Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundr...Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.展开更多
Hyperspectral remote sensing has emerged as a transformative technology for sustainable natural resource management by providing unprecedented insight into the biochemical,biophysical,and compositional properties of E...Hyperspectral remote sensing has emerged as a transformative technology for sustainable natural resource management by providing unprecedented insight into the biochemical,biophysical,and compositional properties of Earth’s surface.The high spectral resolution of hyperspectral sensors allows a very specific discrimination of materials,monitoring of environmental stress at a very early stage,and provides quantitative retrieval of ecological and geochemical parameters in a wide range of landscapes.The booming technology in sensor design,machine learning,spectral unmixing,and multi-sensor data fusion has further improved the analysis potential and application of imaging spectroscopy to a large extent.This paper involves a discussion of the oversight of such technological advances and the manner in which they are utilized in the principal fields that include forestry,agriculture,water,mineral exploration,and coastal ecosystems.Case studies allow us to identify the potential practical consequences of both spaceborne and unmanned aerial vehicles(UAV)-based hyperspectral systems and AI-based workflows that can be used to aid in more efficient and accurate environmental review.Even though the issues associated with data volume,atmospheric impacts,lack of uniformity in the calibration process,and socioeconomic limits continue to exist,the new technology in sensor miniaturization,cloud computing,and artificial intelligence indicates a fast-changing environment.All these developments make hyperspectral remote sensing a key instrument in solving global sustainability problems and evidence-based management of natural resources in an evolving world.展开更多
With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil app...With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil application of HRS imagery in the fields of agriculture,forestry,and environmental monitoring.China is playing an important role in this evolution,especially in recent years,with the successful launch and operation of a series of civil hyper-spectral spacecraft and satellites,including the Shenzhou-3 spacecraft,the Gaofen-5 satellite,the SPARK satellite,the Zhuhai-1 satellite network for environmental and resources monitoring,the FengYun series of satellites for meteorological observation,and the Chang’E series of spacecraft for planetary exploration.The Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and microsatellites.This paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the applications.In addition,the future development trends of HRS in China are also discussed and analyzed.展开更多
Chlorophyll α（ch1-α） and suspended solid concentrations are two frequently used water quality parameters for monitoring a lake. Traditional measurement of ch1-α and suspended solids, requiring laborious laborator...Chlorophyll α（ch1-α） and suspended solid concentrations are two frequently used water quality parameters for monitoring a lake. Traditional measurement of ch1-α and suspended solids, requiring laborious laboratory work, which is often expensive and time consuming. Hyperspectral remote-sensing measurement provides a fast and easy tool for estimating water trophic status. In situ hyperspectral data on March 7-8, July 6-7, September 20 and December 7-8, 2004 and the corresponding water chemical data were used to regress the algorithm of water quality parameters. Results showed that the peak of water leaving radiance around 700 nm （R700） varied proportionally with ch1-α concentration, and moved to infrared when algal bloom occurred. The reflectance ratio of R702/R685 was well correlated with ch1-α when water surface in no algal bloom case and the correlation coefficient was better if absorption of phycocyanin was considered. The reflectance ratio R620/R531 was highly correlated to the concentration of suspended solids. The relationship between suspended solids and other band groups were also compared. Secchi disk depth could be calculated by non-linear correlation with suspended solids concentration.展开更多
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
Requirements for monitoring the coastal zone environment are first summarized. Then the appli- cation of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coa...Requirements for monitoring the coastal zone environment are first summarized. Then the appli- cation of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coast beaches and bottom matter, target recognition, mine detection, oil spill identification and ocean color remote sensing. Finally, what is needed to follow on in application of hyperspectral remote sensing to coast environment is recommended.展开更多
The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric ...The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.展开更多
Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Theref...Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.展开更多
Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is cur...Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.展开更多
Taking into account the demands of hyperspectral remote sensing(RS) image retrieval and processing, some encoding methods of spectral vector including direct encoding, feature-based encoding and tree-based encoding me...Taking into account the demands of hyperspectral remote sensing(RS) image retrieval and processing, some encoding methods of spectral vector including direct encoding, feature-based encoding and tree-based encoding methods are proposed and compared. In direct encoding, based on the analysis of binary encoding and quad-value encoding, decimal encoding is proposed. It is proved that quad-value encoding and decimal encoding are suitable to fast processing and retrieval. In absorption feature-based encoding method, five common metrics are compared. Because locations of reflection/absorption features are sensitive to noise, this method is not very effective in retrieval. In tree-based encoding methods, bitree, quadtree, octree and hextree are proposed and discussed. It is proved that 2-level octree and 2-level hextree are more effective than bitree and quadtree. Finally, quad-value encoding, decimal encoding, 2-level octree and 2-level hextree are proposed in spectral vectors encoding, similarity measure and hyperspectral RS image retrieval.展开更多
Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works,the authors in the pr...Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works,the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively,fulfilling the growth monitoring of the transgenic samples.The spectral parameters(spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts,and thus could be applied as indicators of biophysical or biochemical processes changes of plant.By ASD portable field spectroradiometer with high-density probe,fine foliar spectra of 8 groups were obtained.By analyzing spectral angle and continuum removal,the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis.By investigating spectral indices of the samples,the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content.In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated.The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches.By this technique,quantitative and qualitative results of sample spectra could be provided as prior knowledge,as certain orientation,for laboratory professional advanced transgenic breeding study.展开更多
The classification of hyperspectral remote sensing data is an important problem theoretically and practically.With the increase of spectral bands,the separability of objects on remote sensing image should be improved....The classification of hyperspectral remote sensing data is an important problem theoretically and practically.With the increase of spectral bands,the separability of objects on remote sensing image should be improved.But the effects of traditional algorithm on feature extraction such as principal component analysis(PCA)is not so good for hyperspectral image.The key problem is that PCA can only represent the linear structure of data set;while the data clouds of different objects on hyperspectral image usually distribute on a nonlinear manifold.This paper established an algorithm of nonlinear feature extraction named as nonlinear principal poly lines,based on the algorithm,a classifier is constructed and the classification accuracy of hyperspectral image can be improved.展开更多
Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands,high resolution,and abundant information.Although researchers have paid considerable attention to selecti...Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands,high resolution,and abundant information.Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks,the influence of bandwidth on the inversion accuracy are ignored.In this study,we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City,Xinjiang Uygur Autonomous Region,China and measured the ground spectra of these samples.The original spectra were resampled with different bandwidths.A Partial Least Squares Regression(PLSR)model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored.According to the results,the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm,with the model determination coefficient(R^(2))of 0.5907.The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5-80 nm,but the accuracy decreases significantly at 85 nm bandwidth(R^(2)=0.5473),and the accuracy gradually decreased at bandwidths beyond 85 nm.Hence,bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model.This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.展开更多
Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protectio...Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.展开更多
Lithium(Li)is an‘emerging'environmental pollutant,especially in soil,which is a great concern because it can endanger human health through the food chain.Compared with traditional chemical analyses,hyperspectral ...Lithium(Li)is an‘emerging'environmental pollutant,especially in soil,which is a great concern because it can endanger human health through the food chain.Compared with traditional chemical analyses,hyperspectral techniques have achieved many exciting results in soil metal monitoring due to their advantages of being fast and non-destructive.However,insufficient attention has been paid to lithium in soil,and the feasibility of its estimation using hyperspectral techniques needs to be investigated.We studied 97 soil samples from claytype lithium mines in the Ertanggou area of the East Tianshan Mountains of Xinjiang to explore the effects of spectral resolution,fractional order derivatives(FOD),and characteristic band selection on the estimation accuracy of clay Li content,to obtain a fast and effective method for estimating clay Li content.Finally,we developed a new method for rapid and nondestructive estimation of soil lithium content.We have obtained some important results from the study.Spectral resolution exerts a significant impact on model performance,and its reduction usually leads to a decline in model performance.For the full band,the models constructed with low-order derivatives were superior to those with high-order derivatives,and the best model was obtained at the 0.4-order derivative(coefficient of determination(R^(2))and relative predictive deviation(RPD)of 0.777 and 2.118,respectively).In the characteristic bands,the lower order is sensitive to the visible-near-infrared range,and the higher order is sensitive to the short-wave infrared range,and the model constructed with the higher-order derivatives outperforms the lower-order derivatives.In this study,the combination of FOD and Random Forest(RF)can significantly improve the model performance,with R^(2),Relative Root Mean Squared Error(RRMSE),and RPD being 0.849,1.526,and 2.574,respectively.Therefore,this research provides a theoretical basis and technical reference for imaging hyperspectral exploration of anomalous areas of clay-type Li resources.展开更多
Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events.Leaf water content(LWC)is an important waterlogging indicator,and hyperspectral ...Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events.Leaf water content(LWC)is an important waterlogging indicator,and hyperspectral remote sensing provides a non-destructive,real-time and reliable method to determine LWC.Thus,based on a pot experiment,winter wheat was subjected to different gradients of waterlogging stress at the jointing stage.Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature.Combined with methods such as vegetation index construction,correlation analysis,regression analysis,BP neural network(BPNN),etc.,we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC.LWC decreased faster under severe stress than under slight stress,but the effect of long-term slight stress was greater than that of short-term severe stress.The sensitive spectral bands of LWC were located in the visible(VIS,400–780 nm)and short-wave infrared(SWIR,1400–2500 nm)regions.The BPNN Model with the original spectrum at 648 nm,the first derivative spectrum at 500 nm,the red edge position(λr),the new vegetation index RVI(437,466),NDVI(437,466)and NDVI´(747,1956)as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set:R^(2)=0.889,RMSE=0.138;validation set:R^(2)=0.891,RMSE=0.518).These results have important theoretical significance and practical application value for the precise control of waterlogging stress.展开更多
In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree alg...In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.展开更多
Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hy...Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM). Global optimal search performance of PSO is improved by using a chaotic optimization search technique. Granularity based grid search strategy is used to optimize the SVM model parameters. Parameter optimization and classification of the SVM are addressed using the training date corre-sponding to the feature subset. A false classification rate is adopted as a fitness function. Tests of feature selection and classification are carried out on a hyperspectral data set. Classification performances are also compared among different feature extraction methods commonly used today. Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands. A feasible approach is provided for feature selection and classifica-tion of hyperspectral image data.展开更多
The fraction of photosynthetically active radiation （FPAR） is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and...The fraction of photosynthetically active radiation （FPAR） is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index （VI） and neural network （NN） methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance （SWIR） bands are stronger than the positive correlations between FPAR and near-infrared band re- flectance （NIR）. For the six VIs, the normalized difference vegetation index （NDVI） and simple ratio （SR） performed best for estimating corn FPAR （the maximum R2 of 0.8849 and 0.8852, respectively）. However, the NN method esti- mated results （the maximum Rz is 0.9417） were obviously better than all of the VIs. For NN method, the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands; for VIs, however, they were from the SWIR and NIR bands. As for both the methods, the SWIR band performed exceptionally well for corn FPAR estimation. This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content, which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation （APAR）, and makes further impact on corn-canopy FPAR.展开更多
Potentially harmful cyanobacterial blooms are an emerging environmental concern in freshwater bodies worldwide. Cyanobacterial blooms are generally caused by high nutrient inputs and warm, still waters and have been a...Potentially harmful cyanobacterial blooms are an emerging environmental concern in freshwater bodies worldwide. Cyanobacterial blooms are generally caused by high nutrient inputs and warm, still waters and have been appearing with increasing frequency in water bodies used for drinking water supply and recreation, a problem which will likely worsen with a warming climate. Cyanobacterial blooms are composed of genera with known biological pigments and can be distinguished and analyzed via hyperspectral image collection technology such as remote sensing by satellites, airplanes, and drones. Here, we utilize hyperspectral microscopy and imaging spectroscopy to charac</span><u><span style="font-family:Verdana;">t</span></u><span style="font-family:Verdana;">erize and differentiate several important bloom-forming cyanobacteria genera obtained in the field during active research programs conducted by US Geological Survey and from commercial sources. Many of the cyanobacteria genera showed differences in their spectra that may be used to identify and predict their occurrence, including peaks and valleys in spectral reflectance. </span><span><span style="font-family:Verdana;">Because certain cyanobacteria, such as </span><i><span style="font-family:Verdana;">Cylindrospermum</span></i><span style="font-family:Verdana;"> or </span><i><span style="font-family:Verdana;">Dolichospe</span></i></span><i><span style="font-family:Verdana;">rmum</span></i><span style="font-family:Verdana;">, are more prone to produce cyanotoxins than others, the ability to different</span><span style="font-family:Verdana;">iate these species may help target high priority waterbodies for sampl</span><span style="font-family:Verdana;">ing. These spectra may also be used to prioritize restoration and research efforts </span><span style="font-family:Verdana;">to control cyanobacterial harmful algal blooms (CyanoHABs) and improv</span><span style="font-family:Verdana;">e water quality for aquatic life and humans alike.展开更多
文摘Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.
