Airborne Multispectral Imagery Research Articles

Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot

Cotton root rot is a destructive disease affecting cotton production. Accurate identification of infected areas within fields is useful for cost-effective control of the disease. The uncertainties caused by various infection stages and newly infected plants make it difficult to achieve accurate classification results from airborne imagery. The objectives of this study were to apply fuzzy set theory and nonlinear stretching enhancement to airborne multispectral imagery for unsupervised classification of cotton root rot infections. Four cotton fields near Edroy and San Angelo, Texas, were selected for this study. Airborne multispectral imagery was taken and the color-infrared (CIR) composite images were used for classification. The intensity component was enhanced by using a fuzzy-set based method, and the saturation component was enhanced by a nonlinear stretching image enhancement algorithm. The enhanced CIR composite images were then classified into infected and noninfected areas. Iterative self organization data analysis and adaptive Otsu’s method were used to compare the performance of the proposed image enhancement method. The results showed that image enhancement has improved the classification accuracy of these two unsupervised classification methods for all four fields. The results from this study will be useful for detection of cotton root rot and for site-specific treatment of the disease.

Multispectral airborne imagery in the field reveals genetic determinisms of morphological and transpiration traits of an apple tree hybrid population in response to water deficit.

Genetic studies of response to water deficit in adult trees are limited by low throughput of the usual phenotyping methods in the field. Here, we aimed at overcoming this bottleneck, applying a new methodology using airborne multispectral imagery and in planta measurements to compare a high number of individuals.An apple tree population, grafted on the same rootstock, was submitted to contrasting summer water regimes over two years. Aerial images acquired in visible, near- and thermal-infrared at three dates each year allowed calculation of vegetation and water stress indices. Tree vigour and fruit production were also assessed. Linear mixed models were built accounting for date and year effects on several variables and including the differential response of genotypes between control and drought conditions.Broad-sense heritability of most variables was high and 18 quantitative trait loci (QTLs) independent of the dates were detected on nine linkage groups of the consensus apple genetic map. For vegetation and stress indices, QTLs were related to the means, the intra-crown heterogeneity, and differences induced by water regimes. Most QTLs explained 15-20% of variance.Airborne multispectral imaging proved relevant to acquire simultaneous information on a whole tree population and to decipher genetic determinisms involved in response to water deficit.

Use of multi-spectral airborne imagery to improve yield sampling in viticulture

The wine industry needs to know the yield of each vine field precisely to optimize quality management and limit the costs of harvest operations. Yield estimation is usually based on random vine sampling. The resulting estimations are often not precise enough because of the high variability within vineyard fields. The aim of the work was to study the relevance of using NDVI-based sampling strategies to improve estimation of mean field yield. The study was conducted in nine non-irrigated vine fields located in southern France. For each field, NDVI was derived from multi-spectral airborne images. The variables which define the yield: [berry weight at harvest (BWh), bunch number per vine (BuN) and berry number per bunch (BN)] were measured on a regular grid. This data-base allowed for five different sampling schemes to be tested. These sampling methods were mainly based on a stratification of NDVI values, they differed in the way as to whether NDVI was used as ancillary information to design a sampling strategy for BuN, BN, BW or for all yield variables together. Results showed a significant linear relationship between NDVI and BW, indicating the interest of using NDVI information to optimize sampling for this parameter. However this result is mitigated by the low incidence of BW in the yield variance (4 %) within the field. Other yield components, BuN and BN explain a higher percentage of yield variance (60 and 11 % respectively) but did not show any clear relationship with NDVI. A large difference was observed between fields, which justifies testing the optimized sampling methods on all of them and for all yield variables. On average, sampling methods based on NDVI systematically improved vine field yield estimates by at least 5–7 % compared to the random method. Depending on the fields, error improvement ranged from −2 to 15 %. Based on these results, the practical recommendation is to consider a two-step sampling method where BuN is randomly sampled and BW is sampled according to the NDVI values.

