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1.
利用遥感数据评价燃煤电厂空气质量   总被引：1，自引：0，他引：1  
卫星观测数据可以评价燃煤电厂的空气质量等级。NO2、SO2 和烟尘是燃煤电厂排放的主要污染物,本文利用卫星遥感观测的NO2、SO2和气溶胶光学厚度AOD(Aerosol Optical Depth)开展燃煤电厂空气质量评价。以中国华北地区为实验区,分析对比了3种污染物不同时间分辨率和空间分辨率的污染状况,确定了单因子的5级分级标准,根据燃煤电厂排放污染物的权重不同,提出了评价近地表空气质量状况的模型。本文综合考虑3种污染因子来反映电厂空气质量,有利于提高评价的准确性以及反应信息的全面性。结果表明,该模型能正确反映不同地区电厂的空气质量特点。  相似文献   

2.
Nitrogen dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was oxides of nitrogen (NOx) derived from dispersion modelling outputs, at 1 km × 1 km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.  相似文献   

3.
ABSTRACT

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NOx during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO2, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O3 and NOx) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while tra?c emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.  相似文献   

4.
The spread of invasive Australia native Acacia tree species threatens biodiversity and adversely affecting on vegetative structure and function, including plant community composition, quantity and quality worldwide. It is essential to provide researchers and land managers for biological invasion science and management with accurate information of the distribution of invasive alien species and their dynamics. Remotely sensed data that reveal spatial distribution of the earth’s surface features/objects provide great potential for this purpose. Consistent satellite monitoring of alien invasive plants is often difficult because of lack of sufficient spectral contrast between them and co-occurring plants species. Time series analysis of spectral properties of the species can reveal timing of their variations among adjacent species. This information can improve accuracy of invasive species discrimination and mapping using remote sensing data at large scale. We sought to identify and better understand the optimal time window and key spectral features sufficient to detect invasive Acacia trees in heterogeneous forested landscape in South Africa. We explored one-year (January to December 2018) time series spectral bands and vegetation indices derived from optical Copernicus Sentinel-2 data. The attributes correspond to geographical information of invasive Acacia and native species recorded during a field survey undertaken from 21 February to 25 February 2018 over Kwa-Zulu Natal grasslands landscape, in South Africa. The results showed comparable separability prospects between times series of spectral bands and that of vegetation indices.Substantial differences between Acacia species and native species were observed from spectral indices and spectral bands which are sensitive to Leaf Area Index, canopy chlorophyll and nitrogen concentrations. The results further revealed spectral differences between Acacia species and co-occurring native vegetation in April (senescence for deciduous plants), June-July (dry season), September (peak flowering period of Acacia spp) and December (leaf green-up) with vegetation indices (overall accuracy > 80 %). While spectral bands showed the beginning of the growing season (November–January) and peak vegetation productivity (February-March) as the optimal seasons or dates for image acquisition for discriminating Acacias from its co-occurring native species (overall accuracy > 80 %). In general, the use of Sentinel-2 time series spectral bands and vegetation indices has increased our understanding of Australian Acacias spectral dynamics, and proved that the sentinel-2 data is useful for characterization and monitoring Acacias over a large scale. Our results and approach could assist in deriving detailed geographic information of the species and assessment of a spread invasive plant species and severity of invasion.  相似文献   

5.
Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (Hcanopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 Hcanopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 Hcanopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 Hcanopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted Hcanopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in Hcanopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.  相似文献   

6.
In this paper, remote sensing and GIS have been used to assess the status of NO2 at the south west of Iran. 221 data about concentration of NO2 was extracted from Ozone Monitoring Instrument. Ordinary kriging and inverse distance weighting interpolation methods was used to interpolate data. Results showed that ordinary kriging method using cross-validation have had less error. North east of the study area has the highest concentration of NO2 (329 molecule/cm2) and the concentration of NO2 decreases from north east to South west of the study area. On the other hand, data trend results showed that the data seems to exhibit a fairly strong trend in the east west direction and a weaker one in the north–south direction.  相似文献   