基金supported by the Shandong Province Higher Education Institutions New Technology R&D Platform—Spatiotemporal IoT Cloud Application New Technology R&D Center,Shandong Vocational Education Skill Master Studio—Zhao Yaqian Skill Master Studio,and Shandong University of Engineering and Vocational Technology.
文摘Hyperspectral remote sensing has emerged as a transformative technology for sustainable natural resource management by providing unprecedented insight into the biochemical,biophysical,and compositional properties of Earth’s surface.The high spectral resolution of hyperspectral sensors allows a very specific discrimination of materials,monitoring of environmental stress at a very early stage,and provides quantitative retrieval of ecological and geochemical parameters in a wide range of landscapes.The booming technology in sensor design,machine learning,spectral unmixing,and multi-sensor data fusion has further improved the analysis potential and application of imaging spectroscopy to a large extent.This paper involves a discussion of the oversight of such technological advances and the manner in which they are utilized in the principal fields that include forestry,agriculture,water,mineral exploration,and coastal ecosystems.Case studies allow us to identify the potential practical consequences of both spaceborne and unmanned aerial vehicles(UAV)-based hyperspectral systems and AI-based workflows that can be used to aid in more efficient and accurate environmental review.Even though the issues associated with data volume,atmospheric impacts,lack of uniformity in the calibration process,and socioeconomic limits continue to exist,the new technology in sensor miniaturization,cloud computing,and artificial intelligence indicates a fast-changing environment.All these developments make hyperspectral remote sensing a key instrument in solving global sustainability problems and evidence-based management of natural resources in an evolving world.
基金This work was supported by National Natural Science Foundation of China under Grant Nos.42071350,41820104006,41771385 and 41622107supported by Postdoctoral Research Foundation of China.
文摘With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil application of HRS imagery in the fields of agriculture,forestry,and environmental monitoring.China is playing an important role in this evolution,especially in recent years,with the successful launch and operation of a series of civil hyper-spectral spacecraft and satellites,including the Shenzhou-3 spacecraft,the Gaofen-5 satellite,the SPARK satellite,the Zhuhai-1 satellite network for environmental and resources monitoring,the FengYun series of satellites for meteorological observation,and the Chang’E series of spacecraft for planetary exploration.The Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and microsatellites.This paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the applications.In addition,the future development trends of HRS in China are also discussed and analyzed.