Evaluating unsupervised and supervised image classification methods for mapping cotton root rot

Cotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivora, is one of the most destructive plant diseases occurring throughout the southwestern United States. This disease has plagued the cotton industry for over a century, but effective practices for its control are still lacking. Recent research has shown that a commercial fungicide, flutriafol, has potential for the control of cotton root rot. To effectively and economically control this disease, it is necessary to identify infected areas within fields so that site-specific technology can be used to apply fungicide only to the infected areas. The objectives of this study were to evaluate unsupervised classification applied to multispectral imagery, unsupervised classification applied to the normalized difference vegetation index (NDVI)and six supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood and spectral angle mapper (SAM), neural net and support vector machine (SVM),for mapping cotton root rot from airborne multispectral imagery. Two cotton fields with a history of root rot infection in Texas, USA were selected for this study. Airborne imagery with blue, green, red and near-infrared bands was taken from the fields shortly before harvest when infected areas were fully expressed in 2011. The four-band images were classified into infected and non-infected zones using the eight classification methods. Classification agreement index values for infected area estimation between any two methods ranged from 0.90 to 1.00 for both fields, indicating a high degree of agreement among the eight methods. Accuracy assessment showed that all eight methods accurately identified root rot-infected areas with overall accuracy values from 94.0 to 96.5 % for Field 1 and 93.0 to 95.0 % for Field 2. All eight methods appear to be equally effective and accurate for detection of cotton root rot for site-specific management of this disease, though the NDVI-based classification, minimum distance and SAM can be easily implemented without the need for complex image processing capability. These methods can be used by cotton producers and crop consultants to develop prescription maps for effective and economical control of cotton root rot.

Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery

Giant reed is an aggressive invasive plant of riparian ecosystems in many sub-tropical and warm-temperate regions, including Mediterranean Europe. In this study we tested a set of geometric, spectral and textural attributes in an object based image analysis (OBIA) approach to map giant reed invasions in riparian habitats. Bagging Classification and Regression Tree were used to select the optimal attributes and to build the classification rules sets. Mapping accuracy was performed using landscape metrics and the Kappa coefficient to compare the topographical and geometric similarity between the giant reed patches obtained with the OBIA map and with a validation map derived from on-screen digitizing. The methodology was applied in two high spatial resolution images: an airborne multispectral imagery and the newly WorldView-2 imagery. A temporal coverage of the airborne multispectral images was radiometrically calibrated with the IR-Mad transformation and used to assess the influence of the phenological variability of the invader.We found that optimal attributes for giant reed OBIA detection are a combination of spectral, geometric and textural information, with different scoring selection depending on the spectral and spatial characteristics of the imagery. WorldView-2 showed higher mapping accuracy (Kappa coefficient of 77%) and spectral attributes, including the newly yellow band, were preferentially selected, although a tendency to overestimate the total invaded area, due to the low spatial resolution (2m of pixel size vs. 50cm) was observed. When airborne images were used, geometric attributes were primarily selected and a higher spatial detail of the invasive patches was obtained, due to the higher spatial resolution. However, in highly heterogeneous landscapes, the low spectral resolution of the airborne images (4 bands instead of the 8 of WorldView-2) reduces the capability to detect giant reed patches. Giant reed displays peculiar spectral and geometric traits, at leaf, canopy and stand level, which makes the OBIA approach a very suitable technique for management purposes.