7.
Assessment of human health impact caused by air pollution is crucial for evaluating environmental hazards. In this paper, concentrations of six air pollutants (PM10, PM2.5, NO2, SO2, O3, and CO) were first derived from satellite observations, and then the overall human health risks in China caused by multiple air pollutants were assessed using an aggregated health risks index. Unlike traditional approach for human health risks assessment, which relied on the in-situ air pollution measurements, the spatial distribution of aggregated human health risks in China were obtained using satellite observations in this research. It was indicated that the remote sensing data have advantages over in-situ data in accessing human health impact caused by air pollution.  相似文献   

8.
Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.  相似文献   

9.
珠江三角洲地区SO2浓度卫星遥感长时间序列监测   总被引：2，自引：1，他引：1  
利用卫星OMI(Ozone Monitoring Instrument)数据获得的长时间序列SO2浓度,分析广州亚运会从申办到成功举办期间珠江三角洲地区近地面SO2浓度的变化过程,以及亚运会举办期间珠江三角洲SO2浓度分布状况。结果表明OMI能够反映近地表的SO2浓度变化趋势,广州亚运会从申办到成功举办期间珠江三角洲地区近地面SO2浓度表现出明显的季节变化,且亚运会期间该地区近地面SO2浓度比以往年同期要低,说明珠江三角洲地区联动的空气质量保障措施明显降低了该地区近地面的SO2浓度。  相似文献   

10.
Sentinel-2 is planned for launch in 2014 by the European Space Agency and it is equipped with the Multi Spectral Instrument (MSI), which will provide images with high spatial, spectral and temporal resolution. It covers the VNIR/SWIR spectral region in 13 bands and incorporates two new spectral bands in the red-edge region, which can be used to derive vegetation indices using red-edge bands in their formulation. These are particularly suitable for estimating canopy chlorophyll and nitrogen (N) content. This band setting is important for vegetation studies and is very similar to the ones of the Ocean and Land Colour Instrument (OLCI) on the planned Sentinel-3 satellite and the Medium Resolution Imaging Spectrometer (MERIS) on Envisat, which operated from 2002 to early 2012. This paper focuses on the potential of Sentinel-2 and Sentinel-3 in estimating total crop and grass chlorophyll and N content by studying in situ crop variables and spectroradiometer measurements obtained for four different test sites. In particular, the red-edge chlorophyll index (CIred-edge), the green chlorophyll index (CIgreen) and the MERIS terrestrial chlorophyll index (MTCI) were found to be accurate and linear estimators of canopy chlorophyll and N content and the Sentinel-2 and -3 bands are well positioned for deriving these indices. Results confirm the importance of the red-edge bands on particularly Sentinel-2 for agricultural applications, because of the combination with its high spatial resolution of 20 m.  相似文献   

11.
为探讨1996年以来华北平原对流层NO_2柱浓度变化的空间特征,基于GOME、SCIAMACHY和OMI卫星传感器的监测数据,以SCIAMACHY的NO_2柱浓度为基准,根据建立的GOME和SCIAMACHY,SCIAMACHY和OMI之间的相关关系,校正GOME和OMI监测的NO_2数据;利用线性正弦曲线模型拟合方法研究1996年—2016年长时间序列华北平原对流层NO_2变化的空间分布特征。结果显示,华北平原对流层NO_2浓度自1996年开始持续上升,到2011年达到最高值,然后呈现下降趋势。该变化趋势主要受经济发展和环保政策的双重影响。1996年—2011年,NO_2柱浓度高值区分布在北京市、天津市、河北唐山市和保定市、山东德州市和济南市、安徽滁州市以及江苏南京市、常州市和无锡市,并且具有较高增长率;2012年—2016年NO_2柱浓度平均值远高于1996年—2011年NO_2柱浓度平均值,高值地区范围扩大,分布在河北南部、河南北部和山东西部,但华北平原地区NO_2柱浓度均呈现负增长,表明"十二五"规划提出的大气环保政策取得了显著成效。同时,对流层NO_2柱浓度可以反映典型环保事件如北京奥运会、国庆阅兵和南京青奥会时期大气环保政策的实施效果。  相似文献   