基金Supported by National Natural Science Foundation of China (No. 40576078), Natural Science Foundation of Guangdong Province (No. 5003685), Post-Doctor Foundation of China, Post-doctor Foundation of Zhejiang Province, Post-Doctor Foundation of Shanghai and the Na-tional High-Tech R&D of China (863 Program) (No. 2002AA639490)
文摘Chlorophyll α（ch1-α） and suspended solid concentrations are two frequently used water quality parameters for monitoring a lake. Traditional measurement of ch1-α and suspended solids, requiring laborious laboratory work, which is often expensive and time consuming. Hyperspectral remote-sensing measurement provides a fast and easy tool for estimating water trophic status. In situ hyperspectral data on March 7-8, July 6-7, September 20 and December 7-8, 2004 and the corresponding water chemical data were used to regress the algorithm of water quality parameters. Results showed that the peak of water leaving radiance around 700 nm （R700） varied proportionally with ch1-α concentration, and moved to infrared when algal bloom occurred. The reflectance ratio of R702/R685 was well correlated with ch1-α when water surface in no algal bloom case and the correlation coefficient was better if absorption of phycocyanin was considered. The reflectance ratio R620/R531 was highly correlated to the concentration of suspended solids. The relationship between suspended solids and other band groups were also compared. Secchi disk depth could be calculated by non-linear correlation with suspended solids concentration.
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
基金The National "973" Program of China under contract No.2009CB723902the Key Projects of the Knowledge Innovation Program of Chinese Academy of Sciences under contract No.KZCX1-YW-14-2.
文摘Requirements for monitoring the coastal zone environment are first summarized. Then the appli- cation of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coast beaches and bottom matter, target recognition, mine detection, oil spill identification and ocean color remote sensing. Finally, what is needed to follow on in application of hyperspectral remote sensing to coast environment is recommended.
基金This research was supported by the Ningxia Hui Autonomous Region Key Research and Development Plan(2022BEG03052).
文摘The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.
基金funded by China Geological Survey (grant no.1212011120899)the Department of Geology & Mining, China National Nuclear Corporation (grant no.201498)
文摘Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.
基金The National Natural Science Fundation of China under contract No.41306091the Public Science and Technology Research Funds Projects of Ocean under contract Nos 201105016 and 201505019
文摘Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.
文摘Taking into account the demands of hyperspectral remote sensing(RS) image retrieval and processing, some encoding methods of spectral vector including direct encoding, feature-based encoding and tree-based encoding methods are proposed and compared. In direct encoding, based on the analysis of binary encoding and quad-value encoding, decimal encoding is proposed. It is proved that quad-value encoding and decimal encoding are suitable to fast processing and retrieval. In absorption feature-based encoding method, five common metrics are compared. Because locations of reflection/absorption features are sensitive to noise, this method is not very effective in retrieval. In tree-based encoding methods, bitree, quadtree, octree and hextree are proposed and discussed. It is proved that 2-level octree and 2-level hextree are more effective than bitree and quadtree. Finally, quad-value encoding, decimal encoding, 2-level octree and 2-level hextree are proposed in spectral vectors encoding, similarity measure and hyperspectral RS image retrieval.
基金supported by The Research Grants Council,Hong Kong:Competitive Earmarked Research Grant,No.461907
文摘Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works,the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively,fulfilling the growth monitoring of the transgenic samples.The spectral parameters(spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts,and thus could be applied as indicators of biophysical or biochemical processes changes of plant.By ASD portable field spectroradiometer with high-density probe,fine foliar spectra of 8 groups were obtained.By analyzing spectral angle and continuum removal,the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis.By investigating spectral indices of the samples,the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content.In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated.The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches.By this technique,quantitative and qualitative results of sample spectra could be provided as prior knowledge,as certain orientation,for laboratory professional advanced transgenic breeding study.
基金Project(40174003)supported by the National Natural Science Foundation of China
文摘The classification of hyperspectral remote sensing data is an important problem theoretically and practically.With the increase of spectral bands,the separability of objects on remote sensing image should be improved.But the effects of traditional algorithm on feature extraction such as principal component analysis(PCA)is not so good for hyperspectral image.The key problem is that PCA can only represent the linear structure of data set;while the data clouds of different objects on hyperspectral image usually distribute on a nonlinear manifold.This paper established an algorithm of nonlinear feature extraction named as nonlinear principal poly lines,based on the algorithm,a classifier is constructed and the classification accuracy of hyperspectral image can be improved.
基金supported by the Science and Technology Major Project of Xinjiang Uygur Autonomous Region,China(2021A03001-3)the Key Area Deployment Project of the Chinese Academy of Sciences(ZDRW-ZS-2020-4-30)the National Natural Science Foundation of China(U1803117).
