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Wang, Dongdong. "Improving satellite Leaf Area Index estimation based on various integration methods". College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9872.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2009.Thesis research directed by: Dept. of Geography. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Winkler, Tobias. "Empirical models for grape vine leaf area estimation on cv. Trincadeira". Master's thesis, ISA-UL, 2016. http://hdl.handle.net/10400.5/13008.
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Mestrado Vinifera Euromaster - Viticulture and Enology - Instituto Superior de Agronomia - UL / Institut National D'Etudes Superieures Agronomiques de MontpellierEstimating a Vineyard’s leaf area is of great importance when evaluating the productive and quality potential of a vineyard and for characterizing the light and thermal microenvironments of grapevine plants. The aim of the present work was to validate the Lopes and Pinto method for determining vineyard leaf area in the vineyards of Lisbon’s wine growing region in Portugal, with the typical local red grape cultivar Trincadeira, and to improve prediction quality by providing cultivar specific models. The presented models are based on independent datasets of two consecutive years 2015 and 2016. Fruiting shoots were collected and analyzed during all phenological stages. Primary leaf area of shoots is estimated by models using a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves, as presented by Lopes and Pinto (2005). Lateral Leaf area additionally uses the area of the biggest lateral leaf as predictor. Models based on Shoot length and shoot diameter and number of lateral leaves were tested as less laborious alternatives. Although very fast and easy to assess, models based on shoot length and diameter were not able to predict variability of lateral leaf area sufficiently and were susceptible to canopy management. The Lopes and Pinto method is able to explain a very high proportion of variability, both in primary and lateral leaf area, independently of the phenological stage, as well as before and after trimming. They are inexpensive, universal, practical, non-destructive methods which do not require specialized staff or expensive equipment
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Phinopoulos, Victoras Georgios. "Estimation of leaf area in grapevine cv. Syrah using empirical models". Master's thesis, ISA/UL, 2014. http://hdl.handle.net/10400.5/8631.
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Mestrado Vinifera EuroMaster - Instituto Superior de AgronomiaEmpirical models for the estimation of the Area of single Primary and Lateral leaves, and total Primary and Lateral Leaf Area of a shoot, are presented for the grapevine cv. Syrah (Vitis vinifera L.). The Area of single Leaves is estimated with models using the sum of the lengths of the two lateral veins of each leaf, with logarithmic transformation of both variables. Separate models are proposed for Primary and Lateral Leaves. Models based on the Lopes and Pinto (2005) method, using Mean Leaf Area multiplied by the number of Leaves as predictors, are proposed for the estimation for Total Primary and Lateral Leaf Area. It is suggested, that failure to locate the Largest Leaf of a Primary or Lateral shoot, would not significantly impair the accuracy of the models. All models explain a very high proportion of variability in Leaf Area and they can by applied in research and viticulture for the frequent estimation of Leaf Area in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialised staff or expensive equipment
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Fang, Hongliang. "Improving the estimation of leaf area index from multispectral remotely sensed data". College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/304.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2003.Thesis research directed by: Geography. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Pacheco, Anna. "Contribution of hyperspectral remote sensing to the estimation of leaf area index in the context of precision agriculture". Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26734.
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The estimation of Leaf Area Index (LAI) is a key parameter controlling biophysical processes of the vegetation canopy, and ultimately yield. Defined as one half the total green leaf area per unit ground surface area, LAI is an essential component of precision crop management. Direct field techniques are tedious, time-consuming and labour-intensive. Indirect techniques, such as determining gap fraction with optical instruments have proven to be a good alternative, but their use is limited to rigid field sampling techniques. Vegetation indices have been useful to estimate LAI but are limited mostly due to its background reflectance noise. LAI can be estimated using different types of data, but only hyperspectral remote sensing has the potential to distinguish effectively the crop from other field components using spectral mixture analysis. Once the crop fraction has been derived, LAI is estimated using a crop fraction inversion technique. The application of this technique under agricultural field conditions has been very limited and not rigorously validated. The main objective of this study is to validate the crop fraction inversion technique for LAI estimation, and to examine the potential for LAI estimation using hyperspectral remote sensing data in the context of precision agriculture. This research will provide a unique scientific contribution to the field of hyperspectral remote sensing and greatly contribute to the advancement of remote sensing agriculture applications in Canada. (Abstract shortened by UMI.)
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Banskota, Asim. "The discrete wavelet transform as a precursor to leaf area index estimation and species classification using airborne hyperspectral data". Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39188.
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The need for an efficient dimensionality reduction technique has remained a critical challenge for effective analysis of hyperspectral data for vegetation applications. Discrete wavelet transform (DWT), through multiresolution analysis, offers oppurtunities both to reduce dimension and convey information at multiple spectral scales. In this study, we investigated the utility of the Haar DWT for AVIRIS hyperspectral data analysis in three different applications (1) classification of three pine species (Pinus spp.), (2) estimation of leaf area index (LAI) using an empirically-based model, and (3) estimation of LAI using a physically-based model. For pine species classification, different sets of Haar wavelet features were compared to each other and to calibrated radiance. The Haar coefficients selected by stepwise discriminant analysis provided better classification accuracy (74.2%) than the original radiance (66.7%). For empirically-based LAI estimation, the models using the Haar coefficients explained the most variance in observed LAI for both deciduous plots (cross validation R2 (CV-R2) = 0.79 for wavelet features vs. CV-R2 = 0.69 for spectral bands) and all plots combined (CV R2 = 0.71 for wavelet features vs. CV-R2 = 0.50 for spectral bands). For physically-based LAI estimation, a look-up-table (LUT) was constructed by a radiative transfer model, DART, using a three-stage approach developed in this study. The approach involved comparison between preliminary LUT reflectances and image spectra to find
the optimal set of parameter combinations and input increments. The LUT-based inversion was performed with three different datasets, the original reflectance bands, the full set of the wavelet extracted features, and the two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R2 = 0.77) than the original spectral bands (RMSE = 0.69, R2 = 0.42). This study has demonstrated that the application of the discrete wavelet transform can provide more accurate species discrimination within the same genus than the original hyperspectral bands and can improve the accuracy of LAI estimates from both empirically- and physically-based models.Ph. D.
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Kandasamy, Sivasathivel. "Leaf Area Index (LAI) monitoring at global scale : improved definition, continuity and consistency of LAI estimates from kilometric satellite observations". Phd thesis, Université d'Avignon, 2013. http://tel.archives-ouvertes.fr/tel-00967319.
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Monitoring biophysical variables at a global scale over long time periods is vital to address the climatechange and food security challenges. Leaf Area Index (LAI) is a structure variable giving a measure of the canopysurface for radiation interception and canopy-atmosphere interactions. LAI is an important variable in manyecosystem models and it has been recognized as an Essential Climate Variable. This thesis aims to provide globaland continuous estimates of LAI from satellite observations in near-real time according to user requirements to beused for diagnostic and prognostic evaluations of vegetation state and functioning. There are already someavailable LAI products which show however some important discrepancies in terms of magnitude and somelimitations in terms of continuity and consistency. This thesis addresses these important issues. First, the nature ofthe LAI estimated from these satellite observations was investigated to address the existing differences in thedefinition of products. Then, different temporal smoothing and gap filling methods were analyzed to reduce noiseand discontinuities in the time series mainly due to cloud cover. Finally, different methods for near real timeestimation of LAI were evaluated. Such comparison assessment as a function of the level of noise and gaps werelacking for LAI.Results achieved within the first part of the thesis show that the effective LAI is more accurately retrievedfrom satellite data than the actual LAI due to leaf clumping in the canopies. Further, the study has demonstratedthat multi-view observations provide only marginal improvements on LAI retrieval. The study also found that foroptimal retrievals the size of the uncertainty envelope over a set of possible solutions to be approximately equal tothat in the reflectance measurements. The results achieved in the second part of the thesis found the method withlocally adaptive temporal window, depending on amount of available observations and Climatology as backgroundestimation to be more robust to noise and missing data for smoothing, gap-filling and near real time estimationswith satellite time series.
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Mazumdar, Deepayan Dutta. "Multiangular crop differentiation and LAI estimation using PROSAIL model inversion". Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2011, 2011. http://hdl.handle.net/10133/3103.
