In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.  相似文献   

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India     

Abhishek Danodia  N. R. Patel  Choe Won Chol  B. R. Nikam  V. K. Sehgal 《国际地球制图》2019,34(1):114-131

Estimation and monitoring of crop evapotranspiration (ET_c) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ET_c by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ET_c was estimated 2.72 and 2.47 in mm day^?1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ET_c.  相似文献   

金沙江白格滑坡残留体稳定性分析与防治对策     

朱德明  李鹏岳  胡孝洪  吴新明 《现代地质》2021,35(1):56-63

金沙江白格滑坡在经历了2018年10月和11月的2次滑动堵江后,在其后缘仍存在K1、K2和K3等3处规模较大的残留崩滑体,并有再次失稳堵江的可能。目前关于残留体稳定性的研究还存在较大争议,多从影响因素及成因机制等方面进行定性分析,其结果依赖于评价者的经验,差异较大。在野外调查的基础上,针对白格滑坡所在区工程地质条件及其残留体变形特征,选取坡高、坡度、坡面形态、临空面、地下水出露、裂缝发育和变形等7个因素作为评价因子,基于模糊综合评判模型,运用层次分析法对白格滑坡残留体进行分区稳定性评价,并提出相应的防治对策。评价结果表明:K1与K2残留体稳定性较差;K3残留体基本稳定,与实际调查状况较吻合。研究结果可为同类型滑坡的评价与防治提供参考。  相似文献   

京津冀周边秸秆燃烧对PM_2.5无机组分影响          下载免费PDF全文

张方健  徐敬  马建中  寇星霞 《应用气象学报》2019,30(4):467-478

华北平原是我国主要农作物产区,田间秸秆焚烧现象普遍存在,选取秋收季节（2014年10月）分析了秸秆燃烧的排放特征,利用区域化学传输模型WRF-Chem模拟研究了燃烧排放对气态前体物及其氧化产物的影响,以及最终导致的PM_2.5中硫酸盐、硝酸盐和铵盐的变化。研究表明:2014年秋收季节,河南和山东等省份的秸秆燃烧排放会在东南风的输送作用下影响京津冀地区;秸秆燃烧排放大量挥发性有机物（VOCs）,导致火点源及周边地区大气中主要氧化剂浓度上升,提升了区域大气氧化能力;当携带大量VOCs的秸秆燃烧烟羽与以化石燃料排放为主的城市气团相混合时,大气氧化性增强会加速城市地区人为源排放的NO_x和SO₂等气态前体物的氧化过程,提高硫酸盐和硝酸盐的形成速率、促进二次无机气溶胶的生成。  相似文献   

淮河流域秸秆焚烧关键期主要大气污染物浓度时空分布特征   总被引：2，自引：0，他引：2  

吴文玉  张浩  何彬方  霍彦峰  张宏群  翟菁 《气象与环境学报》2019,35(4):33-39

基于2015年6月淮河流域卫星遥感监测火点信息、环境空气质量监测数据和常规气象观测资料,利用ANUSPLIN和ArcGISKriging方法对气象要素和主要大气污染物浓度空间栅格化,分析了秸秆焚烧关键期内AQI和主要污染物浓度的时空变化特征及其与气温、相对湿度、风速等气象要素的相关关系。结果表明:秸秆焚烧关键期内,淮河流域城市AQI、PM10与PM2.5浓度均明显升高,且与卫星监测火点具有一定时空响应关系。在时间变化上,AQI、PM10与PM2.5浓度6月上中旬呈波动上升,6月下旬趋于回落;在空间分布方面,AQI、PM10与PM2.5浓度三者分布形态相似,总体上呈现"南低北高、两高一低"分布特征;期间AQI、PM10与PM2.5浓度与气温呈显著正相关,与相对湿度呈显著负相关,与风速的相关性不显著。  相似文献   