文摘Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands,high resolution,and abundant information.Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks,the influence of bandwidth on the inversion accuracy are ignored.In this study,we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City,Xinjiang Uygur Autonomous Region,China and measured the ground spectra of these samples.The original spectra were resampled with different bandwidths.A Partial Least Squares Regression(PLSR)model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored.According to the results,the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm,with the model determination coefficient(R^(2))of 0.5907.The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5-80 nm,but the accuracy decreases significantly at 85 nm bandwidth(R^(2)=0.5473),and the accuracy gradually decreased at bandwidths beyond 85 nm.Hence,bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model.This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.
基金The National Natural Science Foundation of China under contract Nos 61890964 and 42206177the Joint Funds of the National Natural Science Foundation of China under contract No.U1906217.
文摘Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.
基金Sponsored the National Natural Science Foundation of China(42502088)the National Major Science and Technology Project of China(2025ZD1007504-1)+2 种基金the Special Research Fund of Natural Science(Special Post)of Guizhou University(X202402)the Guizhou Provincial Science and Technology Projects(QKHJC[2024]youth 153)the Xinjiang Uygur Autonomous Region Natural Science Foundation(2024D01A147)。
文摘Lithium(Li)is an‘emerging'environmental pollutant,especially in soil,which is a great concern because it can endanger human health through the food chain.Compared with traditional chemical analyses,hyperspectral techniques have achieved many exciting results in soil metal monitoring due to their advantages of being fast and non-destructive.However,insufficient attention has been paid to lithium in soil,and the feasibility of its estimation using hyperspectral techniques needs to be investigated.We studied 97 soil samples from claytype lithium mines in the Ertanggou area of the East Tianshan Mountains of Xinjiang to explore the effects of spectral resolution,fractional order derivatives(FOD),and characteristic band selection on the estimation accuracy of clay Li content,to obtain a fast and effective method for estimating clay Li content.Finally,we developed a new method for rapid and nondestructive estimation of soil lithium content.We have obtained some important results from the study.Spectral resolution exerts a significant impact on model performance,and its reduction usually leads to a decline in model performance.For the full band,the models constructed with low-order derivatives were superior to those with high-order derivatives,and the best model was obtained at the 0.4-order derivative(coefficient of determination(R^(2))and relative predictive deviation(RPD)of 0.777 and 2.118,respectively).In the characteristic bands,the lower order is sensitive to the visible-near-infrared range,and the higher order is sensitive to the short-wave infrared range,and the model constructed with the higher-order derivatives outperforms the lower-order derivatives.In this study,the combination of FOD and Random Forest(RF)can significantly improve the model performance,with R^(2),Relative Root Mean Squared Error(RRMSE),and RPD being 0.849,1.526,and 2.574,respectively.Therefore,this research provides a theoretical basis and technical reference for imaging hyperspectral exploration of anomalous areas of clay-type Li resources.
基金This work was supported by the National Key Research and Development Program of China(2016YFD0200600,2016YFD0200601)the Key Research and Development Program of Hebei Province,China(19227407D)+1 种基金the Central Public-interest Scientific Institution Basal Research Fund(JBYW-AII-2020-29,JBYW-AII-2020-30)the Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2020-AII).
文摘Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events.Leaf water content(LWC)is an important waterlogging indicator,and hyperspectral remote sensing provides a non-destructive,real-time and reliable method to determine LWC.Thus,based on a pot experiment,winter wheat was subjected to different gradients of waterlogging stress at the jointing stage.Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature.Combined with methods such as vegetation index construction,correlation analysis,regression analysis,BP neural network(BPNN),etc.,we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC.LWC decreased faster under severe stress than under slight stress,but the effect of long-term slight stress was greater than that of short-term severe stress.The sensitive spectral bands of LWC were located in the visible(VIS,400–780 nm)and short-wave infrared(SWIR,1400–2500 nm)regions.The BPNN Model with the original spectrum at 648 nm,the first derivative spectrum at 500 nm,the red edge position(λr),the new vegetation index RVI(437,466),NDVI(437,466)and NDVI´(747,1956)as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set:R^(2)=0.889,RMSE=0.138;validation set:R^(2)=0.891,RMSE=0.518).These results have important theoretical significance and practical application value for the precise control of waterlogging stress.