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Understanding variations in remote sensing data with illumination and sensor angle changes is important in agricultural crop monitoring. This research investigated field bidirectional reflectance factor (BRF) in crop differentiation and PROSAIL leaf area index (LAI) estimation. BRF and LAI data were collected for planophile and erectophile crops at three growth stages. In the solar principal plane, BRF differed optimally at 860 nm 60 days after planting (DAP) for canola and pea, at 860 nm 45 and 60 DAP for wheat and barley, and at 860 nm and 670 nm 45 and 60 DAP for planophiles versus erectophiles. The field BRF data helped better understand PROSAIL LAI estimation. NDVI was preferred for estimating LAI, however the MTVI2 vegetation index showed high sensitivity to view angles, particularly for erectophiles. The hotspot was important for crop differentiation and LAI. Availability of more along-track, off-nadir looking spaceborne sensors was recommended for agricultural crop monitoring.xiii, 161 leaves : ill., map ; 29 cm
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Soma, Maxime. "Estimation de la distribution spatiale de surface et de biomasse foliaires de couverts forestiers méditerranéens à partir de nuages de points acquis par un LIDAR terrestre". Thesis, Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0111.
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Pour mieux comprendre le fonctionnement des écosystèmes forestiers à échelle fine, les modèles écophysiologiques cherchent à intégrer les flux d’énergie et de matière. Ces échanges dépendent de la distribution de la végétation. Leur modélisation nécessite donc une description de la structure de la végétation en trois dimensions (3D) à un niveau de détail que seule la télédétection peut produire à haut débit. Les LiDAR terrestre (Light Detection And Ranging) possèdent un fort potentiel pour caractériser en 3D la végétation au sein des canopées. De précédents travaux qui relient la densité de points à la quantité de végétation ont montré des résultats prometteurs. Cette thèse développe ces approches en explorant les diverses sources d’erreurs. Les biais systématiques sont corrigés à l’échelle de la branche, de l’arbre et de la placette. Ce travail s’appuie à la fois sur des travaux théoriques et expérimentaux. Nous avons d’abord évalué des estimateurs théoriques sur des branches. Sur cette végétation réelle, les estimateurs se sont révélés sensibles à la taille de voxel utilisée et à la distance de mesure. Les corrections apportées sont demeurées robustes sur des arbres entiers. Cependant, l’échantillonnage au LiDAR terrestre est limité par l’occlusion végétale. Un travail spécifique a été conduit pour optimiser les estimations en tirant avantage des corrélations spatiales présentes dans la végétation. Cette approche permet de limiter les sous-estimations systématiques liées à l’occlusion. L’ensemble des outils présentés offrent la possibilité de dresser des cartes de végétation à l’échelle de la placette en fournissant des estimateurs non biaisés de la surface foliaireTo better understand functioning of forest ecosystems at fine scale, ecophysiological model attempt to include energy and material fluxes. Such exchanges depend on the distribution of vegetation. Hence, these models require a tridimensional (3D) description of vegetation structure, at a level of detail which can only be retrieve with remote sensing at large scale. Terrestrial LiDAR (Light Detection And Ranging) have a great potential to provide 3D description of vegetation elements in canopy. Previous studies established promising relations between the point density and quantity of vegetation. This work develop these statistical methods, focusing on source of errors. Systematic biases are corrected at branch, tree and plot scales. This study relies on both numerical simulations and field experiments. First, we test estimators on branches in laboratory conditions. On this natural vegetation, estimators are sensitive to voxel size and distance from instrument with phase-shift LiDAR. Developed corrections from this branch experiment are valid at tree scale. However, difficulties arising from sampling limitations due to occlusion and instrument sampling pattern cause negative biases in dense areas. Specific investigations are conducted to identify source of errors and to optimize multiscan estimations. A statistical method called LAD-kriging, based on spatial correlation within vegetation, improves local accuracy of estimations and limits underestimations due to occlusion. The tools produced in this work allow to map vegetation at plot scale by providing unbiased estimator of leaf area. Some of these tools are currently implemented within open access Computree software
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Pinjuv, Guy L. "Hybrid forest modelling of Pinus Radiata D. Don in Canterbury, New Zealand". Thesis, University of Canterbury. New Zealand School of Forestry, 2006. http://hdl.handle.net/10092/1102.
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During this study two models were developed to predict growth of Pinus radiata D.Don plantations in Canterbury, New Zealand. The first, CanSPBL(1.2), is a model for whole rotations of stands owned by Selwyn Plantation Limited in Canterbury. The second model, CanSPBL(water) is a hybrid growth model for the Selwyn estate in Canterbury that incorporates an index of root zone water balance over the simulation period. An existing stand growth and yield model CanSPBL was examined using a validation dataset of PSP measurements that were not used in model fitting. Projection bias was shown for mean top height, basal area per hectare, and residual stand stocking particularly for stands at elevations exceeding 450 metres. The new model, CanSPBL(1.2) showed an increase in precision of 4 - 46% over CanSPBL(1.0) at a stand level. The components of the stand model include mean top height, basal area per hectare, stems per hectare, and diameter distribution. The mortality model was made in conjunction with managers at CanSPBL to exclude catastrophic mortality events from model projections. Data used for model fitting was filtered using a mortality index based on the -3/2 power law. An examination of this model with an independent dataset showed little apparent bias. The new model, CanSPBL(water) was developed to include an index of water balance over the simulation period. Water balance estimates were made using a sub model for root zone water balance included in the hybrid physiological model 3-PG (Landsberg and Waring, 1997). The new model showed an increase in precision of 1 - 4% over CanSPBL(1.2) at a stand level (with the exception of the model for maximum diameter which showed a decrease in precision of 0.78%) using climatic inputs that included yearly variation. However the model showed increases of precision from 0.5 to 8% (with the exception of maximum diameter again, showing a decrease in precision of 0.13%) using long term monthly average climatic inputs. The components of the stand model also include mean top height, basal area per hectare, stems per hectare, and diameter distribution. The mortality model was also fitted with a data set filtered using a mortality severity index based on the -3/2 power law to exclude catastrophic mortality events. An examination of this model with an independent dataset showed little apparent bias. Two models to predict a one sided canopy leaf area index (LAI) of radiata pine stands in the Canterbury Plains of New Zealand were also developed. The models were fitted using non-linear least squares regression of LAI estimates against stem measurements and stand characteristics. LAI estimates were derived from digital analysis of fisheye lens photography. The models were kept simple to avoid computational circularity for physiological modelling applications. This study included an objective comparison and validation of a range of model types. The models CANTY (Goulding, 1995), CanSPBL(1.2) (Pinjuv, 2005), CanSPBL-water (Pinjuv, 2005), and 3-PG (Landsberg and Waring, 1997) were compared and validated with the main criteria for comparison being each model s ability to match actual historical measurements of forest growth in an independent data set. Overall, the models CanSPBL(water), and CanSPBL(1.2) performed the best in terms of basal area and mean top height prediction. Both models CanSPBL(water), and CanSPBL(1.2) showed a slightly worse fit in predictions of stocking than did the model CANTY. The hybrid model 3PG showed a better fit for the prediction of basal area than the statistically based model CANTY, but showed a worse fit for the prediction of final stocking than all other models. In terms of distribution of residuals, CanSPBL(1.2) had overall the lowest skewness, kurtosis, and all model parameters tested significant for normality. 3PG performed the worst on average, in terms of the distribution of residuals, and all models tested positively for the normality of residual distribution.
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Hu, Ronghai. "Estimation cohérente de l'indice de surface foliaire en utilisant des données terrestres et aéroportées". Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAD021/document.
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L’indice de surface foliaire (Leaf Area Index, LAI), défini comme la moitié de la surface foliaire par unité de surface de sol, est un paramètre clé du cycle écologique de la Terre, et sa précision d'acquisition a toujours la nécessité et la possibilité d'amélioration. La technologie du scanner laser actif offre une possibilité d'obtention cohérente du LAI à plusieurs échelles, car le scanner laser terrestre et le scanner laser aéroporté fonctionnent sur le même mécanisme physique. Cependant, les informations tridimensionnelles du scanner laser ne sont pas complètement explorées dans les méthodes actuelles et les théories traditionnelles ont besoin d'adaptation. Dans cette thèse, le modèle de distribution de longueur de trajet est introduit pour corriger l'effet d’agrégation, et il est appliqué aux données du scanner laser terrestre et du scanner laser aéroporté. La méthode d'obtention de la distribution de longueur de trajet de différentes plates-formes est étudiée et le modèle de récupération cohérent est établi. Cette méthode permet d’améliorer la mesure du LAI des arbres individuels dans les zones urbaines et la cartographie LAI dans les forêts naturelles, et ses résultats sont cohérents à différentes échelles. Le modèle devrait faciliter la détermination cohérente de l'indice de surface foliaire des forêts à l'aide de données au sol et aéroportéesLeaf Area Index (LAI), defined as one half of the total leaf area per unit ground surface area, is a key parameter of vegetation structure for modeling Earth's ecological cycle and its acquisition accuracy always has the need and opportunity for improvement. Active laser scanning provides an opportunity for consistent LAI retrieval at multiple scales because terrestrial laser scanning (TLS) and airborne laser scanning (ALS) have the similar physical mechanism. However, the three-dimensional information of laser scanning is not fully explored in current methods and the traditional theories require adaptation. In this thesis, the path length distribution model is proposed to model the clumping effect, and it is applied to the TLS and ALS data. The method of obtaining the path length distribution of different platforms is studied, and the consistent retrieval model is established. This method is found to improve the individual tree measurement in urban areas and LAI mapping in natural forest, and its results at consistent at different scales. The model is expected to facilitate the consistent retrieval of the forest leaf area index using ground and airborne data
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Jiang, Jingyi. "Retrieving leaf and canopy characteristics from their radiative properties using physically based models : from laboratory to satellite observations Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model a model of leaf optical properties accounting for the differences between upper and lower faces Speeding up 3D radiative transfer simulations: a physically based approximation of canopy reflectance dependency on wavelength, leaf biochemical composition and soil reflectance Effective GAI for crops is best estimated from reflectance observations as compared to GAI and LAI Optimal learning for GAI and chlorophyll estimation from 1D and 3D radiative transfer model inversion: the case of wheat and maize crops observed by Sentinel2". Thesis, Avignon, 2019. http://www.theses.fr/2019AVIG0708.
