证了SHLUCD的灵活性和适用性。SHLUCD方法在离散变化区能够通过土地覆盖类型状态变化来有效地识别出冬小麦,在连续变化区可识别出土地覆盖的状态变化程度定量表达冬小麦的丰度,是其他作物多时相遥感变化检测的前期实验基础。 相似文献
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及时准确地获取耕地空间分布数据对于农业生产管理、产量估算、种植结构调整等具有重要意义。目前的耕地提取多基于多时相中低分辨率影像或单时相高分辨率影像,难以满足耕地破碎,农作物种植模式复杂的区域精度需求。基于此,本研究通过协同国产高分一号（GF-1）、高分二号（GF-2）和高分六号（GF-6）卫星影像,探索米级分辨率尺度下的耕地高精度提取方法。该方法以深度神经网络UNet为基础,通过协同GF-1/6的多时相优势和GF-2影像的高空间分辨率构建了CEUNet（Cropland Extraction UNet）模型,以充分挖掘耕地的时相特征和空间几何特征。同时,将基于CEUNet模型提取的米级耕地结果分别与基于UNet和多源不同分辨率遥感影像的语义分割（UNet_m）、基于UNet和单时相高分辨率影像的语义分割（UNet_s）、基于对象的随机森林分类（OBIA）、基于像元的随机森林分类（RF）提取的耕地结果展开对比,分析所提出的方法在不同区域的适宜性。结果表明,基于CEUNet模型提取的米级耕地总体精度达到92.92%,且基于CEUNet提取的耕地的逐像元验证结果在平均F1-Score值上相较于基于对象和基于像元的随机森林分类分别提升了0.21和0.21,相较于UNet_m和UNet_s分别提升了0.04和0.11,其中针对地块破碎,景观异质性高等区域,CEUNet相较于UNet_m和UNet_s提升了0.09和0.26。本研究提出的CEUNet模型能够充分发挥多源国产高分卫星数据的空间和时间优势,两者结合能够快速、高效地提取不同农业景观及不同种植模式的耕地空间分布信息。 相似文献
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Sushma Panigrahy Shibendu Shankar Ray K. R. Manjunath P. S. Pandey S. K. Sharma Anil Sood Manoj Yadav P. C. Gupta N. Kundu Jai Singh Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):355-364
Cropping system study is not only useful to understand the overall sustainability of agricultural system, but also it helps
in generating many important parameters which are useful in climate change impact assessment. Considering its importance,
Space Applications Centre, took up a project for mapping and characterizing major cropping systems of Indo-Gangetic Plains
of India. The study area included the five states of Indo-Gangetic Plains (IGP) of India, i.e. Punjab, Haryana, Uttar Pradesh,
Bihar and West Bengal. There were two aspects of the study. The first aspect included state and district level cropping system
mapping using multi-date remote sensing (IRS-AWiFS and Radarsat ScanSAR) data. The second part was to characterize the cropping
system using moderate spatial resolution multi-date remote sensing data (SPOT VGT NDVI) and ground survey. The remote sensing
data was used to compute three cropping system performance indices (Multiple Cropping Index, Area Diversity Index and Cultivated
Land Utilization Index). Ground survey was conducted using questionnaires filled up by 1,000 farmers selected from 103 villages
based on the cropping systems map. Apart from ground survey, soil and water sampling and quality analysis were carried out
to understand the effect of different cropping systems and their management practices. The results showed that, rice-wheat
was the major cropping system of the IGP, followed by Rice-Fallow-Fallow and Maize-Wheat. Other major cropping systems of
IGP included Sugarcane based, Pearl millet-Wheat, Rice-Fallow-Rice, Cotton-Wheat. The ground survey could identify 77 cropping
systems, out of which 38 are rice-based systems. Out of these 77 cropping systems, there were 5 single crop systems, occupying
6.5% coverage (of all cropping system area), 56 double crop systems with 72.7% coverage, and 16 triple crop systems with 20.8%
coverage. The cropping system performance analysis showed that the crop diversity was found to be highest in Haryana, while
the cropping intensity was highest in Punjab state. 相似文献
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Land cover classification and economic assessment of citrus groves using remote sensing 总被引：1，自引：0，他引：1
《ISPRS Journal of Photogrammetry and Remote Sensing》2007,61(5):341-353
The citrus industry has the second largest impact on Florida's economy, following tourism. Estimation of citrus area coverage and annual forecasts of Florida's citrus production are currently dependent on labor-intensive interpretation of aerial photographs. Remotely sensed data from satellites has been widely applied in agricultural yield estimation and cropland management. Satellite data can potentially be obtained throughout the year, making it especially suitable for the detection of land cover change in agriculture and horticulture, plant health status, soil and moisture conditions, and effects of crop management practices. In this study, we analyzed land cover of citrus crops in Florida using Landsat Enhanced Thematic Mapper Plus (ETM+) imagery from the University of Maryland Global Land Cover Facility (GLCF). We hypothesized that an interdisciplinary approach combining citrus production (economic) data with citrus land cover area per county would yield a correlation between observable spectral reflectance throughout the year, and the fiscal impact of citrus on local economies. While the data from official sources based on aerial photography were positively correlated, there were serious discrepancies between agriculture census data and satellite-derived cropland area using medium-resolution satellite imagery. If these discrepancies can be resolved by using imagery of higher spatial resolution, a stronger correlation would be observed for citrus production based on satellite data. This would allow us to predict the economic impact of citrus from satellite-derived spectral data analysis to determine final crop harvests. 相似文献