Monitoring Cotton Root Rot Progression within a Growing Season Using Airborne Multispectral Imagery

Cotton root rot, caused by the fungus Phymatotrichopsis omnivora, is a serious and destructive disease affecting cotton production in the southwestern United States. Accurate delineation of cotton root rot infections is important for cost-effective management of the disease. The objective of this study was to use airborne multispectral imagery for monitoring the progression of root rot infections within cotton fields during a growing season. A number of cotton fields near Edroy and San Angelo, Texas were selected for this study. Airborne multispectral digital imagery with blue, green, red and near-infrared bands was taken from these fields two to four times during the 2010 growing season. The imagery for two fields from each of the two locations was georeferenced and classified into two to twenty spectral classes using unsupervised classification techniques. The optimal number of spectral classes was determined based on the average transformed divergence for each classification and the spectral classes were then grouped into root rot-infected and non-infected zones. The infected areas within each field were determined for each imaging date and compared among the different dates. Both airborne imagery and ground observations showed that cotton root rot expanded in different patterns and at different rates over the growing season. Toward the end of the growing season, the percentage of root rot-infected areas increased to 13.2% and 26.8% in the two fields in Edroy, and to 37.8% and 50.6% in the two fields in San Angelo. The results from this study will be useful for the understanding of the progression of the disease and for the development of site-specific treatment plans for the disease.

Method for oil spill information extraction from airborne multispectral imagery

In order to study the method for identifying the oil film using the multispectral data,the authors obtained the imagery of Zhoushan waters by using the marine aircraft- borne multi- spectral oil spill detector,and also acquired the reflectance spectra of the water and oil film by means of FieldSpec spectroradiometer. The image characteristics and spectral response characteristics were analyzed so as to detect the distribution of the relative thickness using the decision tree classifier. The overall accuracy is 93. 7%,which shows that the classification method based on the spectral characteristics could effectively recognize the thick thin sheen oil film and extract the information of marine oil pollution,thus sufficiently satisfying the requirements for monitoring marine oil spill.

Impact of spatial, spectral, and radiometric properties of multispectral imagers on glacier surface classification

Using multispectral remote sensing, glacier surfaces can be classified into a range of zones. The properties of these classes are used for a range of glaciological applications including mass balance measurements, glacial hydrology, and melt modelling. However, it is not immediately evident that multispectral data should be optimal for imaging glaciers and ice caps. Thus, this investigation takes an inverse perspective. Taking into account spectral and radiometric properties, in situ spectral reflectance data were used to simulate glacier surface response for a suite of multispectral sensors. Sensor-simulated data were classified and compared. In addition, airborne multispectral imagery was classified for a range of spatial resolutions and intercompared in three different ways. In these analyses, the most important property which determined the suitability of a multispectral imager for glacier surface classification was its radiometric range (i.e. gain settings). Low resolution imagery (250mpixels) is too coarse to represent the true complexity present on a glacier while medium resolution imagery (60m, 30m, or 20m) accurately represented the results derived from high resolution airborne imagery. Of those studied here, the satellite imagers currently in use that are most suitable for glacier surface classification are Landsat TM/ETM+ and ASTER (each with particular gain settings). Both Sentinel-2 and the OLI on Landsat 8 are also expected to be similarly qualified. Landsat MSS is also found to be radiometrically well-suited for glacier surface classification, but its lower spatial resolution makes it a secondary selection.

Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.

Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants

The study investigated the use of aerial multispectral imagery and ground-based hyperspectral data for the discrimination of different crop types and timely detection of cotton plants over large areas. Airborne multispectral imagery and ground-based spectral reflectance data were acquired at the same time over three large agricultural fields in Burleson Co., Texas during the 2010 growing season. The discrimination accuracy of aerial- and ground-based data was examined individually; then a multi-sensor data fusion technique was applied on both datasets in order to improve the accuracy of discrimination. The individual classification accuracy of data taken with the aerial- and ground-based sensors were 90% and 93.3%, respectively. In comparison, the accuracy of discriminating crop types with fused data was 100% in the calibration and only 3.33% misclassification in the cross-validation. These results suggest that data fusion techniques could greatly enhance our ability to detect cotton from other plants.

Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture

With increased use of precision agriculture techniques, information concerning within-field crop yield variability is becoming increasingly important for effective crop management. Despite the commercial availability of yield monitors, many crop harvesters are not equipped with them. Moreover, yield monitor data can only be collected at harvest and used for after-season management. On the other hand, remote sensing imagery obtained during the growing season can be used to generate yield maps for both within-season and after-season management. This paper gives an overview on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability. The methodologies for image acquisition and processing and for the integration and analysis of image and yield data are discussed. Five application examples are provided to illustrate how airborne multispectral and hyperspectral imagery and high-resolution satellite imagery have been used for mapping crop yield variability. Image processing techniques including vegetation indices, unsupervised classification, correlation and regression analysis, principal component analysis, and supervised and unsupervised linear spectral unmixing are used in these examples. Some of the advantages and limitations on the use of different types of remote sensing imagery and analysis techniques for yield mapping are also discussed.

Comparison of physically based and empirical models to estimate corn (Zea mays L) LAI from multispectral data in eastern Canada

Leaf area index (LAI) is a key input for many ecological models such as crop and carbon and nitrogen models. The LAI patterns measured in situ are time consuming and expensive and could be substituted by remote sensing technology. The objective of this study was to evaluate the possibility to map LAI of corn fields in eastern Canada using airborne multispectral imagery. Four models were locally optimized, tested, and compared. The first two methodologies relied on simple empirical relationships between LAI and the normalized difference vegetation index. The third methodology was a physically based model assuming that spectral vegetation indices are proportional to the fraction of photosynthetically active radiation absorbed by the green vegetation canopy. The last methodology relied on neural network (NN) models using different channels of the multispectral images as input. No studies combining airborne remote sensed data and NN for corn LAI simulation at regional or field scales are known by the authors. Ground measurements of LAI in five experimental sites at two dates in 2011 and 2012 were used to optimize and evaluate the models. Even though performances of the standard methods were improved by local optimisation, an NN model built with the near-infrared and red channels provided more accurate LAI simulations. All models performed better at LAI values below 3, but scattering at high LAI values was less pronounced with the NN model.

Spectral separability of riparian forests from small and medium-sized rivers across a latitudinal gradient using multispectral imagery

Spectral discrimination between riparian forests is a challenging issue due to the inherent complexity of species composition and the high spatial structural variability of these vegetation types. This study aimed to evaluate spectral separability among riparian forests, in small and medium-sized river catchment areas, in three bioclimatic zones of Portugal (temperate, transitional, and Mediterranean). We also assess the spectral differences using only the dominant riparian woody species in each riparian forest class, namely Alnus glutinosa, Salix salviifolia, and Nerium oleander. Pixel values were extracted from high-resolution airborne multispectral imagery (red, green, blue, and near-infrared, 50 cm pixels) of 26 riparian forests located in the three bioclimatic zones. Spectral separability was calculated using the transformed divergence (TD) distance. Discriminant analysis (DA) was used to select the bands that contribute most to the spectral separability and for the classification accuracy assessment of the riparian forests. Species composition and percentage of canopy closure were collected for all the riparian forests in a field campaign and subjected to hierarchical clustering in order to validate the spectral separability analyses. Optical traits derived from field data were used to interpret the spectral differences between riparian forest classes. The greatest spectral separability was observed between the temperate and the Mediterranean riparian forest classes. Global classification accuracy for the DA was 86.3% for riparian forest classes along medium-sized rivers and 70.1% in small-sized ones. The high floristic and spatial structure variability was responsible for the misclassification errors that occurred between the transitional and the other riparian forest classes. The spectral separability using only the dominant species was greater than that obtained using the overall species assemblages of the riparian forests. Alnus glutinosa had the highest level of classification accuracy, and this may be related to its peculiar yellowish-green tone. DA also revealed that all spectral bands were needed in order to distinguish the riparian forest classes. This study provided evidence that the spectral discrimination of riparian forests can be explained on the basis of differences in species composition and cover, and by a convergence of optical traits, at both leaf and canopy levels. Spectral signatures of these riparian forests and related spectral signatures of key species are useful tools for evaluating the floristic deviations of actual riparian forests from their near-natural benchmarks.