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La mesure des caractéristiques des feuilles et du couvert végétal par télédétection est un moyen efficace et non destructif d’effectuer un suivi des cultures, que ce soit pour la prise de décision dans la gestion d’itinéraires techniques an agriculture de précision ou pour le phénotypage au champ pour améliorer l'efficacité de la sélection variétale. Grâce à l’augmentation de la puissance de calcul des machines et à la disponibilité croissante d'images à haute résolution spatiale, les méthodes d’estimation peuvent maintenant bénéficier de simulations plus précises des modèles de transfert radiatif (RT) dans la végétation. L'objectif de ce travail est de proposer et d'évaluer des moyens efficaces pour estimer les caractéristiques des feuilles et du couvert végétal à partir d'observations rapprochées ou de télédétection en utilisant des modèles RT basés sur une description réaliste de la structure des feuilles et du couvert. Au niveau des feuilles, nous avons d'abord évalué la capacité des différentes versions du modèle PROSPECT à estimer des variables biochimiques comme la chlorophylle (Cab), la teneur en eau et en matière sèche. Nous avons ensuite proposé le modèle FASPECT pour décrire les différences de propriétés optiques entre les faces supérieure et inférieure des feuilles en considérant un système à quatre couches. Après avoir étalonné les coefficients d'absorption spécifiques des principaux constituants de la feuille, nous avons validé FASPECT sur 8 jeux de données. Nous avons montré que les spectres de réflectance et de transmittance des deux faces sont simulés avec une très bonne précision, et même meilleure que PROSPECT pour la face supérieure. De même, en mode inverse, les performances d'estimation de la teneur en matière sèche sont considérablement améliorées avec FASPECT par rapport à PROSPECT, et restent du même ordre de grandeur pour la chlorophylle et l’eau. Au niveau du couvert végétal, nous avons utilisé le simulateur de rendu physique réaliste LuxCoreRender pour calculer le transfert radiatif à partir d'une description 3D de l’architecture de la culture. Nous avons d’abord vérifié ses bonnes performances par comparaison aux modèles 3D les plus récents en utilisant ROMC (RAMI On Line Model Checker). Afin d’accélérer les simulations, nous avons développé une méthode qui repose sur l’utilisation d’un nombre limité de propriétés optiques du sol et des feuilles. Pour estimer les variables d'état du couvert végétal (indice de surface verte, GAI, contenu en chlorophylle du couvert (CCC) ou des feuilles (Cab), nous avons ensuite entrainé des algorithmes d’apprentissage automatique à partir de bases de données « culture spécifique » simulées avec LuxCoreRender pour le blé et le maïs et d’une base de données générique simulée avec le modèle 1D PROSAIL de transfert radiatif. Les résultats sur des simulations et sur des données in situ combinés aux images SENTINEL2 ont montré que les algorithmes spécifiques aux cultures surpassent les algorithmes génériques pour les trois variables, en particulier lorsque la structure du couvert s’éloigne de l'hypothèse 1D du milieu turbide, comme dans le cas du maïs où la structure en rang domine pendant toute une partie de la saison de croissanceMeasuring leaf and canopy characteristics from remote sensing acquisitions is an effective and non destructive way to monitor crops both for decision making within the smart agriculture practices or for phenotyping under field conditions to improve the selection efficiency. With the advancement of computer computing power and the increasing availability of high spatial resolution images, retrieval methods can now benefit from more accurate simulations of the Radiative Transfer (RT) models within the vegetation. The objective of this work is to propose and evaluate efficient ways to retrieve leaf and canopy characteristics from close and remote sensing observations by using RT models based on a realistic description of the leaf and canopy structures. At the leaf level, we first evaluated the ability of the different versions of the PROSPECT model to estimate biochemical variables like chlorophyll (Cab), water and dry matter content. We then proposed the FASPECT model to describe the optical properties differences between the upper and lower leaf faces by considering a four-layer system. After calibrating the specific absorption coefficients of the main absorbing material, we validated FASPECT against eight measured ground datasets. We showed that FASPECT simulates accurately the reflectance and transmittance spectra of the two faces and overperforms PROSPECT for the upper face measurements. Moreover, in the inverse mode, the dry matter content estimation is significantly improved with FASPECT as compared to PROSPECT. At the canopy level, we used the physically based and unbiased rendering engine, LuxCoreRender to compute the radiative transfer from a realistic 3D description of the crop structure. We checked its good performances by comparison with the state of the art 3D RT models using the RAMI online model checker. Then, we designed a speed-up method to simulate canopy reflectance from a limited number of soil and leaf optical properties. Based on crop specific databases simulated from LuxCoreRender for wheat and maize and crop generic databases simulated from a 1D RT model, we trained some machine learning inversion algorithms to retrieve canopy state variables like Green Area Index GAI, Cab and Canopy Chlorophyll Content (CCC). Results on both simulations and in situ data combined with SENTINEL2 images showed that crop specific algorithms outperform the generic one for the three variables, especially when the canopy structure breaks the 1D turbid medium assumption such as in maize where rows are dominant during a significant part of the growing season
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Ebodaghe, Denis Abumere. "Estimating daily green leaf area index for corn in Virginia". Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/74731.
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A model to predict the daily green leaf area index (GLAI) for corn has been developed for Indiana conditions. Using daily maximum and minimum temperatures the GLAI was predicted for the vegetative stage, reproductive and grain filling stage, and the leaf senescing stage of corn. Predictions of GLAI for corn can be made on a daily basis from the day corn is planted until it is harvested for grain.
The GLAI model was tested under Virginia conditions using green leaf area measurements collected from corn plants grown on Davidson silty clay loam, Davidson silty clay, and Mayodan sandy loam soils in the Piedmont region of the State. Maximum and minimum temperature data were also collected at the three sites. Measurements were made for two growing seasons using corn hybrid Pioneer 3369A, three plant population densities and two irrigation schedules. Short duration temperature data were also collected to compare with the daily maximum and minimum temperature data for the Mayodan site. Also a combination of soil temperature at 10 cm depth and air temperatures were used for the temperature functions accumulated from date of planting at the Mayodan site.
Results of this study show that the predicted and measured GLAI values compare favorably under irrigated conditions on the Davidson soil. The results were not as favorable on the irrigated corn on the Mayodan soil. When the corn is subjected to severe moisture stress on either soil, GLAI cannot be predicted with this model. Short duration temperature data resulted in a better prediction of GLAI on the Mayodan soil. When applying nitrogen fertilizer to the corn through the irrigation system through the grain filling stage, the measured GLAI values compared favorably with the predicted GLAI values. However, the application of nitrogen and sulfur fertilizer together resulted in GLAI being maintained above that predicted for a longer period of time during the grain filling stage before its decline.Ph. D.
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Mohammadi, Vahid. "Design, Development and Evaluation of a System for the Detection of Aerial Parts and Measurement of Growth Indices of Bell Pepper Plant Based on Stereo and Multispectral Imaging". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2022. http://www.theses.fr/2022UBFCK109.