2015年11月沈阳地区一次PM2.5重污染过程综合分析     

杨磊  陈传雷  曹世腾  孙丽  崔曜鹏  蒋超  黄海亮  陈宇  杨雪 《气象与环境学报》2019,35(3):37-44

利用多源观测资料综合分析了2015年11月沈阳地区一次PM2.5 重污染天气的气象条件、垂直风场演变、大气边界层特征以及污染物的来源。结果表明:本次重污染过程中,沈阳市区PM2.5浓度长达81h超过250μg · m^-3 ,其中峰值浓度达到1287μg · m^-3 ,重污染期间PM2.5 /PM10 的比例最高为90%。受地面倒槽和黄淮气旋影响,近地面层持续存在的逆温层、高相对湿度和弱偏北风为颗粒物吸湿增长和长时间聚集提供有利的天气条件。风廓线雷达风场资料显示在重污染期间,近地面层存在弱风速区、凌乱风场和弱下沉气流。利用风廓线雷达资料计算了边界层通风量(Ventilation Index,VI)和局地环流指数(Recirculation,R),边界层通风量VI和PM2.5 存在明显的负相关,非污染日VI是重污染日的2倍,局地环流指数R在重污染天气前大于0.9,而在污染期间部分空间R小于0.8。通过后向轨迹模式和火点监测资料分析发现,沈阳上空300m高度气团来自于生物质燃烧区域,而且沈阳地区NO2和CO浓度的变化与PM2.5一致,说明本次重污染过程也可能和生物质燃烧有关。  相似文献   

北京延庆葡萄产地高氟地质环境特征研究     

冯辉 《城市地质》2015,(2):27-30

北京延庆葡萄产地北侧山区出露大面积花岗岩。岩浆活动频繁,构造发育,特殊地质条件造成土壤存在明显的高氟异常,全氟、水溶氟含量高,部分地下水氟化物含量超过相关标准,是导致当地居民因饮用地下水而患有氟中毒地方病的主要原因。尽管农作物含氟符合相关标准,但高氟地质环境对农作物的含氟量仍具有富集趋势。  相似文献   

Efficient collection of training data for sub-pixel land cover classification using neural networks     

Stien Heremans  Bert Bossyns  Herman Eerens  Jos Van Orshoven 《International Journal of Applied Earth Observation and Geoinformation》2011

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.  相似文献   

生物质燃烧颗粒物中有机质及源解析研究进展     

刘刚袁静  陈敏东姜玲 《南京气象学院学报》2011,(2):138-141

对近年来有关生物质燃烧排放的颗粒物中有机化合物和有机示踪物的研究进展进行了综述,分析了各国学者根据有机示踪物研究城市大气颗粒物中生物质燃烧和其他排放源对空气污染的贡献,对以后的相关研究具有借鉴意义．  相似文献   

Early-season mapping of crops and cultural operations using very high spatial resolution Pléiades images     

《International Journal of Applied Earth Observation and Geoinformation》2015

The aim of this study was to assess the contribution of very high spatial resolution (VHSR) Pléiades images to both early season crop identification and the mapping of bare soil surface characteristics due to cultural operations. The study region covering 21 km² is located west of the peri-urban territory of the Versailles plain and the Alluets plateau (Yvelines, France). About 100 cropped fields were observed on the ground synchronously with two Pléiades images of 3 and 24 April 2013 and one SPOT4 image of 2 April 2013. The GIS structuring of these field data along with vector information about field boundaries was used for delimitating both training and test zones for the support vector machine classifier with polynomial function kernel (pSVM). The pSVM was computed on the spectral bands and NDVI for both single-date Pléiades and the bi-temporal Pléiades pair. For the single-date classifications of crops, the overall per-pixel accuracy reached 87% for the SPOT4 image of 2 April (6 classes), 79% for the Pléiades image of 3 April (6 classes) and 82% for that of 24 April (7 classes). At the earlier date (2–3 April), the Pléiades image very well discriminated cultural operations (>77%, user’s or producer’s accuracies) as well as fallows and grasslands, while winter cereals and rapeseed were better discriminated by the SPOT4 image winter cereals (>70%, user’s or producer’s accuracies). As Pléiades images revealed within-field spatial variations of early phenological stages of winter cereals that could be critical for adjusting management of zones with delayed development during the growing season, they brought information complementary to multispectral images with high spatial resolution. For the bi-temporal Pléiades image, the overall per-pixel accuracy was about 80% (7 classes), winter crops, grasslands and fallows being very well detected while confusions occurred between spring barley at initial stages (2–3 leaves) and bare soils prepared for other spring crops. Using an additional validation field set covering ∼1/3 of the study area croplands, the crop map resulting from the bi-temporal Pléiades pair achieved correct crop prediction for about 89.7% of the validation fields when considering composite classes for winter cereals and for spring crops. Early-season Pléiades images therefore show a considerable potential for anticipating regional crop patterns and detecting soil tillage operations in spring.  相似文献