12.
Trees provide low-cost organic inputs, with the potential to improve livelihoods for rural communities. Understanding foliar nutrients of tree species is crucial for integration of trees into agroecosystems. The study explored nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca) concentrations of nine browse species collected from the bushveld region of South Africa using wet analysis and laboratory spectroscopy in the region 400–2500 nm, along with partial least squares (PLS) regression. We further explore the relationship between canopy reflectance of Sentinel-2 image and foliar N, P, K & Ca. Laboratory spectroscopy was significant for N estimation, while satellite imagery also revealed useful information about the estimation of nitrogen at landscape level. Nitrogen was highly correlated with spectral reflectance (R2 = 0.72, p < 0.05) for winter and (R2 = 0.88, p < 0.05) for summer, whilst prediction of phosphorus potassium and calcium were considered not accurate enough to be of practical use. Modelling the relationship using Sentinel-2 data showed lower correlations for nitrogen (R2 = 0.44, p < 0.05) and the other nutrients when compared to the dried samples. The findings indicate that there is potential to assess and monitor resource quality of indigenous trees using nitrogen as key indicator. This multi-level remote sensing approach has promise for providing rapid plant nutrient analyses at different scales.  相似文献   

13.
The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa.  相似文献   

14.
This paper presents a new approach to estimate spatial Sun-Induced Fluorescence (SIF) using the empirical relationship between simulated Canopy Chlorophyll Concentration (CCC) and simulated SIF. PROSAIL model [PROpriétésSPECTrales (PROSPECT) and Scattering by Arbitrarily Inclined Leaves (SAIL) models] was used to simulate CCC. CCC maps were generated through an Automated Radiative Transfer Model Operator (ARTMO) using the PROSAIL model and Sentinel-2 Multi-Spectral Imager (MSI) imagery. The Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE) model was used to simulate SIF emitted at 740 nm (SIF740), at 760 nm (SIF760), and top of canopy (SIFTOC) (640-850 nm). The SCOPE model, configured with the specification of the Sentinel-2 sensor, simulates SIF within the spectrum range of 640-850 nm. A non-linear logarithmic relationship (R2>0.9, p < 0.05) was observed between simulated SIF and simulated CCC. Simulated CCC was linearly related to observed CCC with R2 0.88, 0.92 and 0.89 and RMSE = 0.04, 0.17 and 0.09 gm/m2 at p < 0.05 for summer, post-monsoon and early winter respectively. Whereas, the simulated CCC did not capture the full range of CCC variability for the post-monsoon season. Simulated SIF (SIF760) was well correlated with SIF from Orbiting Carbon Observatory-2 (OCO-2) satellite with R2 0.68, 0.73 and 0.73 (RMSE = <1 W/m2/sr/μm, p < 0.05) for the month of summer (April), pre-monsoon (May) and early winter season (November) respectively. Temporal SIFTOC effectively captured the seasonal variability associated with the phenology of deciduous tree species. Among various Sentinel-2 MSI derived VIs, Red Edge NDVI (RENDVI) exhibited maximum sensitivity with SIF (highest monthly average R2> 0.6, p < 0.05). The spatial SIF would serve as an useful link between airborne /satellite derived SIF and in-situ fluorescence measurements to understand multiscale SIF variability of terrestrial vegetation.  相似文献   

15.
Planting a cover crop between the main cropping seasons is an agricultural management measure with multiple potential benefits for sustainable food production. In the maize production system of the Netherlands, an effective establishment of a winter cover crop is important for reducing nitrogen leaching to groundwater. Cover crop establishment after maize cultivation is obliged by law for sandy soils and consequently implemented on nearly all maize fields, but the winter-time vegetative ground cover varies significantly between fields. The objectives of this study are to assess the variability in winter vegetative cover and evaluate to what extent this variability can be explained by the timing of cover crop establishment and weather conditions in two growing seasons (2017–2018). We used Sentinel-2 satellite imagery to construct NDVI time series for fields known to be cultivated with maize within the province of Overijssel. We fitted piecewise logistic functions to the time series in order to estimate cover crop sowing date and retrieve the fitted NDVI value for 1 December (NDVIDec). We used NDVIDec to represent the quality of cover crop establishment at the start of the winter season. The Sentinel-2 estimated sowing dates compared reasonably with ground reference data for eight fields (RMSE = 6.6 days). The two analysed years differed considerably, with 2018 being much drier and warmer during summer. This drought resulted in an earlier estimated cover crop sowing date (on average 19 days) and an NDVIDec value that was 0.2 higher than in 2017. Combining both years and all fields, we found that Sentinel-2 retrieved sowing dates could explain 55% of the NDVIDec variability. This corresponded to a positive relationship (R2 = 0.50) between NDVIDec and the cumulative growing degree days (GDD) between sowing date and 1 December until reaching 400 GDD. Based on cumulative GDD derived from two weather stations within Overijssel, we found that on average for the past three decades a sowing date of 19 September (± 7 days) allowed to attain these 400 GDD; this provides support for the current legislation that states that from 2019 onwards a cover crop should be sown before 1 October. To meet this deadline, while simultaneously ascertaining a harvest-ready main crop, in practice implies that undersowing of the cover crop during spring will gain importance. Our results show that Sentinel-2 NDVI time series can assess the effectiveness and timing of cover crop growth for small agricultural fields, and as such has potential to inform regulatory frameworks as well as farmers with actionable information that may help to reduce nitrogen leaching.  相似文献   