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Au cours de la croissance des plantes, leur suivi apporte beaucoup d'avantages aux producteurs. Cette surveillance comprend la mesure des propriétés physiques, le comptage des feuilles des plantes, la détection des plantes et leur séparation des mauvaises herbes. Toutes ces techniques peuvent être réalisées de différentes manières, cependant, les techniques favorables sont non destructives car la plante est une créature très sensible que toute manipulation peut perturber sa croissance ou entraîner la perte de feuilles ou de branches. Les techniques d'imagerie sont les meilleures solutions pour le suivi de la croissance des plantes et les mesures géométriques. À cet égard, dans ce projet, l'utilisation de l'imagerie stéréo et des données multispectrales a été étudiée. L'imagerie stéréo active et passive a été utilisée pour l'estimation des propriétés physiques et le comptage des feuilles et des données multispectrales ont été utilisées pour la séparation des cultures et des mauvaises herbes. La plante de poivron a été utilisée pour des mesures d'imagerie pendant une période de 30 jours et pour la séparation culture/mauvaise herbe, les réponses spectrales du poivron et de cinq mauvaises herbes ont été mesurées. Neuf propriétés physiques des feuilles de poivre (c. Le système stéréo était composé de deux caméras LogiTech et d'un vidéoprojecteur. Tout d'abord, le système stéréo a été calibré à l'aide d'images d'échantillons d'un damier standard dans différentes positions et angles. Le système a été contrôlé à l'aide de l'ordinateur pour allumer une ligne lumineuse, enregistrer des vidéos des deux caméras pendant que la lumière est balayée sur la plante, puis arrêter la lumière. Les cadres ont été extraits et traités. L'algorithme de traitement a d'abord filtré les images pour supprimer le bruit, puis a seuillé les pixels indésirables de l'environnement. Ensuite, en utilisant la méthode de détection de pic du centre de masse, la partie principale et centrale de la ligne lumineuse a été extraite. Ensuite, les images ont été rectifiées en utilisant les informations d'étalonnage. Ensuite, les pixels correspondants ont été détectés et utilisés pour le développement du modèle 3D. Le nuage de points obtenu a été transformé en une surface maillée et utilisé pour la mesure des propriétés physiques. Pour les réponses spectrales des plantes, celles-ci ont été fraîchement déplacées au laboratoire, les feuilles ont été détachées des plantes et placées sur un fond sombre flou. Des lumières de type A ont été utilisées pour l'éclairage et les mesures spectrales ont été effectuées à l'aide d'un spectroradiomètre de 380 nm à 1000 nm. Pour réduire la dimensionnalité des données, l'ACP et la transformée en ondelettes ont été utilisées. Les résultats de cette étude ont montré que l'utilisation de l'imagerie stéréo peut proposer un outil bon marché et non destructif pour l'agriculture. Un avantage important de l'imagerie stéréo active est qu'elle est indépendante de la lumière et peut être utilisée pendant la nuit. Cependant, l'utilisation de la stéréo active pour le stade primaire de croissance fournit des résultats acceptables, mais après ce stade, le système sera incapable de détecter et de reconstruire toutes les feuilles et les parties de la plante. En utilisant l'ASI, les valeurs R2 de 0,978 et 0,967 ont été obtenues pour l'estimation de la surface foliaire et du périmètre, respectivement. Les résultats de la séparation des cultures et des mauvaises herbes à l'aide de données spectrales étaient très prometteurs et le classificateur, qui était basé sur un apprentissage en profondeur, pouvait complètement séparer le poivre des cinq autres mauvaises herbesDuring the growth of plants, monitoring them brings much benefits to the producers. This monitoring includes the measurement of physical properties, counting plants leaves, detection of plants and separation of them from weeds. All these can be done different techniques, however, the techniques are favorable that are non-destructive because plant is a very sensitive creature that any manipulation can put disorder in its growth or lead to losing leaves or branches. Imaging techniques are of the best solutions for plants growth monitoring and geometric measurements. In this regard, in this project the use of stereo imaging and multispectral data was studied. Active and passive stereo imaging were employed for the estimation of physical properties and counting leaves and multispectral data was utilized for the separation of crop and weed. Bell pepper plant was used for imaging measurements for a period of 30 days and for crop/weed separation, the spectral responses of bell pepper and five weeds were measured. Nine physical properties of pepper leaves (i.e. main leaf diameters, leaf area, leaf perimeter etc.) were measured using a scanner and was used as a database and also for comparing the estimated values to the actual values. The stereo system consisted of two LogiTech cameras and a video projector. First the stereo system was calibrated using sample images of a standard checkerboard in different position and angles. The system was controlled using the computer for turning a light line on, recording videos of both cameras while light is being swept on the plant and then stopping the light. The frames were extracted and processed. The processing algorithm first filtered the images for removing noise and then thresholded the unwanted pixels of environment. Then, using the peak detection method of Center of Mass the main and central part of the light line was extracted. After, the images were rectified by using the calibration information. Then the correspondent pixels were detected and used for the 3D model development. The obtained point cloud was transformed to a meshed surface and used for physical properties measurement. Passive stereo imaging was used for leaf detection and counting. For passive stereo matching six different matching algorithms and three cost functions were used and compared. For spectral responses of plants, they were freshly moved to the laboratory, leaves were detached from the plants and placed on a blur dark background. Type A lights were used for illumination and the spectral measurements were carried out using a spectroradiometer from 380 nm to 1000 nm. To reduce the dimensionality of the data, PCA and wavelet transform were used. Results of this study showed that the use of stereo imaging can propose a cheap and non-destructive tool for agriculture. An important advantage of active stereo imaging is that it is light-independent and can be used during the night. However, the use of active stereo for the primary stage of growth provides acceptable results but after that stage, the system will be unable to detect and reconstruct all leaves and plant's parts. Using ASI the R2 values of 0.978 and 0.967 were obtained for the estimation leaf area and perimeter, respectively. The results of separation of crop and weeds using spectral data were very promising and the classifier—which was based on deep learning—could completely separate pepper from other five weeds
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Bowyer, P. "Estimating leaf area index in savanna vegetation using remote sensing and inverse modelling". Thesis, University of Salford, 2005. http://usir.salford.ac.uk/2234/.
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Leaf area index (LAI), defined as the one sided green leaf area per unit ground area, is a key parameter in ecosystem process models. Owing to the large area of the earth's surface that they occupy, savanna ecosystems represent the third largest terrestrial carbon sink. There is considerable uncertainty however, as to the functioning of these ecosystems, particularly as they respond to land cover changes. Consequently, ecosystem process models constitute one of the best methods available for investigating the effect this may have on terrestrial carbon cycling. If these models are to be used over large areas however, they need to be parameterised. This thesis develops a methodology to estimate LAI in savanna ecosystems, using remotely sensed earth observation (EO) data, laboratory bidirectional reflectance measurements (BRDF), physically based canopy reflectance models (CRMs), and artificial neural networks (ANN). First, the scattering behaviour of Kalahari soils was characterised, by making laboratory BRDF measurements. Soils were shown to be highly non-Lambertian. These measurements were then used to parameterise three different CRMs. Modelled reflectances were assessed with respect to Landsat ETM+ and Terra-MODIS reflectances. Results showed that a 1-D turbid medium provided the closest fit to the measurements. A series of model sensitivity analyses (SA) were performed, and it was shown that reflectance in the red and shortwave infrared displayed greatest sensitivity to LAI, sensitivity in the near-infrared was negligible. Model inversions were performed with ANN and different waveband combinations, and LAI was estimated. The results showed that LAI could be estimated with high accuracy, an RMSE of 0.3 1, and 0.18, from ETM+ and MODIS measurements, respectively. These results were promising, and with further improvements to models, coupled with more accurate input data, will see the use of EO data play an increasingly important role in understanding the functioning of these savanna ecosystems.
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Souza, Vanessa de Arruda. "Utilização de técnicas de sensoriamento remoto para a estimativa da evapotranspiração em uma cultura de arroz irrigado". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/72454.
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A evapotranspiração (ET) é um fenômeno natural que influencia diretamente mudanças no clima local e global, possuindo grande importância hidrológica e meteorológica. Este trabalho teve como objetivo estimar a ET através do método Penman-Monteith, e comparar com os resultados estimados pelo Método da Covariância de Vórtices Turbulentos. Os dados de IAF (Índice de Área Foliar) para descrição da vegetação foram obtidos a partir do sensor MODIS e de medições em campo da Rede SulFlux. A área de estudo desta pesquisa localizou-se no município de Cachoeira do Sul-RS, em uma propriedade de cultivo de arroz irrigado. O período de estudo referiu-se a safra que estendeu-se de outubro de 2010 a março de 2011. Os resultados mostraram que o IAF e as estimativas de ET apresentam um comportamento temporal semelhante. A comparação entre os resultados das estimativas de ET, utilizando dados obtidos em campo e estimados através de sensoriamento remoto, foram satisfatórios. No entanto, o resultado que apresentou os maiores valores para a ET foi proveniente do sensor MODIS. Sendo assim, pode-se concluir que a estimativa da ET, a partir de dados de vegetação, obtidos através de técnicas de sensoriamento remoto, constituem-se como uma alternativa para os métodos de ET que utilizam dados de vegetação medidos em campo.The Evapotranspiration (ET) is a natural phenomenon that directly causes changes in the local and global climate, having a great hydrologic and meteorological importance. This work has as objective to estimate ET through the Penman-Monteith method and compare with the estimated results by the Eddy Covariance Method. The LAI (Leaf Area Index) data for the vegetation description were obtained from the MODIS sensor and from field measurements of the SulFlux network. The field of study of this research had place in the city of Cachoeira do Sul – Rio Grande do Sul state, in a irrigated rice crop property. The study period is referred to the crop that extended from October 2010 to March 2011. The results showed that the LAI and the ET estimates have a similar temporal behavior. The comparison between the results of ET estimates, using data obtained in field and estimated through remote sensing, were satisfactory. However, the result that showed the highest values for ET was from the MODIS sensor. This way, it can be concluded that the ET estimate, through vegetation data, obtained by remote sensing techniques, are an alternative for the ET methods that use vegetation field measured data.