Estimating radiation interception in an olive orchard using physical models and multispectral airborne imagery

This study was conducted to estimate the fraction of Intercepted Photosinthetically Active Radiation (fIPAR) in an olive orchard. The method proposed to estimate fIPAR in olive canopies consisted of a coupled radiative transfer model that linked the 3D Forest Light Interaction Model (FLIGHT) and the Orchard Radiation Interception Model (ORIM). This method was used to assess the estimation of instantaneous fIPAR as a function of planting grids, percentage cover, and soil effects. The linked model was tested against field measurements of fIPAR acquired for a commercial olive orchard, where study plots showing a gradient in the canopy structure and percentage cover were selected. High-resolution airborne multispectral imagery was acquired at 10 nm bandwidth and 15-cm spatial resolution, and the reflectance used to calculate vegetation indices from each study site. In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR on typical olive orchard planting patterns. Input parameters used for the canopy model, such as the leaf and soil optical properties, the architecture of the canopy, and sun geometry, were studied in order to assess the effect of these inputs on the Normalized Difference Vegetation Index (NDVI) and fIPAR relationships. FLIGHT and ORIM models were independently assessed for fIPAR estimation using structural and ceptometer field data collected from each study site, yielding RMSE values of 0.1 for the FLIGHT model, while the specific olive simulation model by ORIM yielded lower errors (RMSE = 0.05). The reflectance simulations conducted as a function of the orchard architecture confirmed the usefulness of the modeling methods for this heterogeneous olive crop, and the high sensitivity of the NDVI and fIPAR to background, percentage cover, and sun geometry on these heterogeneous orchard canopies. The fIPAR estimations obtained from the airborne imagery through predictive relationships yielded RMSE error values of 0.11 when using FLIGHT to simulate both the canopy reflectance and the fIPAR of the study sites. The coupled FLIGHT+ORIM model yielded better results, obtaining RMSE = 0.05 when using airborne remote sensing imagery to estimate fIPAR.

Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV

This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling-up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10 nm bandwidth and 150 mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0.03 (visible region) and below 0.05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0.08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling-up and model inversion methods conducted with the 3D model yielded RMSE error values below 0.09 for the scaling-up approach, and below 0.10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes.

Remote sensing of geomorphological and ecological change in response to saltmarsh managed realignment, The Wash, UK

An integrated remote sensing approach quantified saltmarsh dynamics in response to a sudden change in surface elevation due to a saltmarsh restoration scheme. The biogeomorphological relationship between surface elevation and saltmarsh presence occurs over the long-term so can be difficult to observe, though the ‘managed realignment’ of coastal defences provides a unique experimental opportunity to study this relationship. Realignment at Freiston Shore, Lincolnshire, UK in August 2002 caused a sudden and high-magnitude sediment input into the local coastal system, significantly increasing the intertidal surface elevation. The resulting impacts on the external, fronting saltmarsh were quantified by aerial photography and airborne multispectral imagery. Algal and pioneer saltmarsh boundary positions were calculated from 1984 to 2006, with the latter zone migrating slowly seaward pre-realignment (3.8ma−1), but advancing significantly post-realignment (21.3ma−1). Classification of five-year multispectral imagery accurately showed subtle changes in vegetation community composition within these boundaries. The realignment site was also colonized rapidly compared to other restoration sites, due to its high starting surface elevation. This study shows how, with sufficient sediment input and accommodation space, coastal management decisions can release intertidal surfaces from physical constraints to saltmarsh colonization.