基金Projects 40401038 and 40871195 supported by the National Natural Science Foundation of ChinaNCET-06-0476 by the Program for New Century Excellent Talents in University20070290516 by the Specialized Research Fund for the Doctoral Program of Higher Education
文摘In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.
基金Project 40401038 supported by the National Natural Science Foundation of China
文摘Classification and recognition of hyperspectral remote sensing images is not the same as that of conventional multi-spectral remote sensing images. We propose,a novel feature selection and classification method for hyperspectral images by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM). Global optimal search performance of PSO is improved by using a chaotic optimization search technique. Granularity based grid search strategy is used to optimize the SVM model parameters. Parameter optimization and classification of the SVM are addressed using the training date corre-sponding to the feature subset. A false classification rate is adopted as a fitness function. Tests of feature selection and classification are carried out on a hyperspectral data set. Classification performances are also compared among different feature extraction methods commonly used today. Results indicate that this hybrid method has a higher classification accuracy and can effectively extract optimal bands. A feasible approach is provided for feature selection and classifica-tion of hyperspectral image data.
基金Under the auspices of National Key Research Program of Global Change Research (No.2010CB951302)National Natural Science Fundation of China (No.40771146)China Postdoctoral Science Foundation Funded Project (No.07Z7601MZ1)
文摘The fraction of photosynthetically active radiation （FPAR） is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index （VI） and neural network （NN） methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance （SWIR） bands are stronger than the positive correlations between FPAR and near-infrared band re- flectance （NIR）. For the six VIs, the normalized difference vegetation index （NDVI） and simple ratio （SR） performed best for estimating corn FPAR （the maximum R2 of 0.8849 and 0.8852, respectively）. However, the NN method esti- mated results （the maximum Rz is 0.9417） were obviously better than all of the VIs. For NN method, the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands; for VIs, however, they were from the SWIR and NIR bands. As for both the methods, the SWIR band performed exceptionally well for corn FPAR estimation. This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content, which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation （APAR）, and makes further impact on corn-canopy FPAR.
文摘Potentially harmful cyanobacterial blooms are an emerging environmental concern in freshwater bodies worldwide. Cyanobacterial blooms are generally caused by high nutrient inputs and warm, still waters and have been appearing with increasing frequency in water bodies used for drinking water supply and recreation, a problem which will likely worsen with a warming climate. Cyanobacterial blooms are composed of genera with known biological pigments and can be distinguished and analyzed via hyperspectral image collection technology such as remote sensing by satellites, airplanes, and drones. Here, we utilize hyperspectral microscopy and imaging spectroscopy to charac</span><u><span style="font-family:Verdana;">t</span></u><span style="font-family:Verdana;">erize and differentiate several important bloom-forming cyanobacteria genera obtained in the field during active research programs conducted by US Geological Survey and from commercial sources. Many of the cyanobacteria genera showed differences in their spectra that may be used to identify and predict their occurrence, including peaks and valleys in spectral reflectance. </span><span><span style="font-family:Verdana;">Because certain cyanobacteria, such as </span><i><span style="font-family:Verdana;">Cylindrospermum</span></i><span style="font-family:Verdana;"> or </span><i><span style="font-family:Verdana;">Dolichospe</span></i></span><i><span style="font-family:Verdana;">rmum</span></i><span style="font-family:Verdana;">, are more prone to produce cyanotoxins than others, the ability to different</span><span style="font-family:Verdana;">iate these species may help target high priority waterbodies for sampl</span><span style="font-family:Verdana;">ing. These spectra may also be used to prioritize restoration and research efforts </span><span style="font-family:Verdana;">to control cyanobacterial harmful algal blooms (CyanoHABs) and improv</span><span style="font-family:Verdana;">e water quality for aquatic life and humans alike.