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Peduzzi, Alicia. "Estimating forest attributes using laser scanning data and dual-band, single-pass interferometric aperture radar to improve forest management". Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39456.
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The overall objectives of this dissertation were to (1) determine whether leaf area index (LAI) (Chapter 2), as well as stem density and height to live crown (Chapter 3) can be estimated accurately in intensively managed pine plantations using small-footprint, multiple-return airborne laser scanner (lidar) data, and (2) ascertain whether leaf area index in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone or both in combination (Chapter 4). In situ measurements of LAI, mean height, height to live crown, and stem density were made on 109 (LAI) or 110 plots (all other variables) under a variety of stand conditions. Lidar distributional metrics were calculated for each plot as a whole as well as for crown density slices (newly introduced in this dissertation). These metrics were used as independent variables in best subsets regressions with LAI, number of trees, mean height to live crown, and mean height (measured in situ) as the dependent variables. The best resulting model for LAI in pine plantations had an R2 of 0.83 and a cross-validation (CV) RMSE of 0.5. The CV-RMSE for estimating number of trees on all 110 plots was 11.8 with an R2 of 0.92. Mean height to live crown was also well-predicted (R2 = 0.96, CV-RMSE = 0.8 m) with a one-variable model. In situ measurements of temperate mixed forest LAI were made on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood). GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes.
Both lidar and GeoSAR metrics were used as independent variables in best subsets regressions with LAI (measured in situ) as the dependent variable. Lidar metrics alone explained 69% of the variability in temperate mixed forest LAI, while GeoSAR metrics alone explained 52%. However, combining the LAI and GeoSAR metrics increased the R2 to 0.77 with a CV-RMSE of 0.42. Analysis of data from active sensors shows strong potential for eventual operational estimation of biophysical parameters essential to silviculture.Ph. D.
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Chiang, Yang-Sheng. "Estimating landscape level leaf area index and net primary productivity using field measurements, satellite imagery, and a 2-D ecophysiological model". Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1294241.
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This study has provided a landscape level estimate of leaf area index (LAI) and net primary productivity (NPP) for a temperate broadleaf forest ecosystem in south-central Indiana. The estimates were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products LAI and NPP from both spatial and temporal perspectives. The evidence suggests that field-based estimates were poorly correlated with global MODIS data due to the simplifying assumptions of the MODIS global applicability, saturation problems of the red reflectance in highly vegetated areas, homogeneous land cover types of the study area, and other design assumptions of the field-based estimates. To improve the localized applicability of MODIS product algorithms, an empirical and localized algorithm combining in-situ measurements, the buildup of a localized biophysical model, and remote sensing-derived data were suggested for each local-scaled ecosystem.Department of Natural Resources and Environmental Management
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Jaboinski, Fernando Roberto. "Avaliação de produtos do sensor MODIS para aplicações na estimativa de parâmetros biofísicos da cultura da soja no estado do Rio Grande do Sul". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/36964.
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Estimativas do rendimento da soja são informações importantes que podem auxiliar as instituições na tomada de decisão quanto à políticas de comercialização. São consideradas também, na liberação de recursos para o financiamento da produção e seguro agrícola. Modelos matemáticos, que se baseiam nas relações clima planta, denominados modelos agrometeorológicos, podem estimar o rendimento médio de grãos, através de dados meteorológicos e biofísicos da cultura, como exemplo: o IAF (Índice de área foliar) da soja pode ser associado ao Kc (Coeficiente de cultura) para estimar o estádio fenológico, e esta informação, ser associada à parametrização dos modelos. O objetivo geral deste trabalho foi avaliar as possibilidades de se utilizar imagens do sensor MODIS, para estimar parâmetros biofísicos da soja, aplicáveis à modelagem do rendimento de grãos. A área de estudo abrangeu a porção norte do estado do Rio Grande do Sul. Foram obtidas as imagens do IAF, MOD15A2, e dos índices de vegetação NDVI (Índice de vegetação por diferença normalizada) e NDWI (Índice de umidade por diferença normalizada), do produto MOD13Q1. Foram analisadas duas safras: 2003/04 e 2004/05. O período de safra foi de 15 de outubro até 30 de abril. Foram geradas máscaras de cultivo para as safras, e aplicadas sobre as imagens. Inicialmente, foram comparadas as médias do IAF, MOD15A2, às outras estimativas do IAF obtidas a partir de funções de relação com o NDVI, e analisados os diagramas de dispersão para cinco datas. Então foi estimado o Kc_1 com as médias do IAF, MOD15A2, conforme Martorano (2007), e comparadas ao Kc_2 ajustado conforme Matzenauer, 2002. Foi calculado o balanço hídrico meteorológico da cultura a fim de se obter o Índice de Satisfação das Necessidades de Água das plantas (ISNA), sendo ISNA_1 considerando o Kc_1, e o ISNA_2, o Kc_2. Foram obtidos os índices de correlação entre o ISNA_1 e 2 e os índices NDVI e NDWI, para o ciclo completo da cultura e para períodos de baixo ISNA. Como resultado, o IAF, MOD15A2 apresentou coerência com a evolução do IAF durante o ciclo, porém, em média, apresentou valores inferiores aos observados por Fontana et al. (1992) e Martorano (2007). Já como estimador do Kc, apresentou coeficientes de variação inferiores ao observado no Kc_2. Observou-se também que no IAF, MOD15A2 ocorriam valores superestimados do Kc entre a semeadura e o máximo desenvolvimento, e após, subestimados, o que potencializou períodos de déficit hídrico acentuado durante a floração e enchimento de grãos, em ambas as safras. Já, avaliando os índices de correlação, o ISNA_1, apresentou correlações de maior significância com os índices de vegetação do que o ISNA_2. Com isso podemos supor que, mesmo o IAF, MOD15A2 não tendo apresentado valores compatíveis com os da soja, demonstrou maior significância nas correlações, o que indica que imagens MODIS, podem gerar estimativas adequadas tanto do IAF, quanto do Kc, e também, representar adequadamente as condições hídricas. É recomendável avaliar estimativas do IAF da soja, a partir de suas relações com o NDVI, a fim de se obter resultados compatíveis com a soja.Estimates of soybean yield are useful information that can assist institutions in decisions related by commercial policies. It is considered also in financing of production and agricultural insurance. Mathematical models, which are based in clime plant relationship known as Agrometeorological Models, can provide an estimate for grain yield through meteorological and biophysical data correlated with the culture, as an example: the LAI (Leaf Area Index) of soybean can be related with the Kc (Culture's coefficient) to estimate the phenological stage, and this information, applied to model's parameters. The main objective of this work was evaluated of possibilities of MODIS's images, in estimation of biophysical parameters, which are applicable in yield's modeling for soybean. The studying area was a portion of northern of State of Rio Grande do Sul. Were obtained images from LAI, MOD15A2, and vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) from MOD13Q1 product. Were analyzed two harvests: 2003/04 and 2004/05. The length of time for harvest was 15th October to 30th April. Soybean's areas was detected and masks were applied on the images. Firstly, were compared the means between LAI, MOD15A2 and two different methodologies of LAI's estimation based on relationship with NDVI. The scatter plots were discussed between LAI, MOD15A2 and other methodologies, for five key-dates. The culture's coefficient Kc_1 was estimated through LAI, MOD15A2, according to Martorano (2007), and compare with Kc_2, which were obtained from fitted culture's coefficient by Matzenauer (2002). The water balance were calculated aiming the index for Plant's water satisfaction needs (ISNA), which, ISNA_1 has considered the Kc_1, and ISNA_2, the Kc_2. Correlation indexes were obtained between ISNA_1 and 2, and vegetation indexes NDVI and NDWI, for the whole culture's cycle and specific periods of low ISNA. As results, LAI, MOD15A2, has presented coherence with soybean's cycle progress, however, as a rule, it presents lower values, comparing to Fontana et al. (1992) and Martorano (2007). Even now as a Kc's estimator, Kc_1 has presented lower variation's coefficient compared to Kc_2. Was observed also overrated in Kc_1 between the sowing to maximum development, and after, underestimated, what provokes periods of accented hydric deficiency during flowering and grain forming stages, in both harvests. Now, as to correlation's indexes, we observed more significance correlations between both vegetation's indexes and ISNA_1. This way, these results has indicated that remote sensing images can provide accurate estimates of IAF as much as Kc, and also indicate the hydric conditions of plants. It is recommended to improve the LAI's mean values on the images, exploring the relationship with NDVI, looking for adequate values for the case of soybean.
