Detection of early plant stress responses in hyperspectral images     

《ISPRS Journal of Photogrammetry and Remote Sensing》2014

Early stress detection in crop plants is highly relevant, but hard to achieve. We hypothesize that close range hyperspectral imaging is able to uncover stress related processes non-destructively in the early stages which are invisible to the human eye. We propose an approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images. Stress of an entire plant is detected by stress response levels at pixel scale. The focus is on drought stress in barley (Hordeum vulgare). Unsupervised learning is used to separate hyperspectral signatures into clusters related to different stages of stress response and progressive senescence. Whereas all such signatures may be found in both, well watered and drought stressed plants, their respective distributions differ. Ordinal classification with Support Vector Machines (SVM) is used to quantify and visualize the distribution of progressive stages of senescence and to separate well watered from drought stressed plants. For each senescence stage a distinctive set of most relevant Vegetation Indices (VIs) is identified. The method has been applied on two experiments involving potted barley plants under well watered and drought stress conditions in a greenhouse. Drought stress is detected up to ten days earlier than using NDVI. Furthermore, it is shown that some VIs have overall relevance, while others are specific to particular senescence stages. The transferability of the method to the field is illustrated by an experiment on maize (Zea mays).  相似文献   

Using high resolution QuickBird imagery for crop identification and area estimation     

Chenghai Yang  James H. Everitt  Reginald S. Fletcher  Dale Murden 《国际地球制图》2013,28(3):219-233

Imagery from recently launched high spatial resolution satellite sensors offers new opportunities for crop assessment and monitoring. A 2.8-m multispectral QuickBird image covering an intensively cropped area in south Texas was evaluated for crop identification and area estimation. Three reduced-resolution images with pixel sizes of 11.2 m, 19.6 m, and 30.8 m were also generated from the original image to simulate coarser resolution imagery from other satellite systems. Supervised classification techniques were used to classify the original image and the three aggregated images into five crop classes (grain sorghum, cotton, citrus, sugarcane, and melons) and five non-crop cover types (mixed herbaceous species, mixed brush, water bodies, wet areas, and dry soil/roads). The five non-crop classes in the 10-category classification maps were then merged as one class. The classification maps were filtered to remove the small inclusions of other classes within the dominant class. For accuracy assessment of the classification maps, crop fields were ground verified and field boundaries were digitized from the original image to determine reference field areas for the five crops. Overall accuracy for the unfiltered 2.8-m, 11.2-m, 19.6-m, and 30.8-m classification maps were 71.4, 76.9, 77.1, and 78.0%, respectively, while overall accuracy for the respective filtered classification maps were 83.6, 82.3, 79.8, and 78.5%. Although increase in pixel size improved overall accuracy for the unfiltered classification maps, the filtered 2.8-m classification map provided the best overall accuracy. Percentage area estimates based on the filtered 2.8-m classification map (34.3, 16.4, 2.3, 2.2, 8.0, and 36.8% for grain sorghum, cotton, citrus, sugarcane, melons, and non-crop, respectively) agreed well with estimates from the digitized polygon map (35.0, 17.9, 2.4, 2.1, 8.0, and 34.6% for the respective categories). These results indicate that QuickBird imagery can be a useful data source for identifying crop types and estimating crop areas.  相似文献   

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping     

S. K. Maxwell  Michael E. Craig 《国际地球制图》2013,28(3):169-179

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (?0.8%–1.6% using the 2002 segment model to ?5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.  相似文献   

Two Mediterranean irrigation communities in front of water scarcity: A comparison using satellite image time series     

《Journal of Arid Environments》2013

Although a number of studies have analysed the methods to monitor crop water needs, the integration of phenological dynamics and irrigation using water stored in reservoirs has received little attention. This study is an example of such analysis applied to two irrigation communities (IC) located in a Spanish Mediterranean area, one with less water resources than the other, which it was carried out between 2002 and 2008. These years comprised periods of water surplus and water scarcity including the 2007–2008 drought considered by the Catalan public water authority the worst since 1944. The dynamics of maize, alfalfa, fruit trees and poplars were analysed using greenness and wetness extracted from remote sensing data. A statistical analysis was applied in order to find out the relationship between crop wetness and stored water. Results show that the IC used two methods for water-saving: crop substitution, by decreasing the area of maize, and timing the crop cycle, delaying or advancing sowing depending on water availability. In conclusion although one IC is drier in rainfall terms than the other, a similar crop wetness status was detected and no difference was observed between periods of water scarcity and water surplus.  相似文献   

Mathematical model for evaluation of the effect of soil erosion on soil productivity     

P. Todorovic  D. A. Woolhiser  K. G. Renard 《水文研究》1987,1(2):181-198

This paper is concerned with the development of a stochastic model for evaluating the long-term effect of soil erosion on soil productivity. Due to random variations in annual crop yield, the effect of erosion on crop production is not easily detectable in the short run, but becomes gradually evident over a sufficiently long time period. Under these circumstances, it seems that an experimental approach to this problem may be very difficult. The long period of time over which such an experiment has to be conducted may result in prohibitively high costs. In addition, it also means that eventual resolution of this problem must be postponed until a distant future time. The stochastic model formulated here provides us with a useful tool to assess the trend in quantitative changes in crop production due to erosion and to project future crop losses. The model is a discrete parameter stochastic process. Its derivation is based on a single assumption that the annual loss rates form a sequence of independent random variables {Z_i}₁∞ (in this paper, we consider only two particular cases: (a) {Z_i}₁∞ is a sequence of constants; (b) {Z_i}₁∞ is a sequence of independent identically distributed random variables). For these particular cases, we obtained its marginal n-dimensional distribution function and correlation function. One of the principal model features is its simple structure and remarkable lack of restrictive and unrealistic assumptions.  相似文献   