16.
ABSTRACT

This study investigates misregistration issues between Landsat-8/ Operational Land Imager and Sentinel-2A/ Multi-Spectral Instrument at 30?m resolution, and between multi-temporal Sentinel-2A images at 10?m resolution using a phase-correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30?m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10?m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07?±?0.02 pixels at 30?m resolution, and 0.09?±?0.05 and 0.15?±?0.06 pixels at 10?m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1st order polynomial function (affine transformation) yielded RMSE of 0.08?±?0.02 pixels at 30?m resolution and 0.12?±?0.06 (same Sentinel-2A orbits) and 0.20?±?0.09 (adjacent orbits) pixels at 10?m resolution.  相似文献   

17.
Five techniques were used to map nitrogen dioxide (NO2) concentrations in the United Kingdom. The methods used to predict from point data, collected as part of the UK NO2 diffusion tube network, were local linear regression (LR), inverse distance weighting (IDW), ordinary kriging (OK), simple kriging with a locally varying mean (SKlm) and kriging with an external drift (KED). These techniques may be divided into two groups: (i) those that use only a single variable in the prediction process (IDW, OK) and (ii) those that make use of additional variables as a part of prediction (LR, SKlm and KED). Nitrous oxides emission data were used as secondary data with LR, SKlm and KED. It was concluded that SKlm provided the most accurate predictions based on the summary statistics of prediction errors from cross-validation.  相似文献   

18.
Gravity reference stations for the National Gravity Survey of Botswana have been established at twenty-three sites throughout the country in a net linked to existing bases in South Africa, Kenya and Zambia with an internal accuracy of better than 0.5 gravity units (one gravity unit, gu, equals an acceleration of 10−6 m.s−2). The field procedure and reduction of data are explained and a list is given of the gravity values.  相似文献   

19.
The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.  相似文献   

20.
定量分析气溶胶与痕量气体之间的时空变化关系有助于进一步研究气粒转化。本文采用2006年—2015年MODIS气溶胶光学厚度(AOD)、细粒子模态比(FMF)和OMI痕量气体(SO_2、NO_2和HCHO)数据,对黄海、东海和南海区域上空的细粒子气溶胶与痕量气体进行定量分析。先对气溶胶和痕量气体作均值分析发现:AOD_(fine)、SO_2、NO_2和HCHO的均值在黄海、南海、东海均依次减小;再对气溶胶对痕量气体的敏感度分析发现:黄海地区的AOD_(fine)对SO_2最敏感,敏感度为0.424,这与中国东部沿海城市的人为排放有关;而东海和南海地区对HCHO的敏感度较高,依次为0.664和0.545,主要受东南亚和中国南方地区生物质燃烧影响。最后,对3个区域的气溶胶与痕量气体按季节作相关性分析发现:黄海地区AOD_(fine)在夏秋两季与SO_2的相关性较强(R0.5),主要由于夏秋两季的温湿度大,利于发生气—粒转化;东海地区夏季HCHO与AOD_(fine)相关性较明显(R=0.57);南海春季HCHO与AOD_(fine)相关性较好(R=0.57),呈现出区域与季节性的变化。最终发现,气溶胶与痕量气体随着时空变化存在相关关系。  相似文献   

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