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Zhao, Kaiguang. "Estimating forest structural characteristics with airborne lidar scanning and a near-real time profiling laser systems". [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2964.
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SILVA, Anderson Santos da. "Estimativa de produtividade da cana-de-açúcar utilizando dados agrometeorológicos e imagens do sensor MODIS". Universidade Federal Rural de Pernambuco, 2016. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5319.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq
This research is based on estimated and observed agricultural productivity in an area of commercial sugarcane production located at São Francisco’s Agroindustry – AGROVALE S.A., Juazeiro – BA, Brazilian northeast. The new yield estimation models were obtained by multiple linear regression, in which the inputs variables were: irrigation, precipitation, average air temperature, vapor saturation deficit of the air, photoperiod, normalized difference vegetation index (NDVI), leaf area index (LAI) and fractional soil cover (FC). To obtain these models, it was used the statistics program Statística version 10. Futhermore, the meteorological data were obtained from an automatic weather station located at the Farm Brasil Uvas, Juazeiro – BA such as: precipitation (mm), temperature (°C), relative humidity (%), evapotranspiration (mm), current vapor pressure (hPa) and saturation vapor pressure (hPa). The crop yield data and parameters related to crop development were obtained from AGROVALE Agriculture Department. The spectral data, NDVI, IAF and FC, were extracted from MODIS sensor images (Spectroradiometer Imager Moderate Resolution). The data used to models validation were obtained from the same sources previously mentioned. The data were analyzed by mean absolute error (DMA) and mean relative error (DMR). The comparison of yield observed and estimated values showed that the spectral agrometeorological model (SAM) presented the lower and better mean relative error (DMR) with a mean variation of 0.34 %, followed by agrometeorological model with a mean variation of 1.37 % and, finally, the spectral model presented larger mean relatives errors in comparison with other two models, showing a mean variation of 6.58%, approaching AGROVALE’s technicians estimation that presented a mean variation of 6.75%. At the validation’s model for the 2004/2005 crop year, the spectral surpassed the agrometeorological and agrometeorological spectral with average relative errors of 5.05%, while for other models the difference were 15.11% and 16.19%, reflecting a productivity of 93.05 t ha-1 versus 83.19 t ha-1 and 82.13 t ha-1 of agrometeorological and agrometeorologicalspectral models, respectively, for an observed yield of 98 t ha-1. Soon after the 2011/2012 years crop there was a planting renovation with a new variety, with different physiology and consequently a distinct productive power and, from 2013/2014 crop year, the models underestimated the productivity compared to the real. The estimate made by the technicians, based on the crop development since planting until next harvest, showed satisfactory results as well as the tested models.
Esta pesquisa baseou-se na avaliação de produtividade agrícola estimada e observada em uma área de cultivo comercial de cana-de-açúcar localizada na Agroindústria do Vale do São Francisco – AGROVALE S.A., Juazeiro – BA, sertão nordestino. Novos modelos de estimativas de produtividades foram obtidos por regressão linear múltipla utilizando-se, como variáveis de entrada: a irrigação, a precipitação, a temperatura média do ar, o déficit de saturação de vapor do ar, o fotoperíodo, o índice de vegetação por diferença normalizada (NDVI), o índice de área foliar (IAF) e a fração de cobertura do solo (FC). Para obtenção desses modelos utilizou-se o programa estatístico Statística versão 10. Além disso, os meteorológicos foram obtidos na estação meteorológica automática instalada na Fazenda Brasil Uvas, em Juazeiro – BA sendo elas: precipitação, temperatura, umidade relativa, evapotranspiração, pressão atual de vapor e pressão de saturação de vapor. Os dados de rendimento agrícola e parâmetros inerentes ao desenvolvimento da cultura foram disponibilizados pelo Departamento Agrícola da usina AGROVALE. Os dados espectrais: NDVI, IAF e FC foram extraídos de produtos derivados de imagens orbitais do sensor MODIS (Espectrorradiômetro Imageador de Resolução Moderada). Os dados para validação dos modelos também foram obtidos nas mesmas fontes citadas anteriormente. Os dados foram avaliados por meio do cálculo do erro médio absoluto e do erro médio relativo ou percentual. A comparação dos valores observados e estimados de produtividades mostra que o modelo agrometeorológico-espectral (MAE) apresentou as menores e melhores diferenças médias relativas com uma variação média de 0,34%, seguido do modelo agrometeorológico (MA) com uma variação média de 1,37% e por último o modelo espectral (ME) apresentou as maiores diferenças médias relativas, quando comparado com os outros dois modelos obtendo uma variação média de 6,58%, aproximando-se mais da estimativa feita pelos técnicos da usina que apresentou variação média de 6,75%. Na validação dos modelos para o ano-safra de 2004/2005 o espectral superou os agrometeorológico e o agrometeorológico-espectral com diferenças médias relativas na ordem de 5,05% enquanto nos demais modelos as diferenças foram de 15,11% e 16,19%, refletindo numa produtividade de 93,05 t ha-1 contra 83,19 t ha-1 e 82,13 t ha-1 dos modelos agrometeorológicos e agrometeorológico-espectral, respectivamente, para uma produtividade observada de 98 t ha-1. Logo após a safra de 2011/2012 ocorreu uma renovação de plantio com nova variedade, fisiologia diferenciada e, consequentemente, um poder produtivo distinto e a partir da safra de 2013/2014 os modelos subestimaram a produtividade quando comparadas com o real. A estimativa feita pelos técnicos da usina baseada no desenvolvimento da cultura desde o plantio até próximo da colheita, apresentou resultados satisfatórios assim como os modelos testados.
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Li-PingLin i 林莉萍. "Estimation of Forest Canopy Height Model and Leaf Area Index Using Airborne LiDAR data". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/91295838225601949138.
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碩士國立成功大學
測量及空間資訊學系碩博士班
101
Efficiently obtaining the information in forest region such as forest structure, forest ecosystems is important for forestry management. Remote sensing has been considered as a practical technology to acquire the data of a large area. Compared with spectral images, airborne light detection and ranging (LiDAR) can provide three dimensional coordinates directly, and the penetration characteristics of LiDAR system makes the possibility of seeing through the canopy. Therefore, the structure or the terrain under the canopy can be characterized by the LiDAR point cloud data. The purpose of this study is to estimate the Canopy Height Model (CHM) and the Leave Area Index (LAI) of a dense forest area by using airborne LiDAR data. CHM is estimated by taking the difference of DSM and DEM derived from LiDAR data. Estimation of LAI is achieved based on the calculation of Laser Penetration Index (LPI). Five calculations of LPI were applied in this paper: (1.) The ratio between the number of ground points and that of all the points; (2.) the ratio between the intensities of ground points and that of all the points; (3) the ratio between the number of ground points and the number of laser beams; (4) a weighting method modified from index (1); and (5) the ratio between the area of ground points and that of all the points. The study area is in a nature broadleaf forest of south Taiwan. In this study, we use three sets of airborne LiDAR data acquired with different full waveform LiDAR systems including Leica ALS60, Riegl LMS-Q680i and Optech Pegasus HD400. All of these LiDAR systems are capable of recording full waveform data, then we can get the waveform point clouds by the echo detector to do the comparison. Our experiments results show that the accuracy of CHM by different LiDAR data is about 1.5 meter. And the fourth LPI index has the highest coefficient of determination (about 0.8) and the estimation of LAI can be improved by using the waveform points.
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Pope, Graham. "LiDAR and WorldView-2 Satellite Data for Leaf Area Index Estimation in the Boreal Forest". Thesis, 2012. http://hdl.handle.net/1974/7510.
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Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers. This thesis focused on estimating LAI from: i) height and density metrics derived from Light Detection and Ranging (LiDAR); ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and iii) a combination of these two remote sensing technologies.
In situ measurements of LAI were calculated from digital hemispherical photographs (DHPs) and remotely sensed variables were derived from low density LiDAR and high resolution WorldView-2 data. Multiple Linear Regression (MLR) models were created using these variables, allowing forest-wide prediction surfaces to be created. Results from these analyses demonstrated: i) moderate explanatory power (i.e., R2 = 0.54) for LiDAR models incorporating metrics that have proven to be related to canopy structure; ii) no relationship when using SVIs; and iii) no significant improvement of LiDAR models when combining them with SVI variables.