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Kuntal Ganguly Rajiv Kumar K. Mruthyunjaya Reddy P. Jagadeeswara Rao Manoj Raj Saxena G. Ravi Shankar 《国际地球制图》2017,32(7):777-796
This study investigated land use/land cover change (LULCC) dynamics using temporal satellite images and spatial statistical cluster analysis approaches in order to identify potential LULCC hot spots in the Pune region. LULCC hot spot classes defined as new, progressive and non-progressive were derived from Gi* scores. Results indicate that progressive hot spots have experienced high growth in terms of urban built-up areas (20.67% in 1972–1992 and 19.44% in 1992–2012), industrial areas (0.73% in 1972–1992 and 3.46% in 1992–2012) and fallow lands (4.35% in 1972–1992 and ?6.38% in 1992–2012). It was also noticed that about 28.26% of areas near the city were identified as new hot spots after 1992. Hence, non-significant change areas were identified as non-progressive after 1992. The study demonstrated that LULCC hot spot mapping through the integrated spatial statistical approach was an effective approach for analysing the direction, rate, spatial pattern and spatial relationship of LULCC. 相似文献
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Increasing population and natural disasters like drought, flood, cyclone etc., has impacted global agriculture area and hence continuously modifying cropping pattern and associated statistics. The present study analysed agriculture dynamics over one of the densely populated and disaster prone state (Bihar) in India and derived vital statistics (single, double and triple cropping area, and monthly, seasonal, annual and long term status at the state and district level) for the years 2001–2012. The study used time-series MODIS vegetation index (EVI; MOD13A2, 1 km, 16 day, 2001–2012), MODIS annual Land Cover product (MCD12Q1, 500 m, 2001–2012) and Global Land Cover map (Scasia_V4, 1 km, 2000; Globcover_V2.2, 300 m, 2005/2006 and V2.3, 2009, 300 m), and extracted horizontal (i.e., area change) and vertical (i.e., cropping intensification) agriculture change pattern. The results were inter-compared, and validated using government reports as well as with high spatial resolution data (IRS-LISS III 23.5 m). From 2001–2006 to 2007–2012, the net horizontal and vertical change in agriculture area is +145.24 and +907.82 km2, respectively, and net change in seasonal crop area (winter, summer and monsoon) is +959.21, +1009.84 and ?1061.64 km2, respectively. The districts which are located along the eastern part of Ganges experienced maximum positive changes and the districts along Gandak river in the north-western part of the study area experienced maximum negative changes. Overall, the study has quantified and revealed interesting space–time agriculture change patterns over 12 years including impacts caused by droughts and floods in the study area. 相似文献
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Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery 总被引：1，自引：0，他引：1
Xiaochun Zhang Qinxue Xiong Liping Di Junmei Tang Jin Yang Huayi Wu 《International Journal of Digital Earth》2018,11(12):1219-1240
Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas. 相似文献
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Carlos Antonio da Silva Junior Marcos Rafael Nanni José Francisco de Oliveira-Júnior Everson Cezar Paulo Eduardo Teodoro Rafael Coll Delgado 《International Journal of Digital Earth》2019,12(3):270-292
This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. Two major techniques were used: GEOgraphic-Object-Based Image Analysis (GEOBIA) and Data Mining (DM). In order to obtain the images, the segmentation algorithm implemented by Definiens Developer was used. A decision tree (DT) was created from a training set previously prepared. Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle. The time-series data were used only for the CEI index. Furthermore, to compare the results obtained from GEOBIA, the slicing technique was used at the CEI level. After the training, the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean. In accordance with the error matrix and kappa parameter analysis, tests for statistical significance were created. Results indicate that the classification achieved by Kappa coefficients is 0.76. In short, the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale. 相似文献