Assessing the spatial distribution of crop areas using a cross-entropy method   总被引：1，自引：0，他引：1  

Liangzhi You  Stanley Wood 《International Journal of Applied Earth Observation and Geoinformation》2005,7(4):310-323

While crop production statistics are reported on a geopolitical – often national – basis, we often need to know, for example, the status of production or productivity within specific sub-regions, watersheds, or agro-ecological zones. Such re-aggregations are typically made using expert judgments or simple area-weighting rules. We describe a new, entropy-based approach to the plausible estimates of the spatial distribution of crop areas. Using this approach tabular crop production statistics are blended judiciously with an array of other secondary data to assess the areas of specific crops within individual ‘pixels’—typically 25–100 km² in size. The information utilized includes crop production statistics, farming system characterization, satellite-based interpretation of land cover, biophysical crop suitability assessments, and population density. An application is presented in which Brazilian state level production statistics are used to generate pixel level crop area data for eight crops. To validate the spatial allocation we aggregated the pixel estimates to obtain synthetic estimates of municipality level areas in Brazil, and compared those estimates with actual municipality statistics. The approach produced extremely promising results. We then examined the robustness of these results compared to simplified approaches to spatializing crop production statistics and showed that, while computationally intensive, the cross-entropy method does provide more reliable spatial allocations.  相似文献   

江苏旱涝灾害对农作物经济损失评估的探讨   总被引：1，自引：2，他引：1  

干莲君  项瑛等 《气象科学》2001,21(1):122-126

江苏旱涝灾害常给农作物经济带来很大损失，本文从旱涝年灾情资料找出各级灾害强度及相应的灾情指标值，根据赂级灾害相应指标值和有关经济指标，建立灾害影响农作物经济的评估模型，应用评估模型进行各级灾害农作物经济损失的评估，通过统计和实际对照，证实评估模型的客观性和可行性。  相似文献   

Inter-annual Variability of Moisture Flux from the Prairie Agro-ecosystem: Impact of Crop Phenology on the Seasonal Pattern of Tornado Days   总被引：1，自引：0，他引：1  

R. L. Raddatz  J. D. Cummine 《Boundary-Layer Meteorology》2003,106(2):283-295

This study, through the inclusion of a simpleparameterization of the phenologicaldevelopment of spring wheat in evapotranspirationsimulations for 1988–2000, at a representativearid grassland and a representative transitionalgrassland site, delineated the inter-annualvariability of the seasonal moisture flux from theCanadian Prairie agro-ecosystem. Theagro-ecosystem's contribution to atmospheric boundary-layermoisture, at these representative sites, wasrelated to the seasonal pattern of tornado days in thegrassland eco-climatic zone for the averageyear, for a warmer/drier year and for a cooler/wetteryear. The following conclusions were drawn:(1) The moisture flux from the Prairie agro-ecosystemdisplays considerable inter-annualvariability due, in the main, to the rate andtiming of crop phenological development andassociated biophysical parameters, and (2) themoisture flux from the Prairie agro-ecosystemtranslates directly into changes in atmosphericboundary-layer moisture, which subsequentlyaffects the magnitude of the potential energyavailable for deep convection and the seasonalpattern of tornado days. For expansive agriculturalareas, representing the inter-annual variabilityof crop phenological development in land surfacemodels is critical to the successful simulationof the surface moisture flux, and thus thethermodynamic properties of the atmospheric boundarylayer. Therefore, it is of particularimportance to Prairie climate and climate change modelling.  相似文献   

基于东北玉米区域动力模型的低温冷害预报应用研究   总被引：35，自引：7，他引：35       下载免费PDF全文

刘布春  王石立  庄立伟  卢志光  史学丽  宋永佳 《应用气象学报》2003,14(5):616-625

在田间试验资料基础上，采用改进的发育模型和分区作物参数，结合前人有关研究成果建立了东北玉米区域动力模型，并利用模型模拟了12站40年 (1961~2000年) 玉米生长发育过程。确定抽雄期延迟天数为低温冷害指标，分析了历史低温冷害年及减产情况。模拟了典型冷害年和40年气候平均的0.25°×0.25°网格点玉米生长发育过程, 探讨了与区域气候模式结合进行低温冷害预报的方法。主要结论有:①玉米发育模型能够较好地模拟玉米发育期和发育期对低温冷害的响应，以抽雄期延迟天数为冷害指标评估的历史冷害发生状况基本符合历史实况。②模型有一定的模拟玉米生长量对低温冷害响应的能力，但还需要更多的试验数据校正品种参数，完善模型。③利用GIS技术，结合区域化的作物参数运行区域作物模型，是作物模型区域化应用的一种解决方案。④东北玉米区域动力模型解释性好，根据确定的害指标，以区域气候模式输出结果驱动玉米模型可以模拟和预测低温冷害，是农业气象灾害预测预报的一个有益的尝试。  相似文献   

Predicting stem borer density in maize using RapidEye data and generalized linear models     

《International Journal of Applied Earth Observation and Geoinformation》2017

Average maize yield in eastern Africa is 2.03 t ha⁻¹ as compared to global average of 6.06 t ha⁻¹ due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson (‘Po’) and negative binomial (‘NB’) distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB (‘ZINB’) models outperformed the ‘NB’ models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69–1.06 and RPD = 8.25–19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions.  相似文献