The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with low ranges of LAI for model calibration. On the other hand, it was anticipated that traditional SVI relationships to LAI would be present with WorldView-2 data, a result that is not easily explained. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at a high spatial resolution for decision makers in the forestry community.Thesis (Master, Geography) -- Queen's University, 2012-09-24 16:18:09.96
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Wilson, Janna L. "Estimation of phenological development and fractional leaf area of canola (Brassica napus L.) from temperature". 2002. http://hdl.handle.net/1993/7805.
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Argentine Canola (Brassica napus L.) is an economically successful crop on the Canadian Prairies. The 1999 growing season had a record area seeded of 5,598,700 hectares, declining slightly to 4,894,600 hectares in 2000. Since 1997. canola has been ranked as Manitoba's most valuable agricultural commodity. Although canola is an important contributor to the Canadian economy, little research has been conducted at the field level to determine how crop phenological stage and ground cover respond to weather variables such as temperature. Such basic agronomic knowledge is essential for successful agrometeorological modeling. The objectives of this project were to develop a methodology for estimating phenological development and fractional leaf area of canola using temperature, and to further evaluate the accuracy of top-zone (10 cm depth) soil moisture modeled from a Canola Phenology and Water-Use Model. Five test sites within Agro-Manitoba were used during the 1999 growing season, while three test sites were used in 2000. Weekly field observations were conducted during the growing season to determine the phenological stage of the crop, the amount of ground cover, and near surface soil moisture. Daily maximum and minimum temperatures and rainfall data were obtained from the nearest Environment Canada weather station. The fungal infection Sclerotinia (Sclerotinia sclerotiorum (Lib.) de Bary) costs prairie canola producers approximately $260 million annually as a result of yield loss and management techniques requiring expensive fungicide applications. The current model for predicting sclerotinia stem rot on the Canadian Prairies estimates the risk of infection based on crop phenological stage and soil moisture estimates derived from the Raddatz model. However, the current model is regional in nature and is limited because crop stage is estimated using a simple growing degree-day (GDD) above 5oC, while soil moisture is modeled using a coupled atmosphere-crop-soil agrometeorological model, which utilizes the simple GDD relationship to estimate fractional leaf area. This study determined that a GDD above 5oC was an inadequate estimator of crop phenology and that the P-Days system, utilizing base, optimal, and maximum temperature thresholds of 5, 17, and 30oC, respectively was an overall better estimator of crop phenology. Further results indicated that fractional leaf area was better estimated from P-Days(5,17,30) than from GDD used in the original model. Observed and modeled top-zone soil moisture values were compared. The relatively low R2 of 0.60 suggests that poor estimates of soil moisture was linked to the use of off-site precipitation data and perhaps linked to the inaccurate estimation of fractional leaf area from GDD above 5oC.
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Burrows, Sean Nicolas Grant. "Geostatistical estimation of leaf area index and net primary production of five North American biomes". 2002. http://www.library.wisc.edu/databases/connect/dissertations.html.
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Peng, Bing-Syun, i 彭炳勳. "Estimating Tree Heights and Leaf Area Index Using Airborne LiDAR Data". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/89499830710571827263.
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碩士國立屏東科技大學
森林系所
95
Due to the development of remote sensing, the remote image data and techniques had widely using in forest resource inventory. Airborne light detection and ranging (LiDAR) is an active remote sensing technique, which sends pulses of laser light toward the ground and detects the return times of back-scattered energy in order to determine ranges of the surface. The suitability of LiDAR in forestry is demonstrated by application such as assessment of timber resources and biomass, quantification of 3D canopy structures, as well as derivation of single trees properties like individual tree positions and tree height. LiDAR studies in forestry often used various canopy height and canopy density variables like laser derived height percentiles and proportions of laser return within various vertical canopy layers as predictor variables. So we can use the airborne laser data to estimate Leaf Area Index (LAI) or foliar mass. In this study, we use the single tree height and LAI data that was investigated in the Chitou and Alishan area, and use airborne LiDAR data to measured mean tree height, analyze the relationship between LAI and Laser Penetration Index (LPI) suitable raster cell size and estimation model. The results of the study indicated that the laser single tree height overestimates the ground truth tree height, especially in compression tree. The laser mean height is computed as the arithmetic mean of the largest laser values within grid size of 15m, and the results was R2 = 0.993. When the raster grid size was above 5× 5 m2 that could be used to calculate the LAI, and the R2 was above 0.6 when the LiDAR point cloud density was about 2-4 pts m-2 . The results was R2 = 0.979 when the LiDAR raster grid size was 15× 15 m 2. Therefore, LiDAR data is useful to estimate the LAI. The estimate map of LAI was obtained according to the results of regression analysis, and it can be found that the LiDAR point cloud of three-dimensional structure made the LAI values between forested region and non-forested region had significant differences. It shows that the LiDAR data have better detection ability for the forest canopy. It provides a new remote sensing technique for forestry investigation in Taiwan.
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Condon, Timothy. "Achieving improved leaf area index estimations from digital hemispherical imagery through destructive sampling methods". Thesis, 2018. https://hdl.handle.net/2144/37054.
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Destructive sampling of 20 trees of four tree species in a mixed New England conifer/hardwood stand shows that leaf area comprises 72, 77, and 78 percent of plant area as measured with digital hemispherical photography of the stand in (1) leaf-off, (2) leaf-out and pre-harvest, and (3) leaf-out and post-harvest conditions. Leaf area index values for the stand, estimated through destructive sampling, were 4.42, 5.98, and 5.08 respectively, documenting the progression of leaf growth through post-harvest. Terrestrial lidar scans (TLS) of the stand in (1) leaf-off and (2) leaf-out and pre-harvest conditions provided leaf area index values of 4.49 and 6.00 using the correction applied to observed plant area index, showing good agreement. The method relies on destructive sampling to relate the weight of foliage removed from sample trees to leaf area and fine twig area within the foliage as measured by a flatbed scanner. Two conifer species, eastern hemlock and white pine, and two deciduous species, red maple and red oak, in five diameter-size classes, were harvested from the 50 x 50-m stand in late summer. Leaf and twig areas of these trees provided species-specific allometric equations relating stem basal area to leaf and twig area, and a stand map provided species, counts and diameters of all trees in the plot. These data then allowed estimation of the leaf area of the stand as a whole for comparison with optical methods. The ratios of leaf to fine-branch area for each species vary, with values of 5.33, 25.38, 260.88 and 140.35 for eastern hemlock, white pine, red maple, and red oak respectively. This variance shows that woody-to-total area constants, which are used for calculating leaf area index from plant area index values determined by optical gap probability methods, will be quite dependent on stand composition and questions the common usage of literature constants for this purpose. This study shows how destructive sampling can lead to better estimation of forest leaf area index and wood area index from hemispherical photography and terrestrial lidar scanning, which has the potential to improve modeling of nutrient cycling and carbon balance in ecosystem models.
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Ghebremicael, Selamawit T. "Estimating leaf area index (LAI) of black wattle (Acacia mearnsii) using Landsat ETM+ satellite imagery". Thesis, 2003. http://hdl.handle.net/10413/4511.
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Leaf area index (LAI) is an important variable in models that attempt to simulate carbon,
nutrient, water and energy fluxes for forest ecosystems. LAI can be measured either
directly (destructive sampling) or by using indirect techniques that involve estimation of
LAI from light penetration through canopies. Destructive sampling techniques are
laborious, expensive and can only be carried out for small plots. Although indirect
techniques are non-destructive and less time consuming, they assume a random foliage
distribution that rarely occurs in nature. Thus a technique is required that would allow for
rapid estimation of LAI at the stand level. A means of getting this information is via
remotely sensed measurements of reflected energy with an airborne or satellite-based
sensor. Such information on an important plant species such as Acacia mearnsii (Black
Wattle) is vital as it provides an insight into its water use.
Landsat ETM+ images covering four study sites In KwaZulu-Natal midlands
encompassing pure stands of Acacia mearnsii were processed to obtain four types of
vegetation indices (VIs). The indices included: normalized difference vegetation index
(NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) and vegetation
index 3 (VB). Ground based measurements of LAI were made using destructive
sampling (actual LAI) and LAI-2000 optical instrument, (plant area index, PAl). Specific
leafarea (SLA) and leaf area (LA) were measured in the field for the entire sample stands
to estimate their LAI values. The relationships between the various VIs and SLA, actual
LAI and PAl values measured by LAI-2000 were evaluated using correlation and
regression statistical analyses.
Results showed that the overall mean SLA value of Acacia mearnsii was 8.28 m2kg-1
SLA showed strong correlations with NDVI (r=0.71, p
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Fassnacht, Karin S. "Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper". 1995. http://catalog.hathitrust.org/api/volumes/oclc/35098568.html.
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Thesis (M.S.)--University of Wisconsin--Madison, 1995.Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 72-77).