Simultaneously acquired airborne laser scanning and multispectral imagery for individual tree species identification

The objective of this study was to investigate the use of multispectral imagery in addition to measurements from airborne laser scanning (ALS) for tree species identification. Multispectral imagery from a medium-format digital frame camera acquired simultaneously with ALS data were utilized and compared with imagery from a large-format digital frame camera acquired on a separate flight mission from a higher altitude. The two acquisitions represent cost efficient methods for data collection of both three-dimensional and spectral information. The classification accuracy was assessed using 1520 segmented spruce, pine, and deciduous trees. Furthermore, ALS intensity was normalized using the range from sensor to the target (range normalization). In addition, a source of variation in intensity known as banding, is described together with a normalization procedure for diminishing this effect. The normalized intensity was better than using the raw intensity, but it did not improve the classification compared with using only ALS structural information, which provided overall classification accuracies of 74%–77%. The combined use of ALS and multispectral imagery from the medium-format imagery acquired simultaneously and the separate acquisition of large-format imagery provided overall accuracies of 87%–89% and 83%–85%, respectively. Simultaneous acquisition of ALS and medium-format digital imagery provides an efficient data acquisition strategy for tree species identification in forest inventory and will likely reduce data acquisition costs by 10%–20%.

Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology

Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.? Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.? This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.? Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.? Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.? Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.? Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields. ?Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.? Compared with traditional visual observations and ground measurements, remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions. Keywords: remote sensing, cotton defoliation, regrowth control, root rot, reflectance spectrum, airborne multispectral imagery, airborne hyperspectral imagery DOI: 10.3965/j.issn.1934-6344.2011.04.001-011 ? Citation: Yang C, Greenberg S M, Everitt J H, Fernandez C J. ?Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology. ?Int J Agric & Biol Eng, 2011; 4(4): 1

Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology

Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.? Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.? This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.? Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.? Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.? Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.? Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields. ?Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.? Compared with traditional visual observations and ground measurements, remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions. Keywords: remote sensing, cotton defoliation, regrowth control, root rot, reflectance spectrum, airborne multispectral imagery, airborne hyperspectral imagery DOI: 10.3965/j.issn.1934-6344.2011.04.001-011 ? Citation: Yang C, Greenberg S M, Everitt J H, Fernandez C J. ?Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology. ?Int J Agric & Biol Eng, 2011; 4(4): 1

Application of airborne LiDAR data and airborne multispectral imagery to structural mapping of the upper section of the Troodos ophiolite, Cyprus

Structural maps are traditionally produced by mapping features such as faults, folds, fabrics, fractures and joints in the field. However, large map areas and the spatially limited ground perspective of the field geologist can potentially increase the likelihood that not all structural features will be identified within a given area. The ability to recognise and map both local and regional structural features using high-resolution remote sensing data provides an opportunity to complement field-based mapping to help generate more comprehensive structural maps. Nonetheless, vegetation cover can adversely affect the extraction of structural information from remotely sensed data as it can mask the appearance of subtle spectral and geomorphological features that correspond to geological structures. This study investigates the utility of airborne Light Detection And Ranging (LiDAR) data and airborne multispectral imagery for detailed structural mapping in vegetated ophiolitic rocks and sedimentary cover of a section of the northern Troodos ophiolite, Cyprus. Visual enhancement techniques were applied to a 4-m airborne LiDAR digital terrain model and 4-m airborne multispectral imagery to assist the generation of structural lineament maps. Despite widespread vegetation cover, dykes and faults were recognisable as lineaments in both data sets, and the predominant strike trends of lineaments in all resulting maps were found to be in agreement with field-based structural data. Interestingly, prior to fieldwork, most lineaments were assumed to be faults, but were ground-verified as dykes instead, emphasising the importance of ground-truthing. Dyke and fault trends documented in this study define a pervasive structural fabric in the upper Troodos ophiolite that reflects the original sea-floor spreading history in the Larnaca graben. This structural fabric has not previously been observed in such detail and is likely to be continuous in adjacent regions under sedimentary cover. This information may be useful to future exploration efforts in the region focused on identification of structurally controlled mineral and groundwater resources. Overall, our case study highlights the efficacy of airborne LiDAR data and airborne multispectral imagery for extracting detailed and accurate structural information in hard-rock terrain to help complement field-based mapping.