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Mthembu, Sibusiso L. "Estimating leaf area index (LAI) of gum tree (Eucalyptus grandis X camaldulensis) using remote sensing imagery and LiCor-2000". 2001. http://hdl.handle.net/10413/4927.
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The use of remotely sensed data to estimate forest attributes involves the acquisition of ground
forest data. Recently the acquisition of ground data (field based) to estimate leaf area index (LAI)
and biomass are becoming expensive and time consuming. Thus there is a need for an easy but yet
effective means of predicting the LAI, which serves as an input to the forest growth prediction
models and the quantification of water use by forests. The ability to predict LAI, biomass and
eventually water use over a large area remotely using remotely sensed data is sought after by the
forestry companies. Remotely sensed LAI values provide the opportunity to gain spatial information
on plant biophysical attributes that can be used in spatial growth indices and process based growth
models. In this study remotely sensed images were transformed into LAI value estimates, through
the use of four vegetation indices (Normalized Difference Vegetation Index (NDVI), Corrected
Normalized Difference Vegetation Index (NDVlc), Ratio Vegetation Index (RVI) and Normalized
Ratio Vegetation Index (NRVI). Ground based measurements (Destructive Sampling and Leaf
Canopy Analyzer) relating to LAI were obtained in order to evaluate the vegetation indices value
estimates. All four vegetation indices values correlated significantly with the ground-based
measurements, with the NDVI correlating the highest. These results suggested that NDVI is the best
in estimating the LAI in Eucalyptus grandis x camaldulensis in the Zululand region with correlation
coefficients of 0.78 for destructive sampling and 0.75 for leaf canopy analyzer. Visual inspection of
scatter plots suggested that the relations between NDVI and ground based measurements were
variable, with R2 values of 0.61 for destructive sampling and 0.55 for Leaf Canopy analyzer. These
LAI estimates obtained through remotely sense data showed a great promise in South African
estimation of LAI values of Eucalyptus grandis x camaldulensis. Thus water use and biomass can
be quantified at a less expensive and time-consuming rate but yet efficiently and effectively.Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2001.
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Mthembu, Ingrid Bongiwe. "Estimating foliar and wood lignin concentrations, and leaf area index (LAI) of Eucalyptus clones in Zululand usig hyperspectral imagery". 2006. http://hdl.handle.net/10413/3470.
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To produce high quality paper, lignin should be removed from the pulp.
Quantification of lignin concentrations using standard wet chemistry is
accurate but time consuming and costly, thus not appropriate for a large
number of samples. The ability of hyperspectral remote sensing to predict
foliar lignin concentrations could be utilized to estimate wood lignin
concentrations if meaningful relationships between wood and foliar chemistry
are established. LAI (leaf area index) is a useful parameter that is
incorporated into physiological models in forest assessment. Measuring LAI
over vast areas is labour intensive and expensive; therefore, LAI has been
correlated to vegetation indices using remote sensing. Broadband indices use
average spectral information over broad bandwidths; therefore details on the
characteristics of the forest canopy are compromised and averaged.
Moreover, the broadband indices are known to be highly affected by soil
background at low vegetation cover. The aim of this study is to determine
foliar and wood lignin concentrations of Eucalyptus clones using hyperspectral
lignin indices, and to estimate LAI of Eucalyptus clones from narrowband
vegetation indices in Zululand, South Africa
Twelve Eucalyptus compartments of ages between 6 and 9 years were
selected and 5 trees were randomly sampled from each compartment. A
Hyperion image was acquired within ten days of field sampling, SI and LAI
measurements. Leaf samples were analyzed in the laboratory using the
Klason method as per Tappi standards (Tappi, 1996-1997). Wood samples
were analyzed for lignin concentrations using a NIRS (Near Infrared
Spectroscopy) instrument. The results showed that there is no general model
for predicting wood lignin concentrations from foliar lignin concentrations in
Eucalyptus clones of ages between 6 and 9 years. Regression analysis
performed for individual compartments and on compartments grouped
according to age and SI showed that the relationship between wood and foliar
lignin concentration is site and age specific. A Hyperion image was georeferenced
and atmospherically corrected using ENVI FLAASH 4.2.
The equation to calculate lignin indices for this study was: L1R= ~n5il: A'''''y .
1750 AI680
The relationship between the lignin index and laboratory-measured foliar lignin
was significant with R2 = 0.79. This relationship was used to calculate imagepredicted
foliar lignin concentrations. Firstly, the compartment specific
equations were used to calculate predicted wood lignin concentrations from
predicted foliar lignin concentrations. The relationship between the laboratorymeasured
wood lignin concentrations and predicted wood lignin concentrations
was significant with R2 = 0.91. Secondly, the age and site-specific equations
were used to convert foliar lignin concentration to wood lignin concentrations.
The wood lignin concentrations predicted from these equations were then
compared to the laboratory-measured wood lignin concentrations using linear
regression and the R2 was 0.79 with a p-value lower than 0.001.
Two bands were used to calculate nine vegetation indices; one band from the
near infrared (NIR) region and the other from the short wave infrared (SWIR).
Correlations between the Vis and the LAI measurements were generated and
. then evaluated to determine the most effective VI for estimating LAI of
Eucalyptus plantations. All the results obtained were significant but the NU
and MNU showed possible problems of saturation. The MNDVI*SR and
SAVI*SR produced the most significant relationships with LAI with R2 values
of 0.899 and 0.897 respectively. The standard error for both correlations was
very low, at 0.080, and the p-value of 0.001.
It was concluded that the Eucalyptus wood lignin concentrations can be
predicted using hyperspectral remote sensing, hence wood and foliar lignin
concentrations can be fairly accurately mapped across compartments. LAI
significantly correlated to eight of the nine selected vegetation indices. Seven
Vis are more suitable for LAI estimations in the Eucalyptus plantations in
Zululand. The NU and MNU can only be used for LAI estimations in arid or
semi-arid areas.Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.
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Mzinyane, Thamsanqa D. "An investigation into estimating productivity, above ground biomass and leaf area index of Eucalyptus grandis using remotely sensed data and a process-based model". 2007. http://hdl.handle.net/10413/3335.
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South Africa depends largely on afforestation programs for its timber supplies due to the great demands for fiber and wood products. This has brought discomfort to other water users who have advocated that the effects of afforestation on water resources are detrimental to the country as a whole since South Africa is known as a water scarce country. This study has undertaken to integrate a process-based model and remote sensing data to estimate water use and productivity of Eucalyptus grandis in the Zululand areas of South Africa. The remote sensing techniques and recently developed "process based model" that is 3PG-S were used to estimate water use and productivity of Eucalyptus grandis, an economically important plantation species grown in the summer rainfall areas of South Africa. The study utilized monthly Landsat Thematic Mapper datasets and climatic data as inputs into the 3PG-S model, determined the Leaf Area Index (LAI) and Specific Leaf Area (SLA) through direct (destructive sampling) and indirect measurements (LiCor- 2000) and assessed the relationships between various vegetation indices (VI's) using correlation and regression analyses. The results suggest that all the indices, except the ratio VI, correlated significantly with LiCor-determined and destructively measured LAI values with both normalized difference vegetation index (NDVI) and Ratio Vegetation Index (RVI) (r=0.86, pThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritburg, 2007.
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Terracciano, Anthony. "Design and Development of Heterogenous Combustion Systems for Lean Burn Applications". Master's thesis, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6201.
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Combustion with a high surface area continuous solid immersed within the flame, referred to as combustion in porous media, is an innovative approach to combustion as the solid within the flame acts as an internal regenerator distributing heat from the combustion byproducts to the upstream reactants. By including the solid structure, radiative energy extraction becomes viable, while the solid enables a vast extension of flammability limits compared to conventional flames, while offering dramatically reduced emissions of NOx and CO, and dramatically increased burning velocities. Efforts documented within are used for the development of a streamlined set of design principles, and characterization of the flame's behavior when operating under such conditions, to aid in the development of future combustors for lean burn applications in open flow systems. Principles described herein were developed from a combination of experimental work and reactor network modeling using CHEMKIN-PRO. Experimental work consisted of a parametric analysis of operating conditions pertaining to reactant flow, combustion chamber geometric considerations and the viability of liquid fuel applications. Experimental behavior observed, when utilizing gaseous fuels, was then used to validate model outputs through comparing thermal outputs of both systems. Specific details pertaining to a streamlined chemical mechanism to be used in simulations, included within the appendix, and characterization of surface area of the porous solid are also discussed. Beyond modeling the experimental system, considerations are also undertaken to examine the applicability of exhaust gas recirculation and staged combustion as a means of controlling the thermal and environmental output of porous combustion systems. This work was supported by ACS PRF "51768-ND10 and NSF IIP 1343454.M.S.M.E.
Masters
Mechanical and Aerospace Engineering
Engineering and Computer Science
Mechanical Engineering; Thermo-Fluids Track