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New Approaches in Airborne Thermal Image Processing for Landscape Assessment
Pivovarník, Marek ; Pokorný,, Jan (referee) ; Štarha, Pavel (referee) ; Zemek, František (advisor)
Letecká termální hyperspektrální data přinášejí řadu informací o teplotě a emisivitě zemského povrchu. Při odhadování těchto parametrů z dálkového snímání tepelného záření je třeba řešit nedourčený systém rovnic. Bylo navrhnuto několik přístupů jak tento problém vyřešit, přičemž nejrozšířenější je algoritmus označovaný jako Temperature and Emissivity Separation (TES). Tato práce má dva hlavní cíle: 1) zlepšení algoritmu TES a 2) jeho implementaci do procesingového řetězce pro zpracování obrazových dat získaných senzorem TASI. Zlepšení algoritmu TES je možné dosáhnout nahrazením používaného modulu normalizování emisivity (tzv. Normalized Emissivity Module) částí, která je založena na vyhlazení spektrálních charakteristik nasnímané radiance. Nový modul je pak označen jako Optimized Smoothing for Temperature Emissivity Separation (OSTES). Algoritmus OSTES je připojen k procesingovému řetězci pro zpracování obrazových dat ze senzoru TASI. Testování na simulovaných datech ukázalo, že použití algoritmu OSTES vede k přesnějším odhadům teploty a emisivity. OSTES byl dále testován na datech získaných ze senzorů ASTER a TASI. V těchto případech však není možné pozorovat výrazné zlepšení z důvodu nedokonalých atmosférických korekcí. Nicméně hodnoty emisivity získané algoritmem OSTES vykazují více homogenní vlastnosti než hodnoty ze standardního produktu senzoru ASTER.
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Standardization of Remote Sensing Usage for Forensic Ecotechnique: forest and trees
Introvičová, Sabina ; Čermák, Jan (referee) ; Ulrich, Radomír (referee) ; Alexandr, Pavel (advisor)
The thesis presents the main points of standardization of remote sensing (RS) usage for Forensic Ecotechnique: forest and trees (FEft). Based on the study FEft expertise database, the types of expertise, which remote sensing data have already been used and the types where the use of remote sensing increases the exactness of solutions were analyzed. RS data are essential in a problematic situation when an examined object does not exist or has been changed at the time of the assignment. This situation may be solved by obtaining information about the investigated object via retrospective image data, as described in the model example. There are chosen categories of image data relevant to the needs of experts in this thesis. These categories of RS data and their products are described with an indication of their possible use in the expertise processing, including freely available or paid resources and archives of image data.
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Využití smart farming technologií při hospodaření na zemědělské půdě
Kovaříková, Jana
The bachelor thesis deals with the basic theoretical description of smart farming technologies or precision farming methods in agricultural land management. Precision farming is a trend that has been developing since the early 1990s. One of the elements of precision farming is locally targeted farming that works with soil heterogeneity and tillage using new technologies. Optimizing soil inputs (mineral fertilizers, pesticides, etc.) and applying them according to the current plant requirements is one of the goals of smart farming. Through precision farming agronomic practices, we can increase the efficiency of crop production, reduce the amount of mineral fertilizer inputs and improve both economic and environmental status. The aim of the presented work was to evaluate the variable application of essential nutrients (P and K) on selected plots managed in a conventional way using PREFARM. The experiment itself was carried out in the agricultural company AGROSUMAK a.s., The practical part includes a comparison of lump and variable application of potassium salt (K2O source) and triple superphosphate (P2O5 source) on three experimental plots. Data on plot heterogeneity were provided by AGROSUMAK a.s., which uses the PREFARM system. In the conclusion of the present work, the results are discussed and compared from economic, environmental and agronomic points of view.
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Využití bezpilotního průzkumu pro cílenou aplikaci herbicidních látek
Panteleev, Evgeny
The thesis "the use of unmanned Exploration for targeted application of herbicides" is focused on the analysis of land data using unmanned Exploration for the following application of herbicides. The theoretical part of the thesis contains a description of common methods for assessing the state of stands in the form of remote sensing. And then it deals with sub-methods, herbicides and drones. The practical part deals with a semi-operational field experiment in the locality of Letkovice. The content includes the results of field and remote sensing, analyzed data from GIS and results of field experiments focused on variable applications of herbicides. And gave an economic comparison of uniform and variable methods of management.
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Prostorová analýza heterogenity pozemků z družicových dat
Kouřil, Jiří
Field heterogeneity is one of the prerequisites for many methods of precision agriculture, like for example variable sowing or variable application of fertilizers. One method of determining heterogeneity is an analysis of multispectral satellite images. The main aim of this thesis is to verify the hypothesis that there is at least a medium positive correlation between values of vegetation indices calculated from multispectral images and yields measured on four fields of a total area of approximately 75 ha during seasons 2019–2021. All fields are managed by the company Spearhead Czech s.r.o. which also provided data for preparing yield maps. These were statistically compared with three vegetation indices NDVI, EVI, and NDRE, which were calculated from Sentinel-2 satellite images. Results of correlation analysis show a medium correlation between vegetation indices and yields on two of four fields, whereas only weak positive and in some cases weak negative correlation can be observed on the other two. This was probably caused by droughts that influenced vegetation in these fields in 2019 and 2021. The thesis shows the potential of satellite images to determine field heterogeneity despite the comparison of vegetation indices and yields of different crops.
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Možnosti využití technologií precizního zemědělství pro aplikaci hnojiv
Sedláčková, Marie
The aim of the bachelor's thesis was to describe modern methods of precision agriculture using remote sensing implemented in the technology of variable applications of nitrogen fertilizers in the operating conditions of the agricultural company and subsequently analyze their economic efficiency. The research took place in the company RenoFarma Troubky, a.s. at two selected sites of interest, DPB 0102 ("Býčí kout") and DPB 9103 ("Klínka"). For production fertilization were used seven composite images from 2014 – 2020 taken by the Landsat 7 satellite. For qualitative fertilization was used an image from the UAV Trinity F90 drone with a multispectral camera, from which were subsequently generated application maps using QGIS software. Fertilization attributes were based on NDVI categorization. The model of technological processing of variable nitrogen application using remote sensing was described and schematized. UREAstabil® fertilizer was used for production and quality fertilization. Zones of fertilizer doses (kg.ha-1) were divided and correlations with average yields (t.ha-1) were monitored. Most of the zones appeared to be economically inefficient during the monitored period. The low profitability was influenced by low yields, caused by an insufficient amount of fertilizer, the late application of the quality dose and the daily rainfall (mm) monitored at the meteorogical station Amet.
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Supraglacial lakes detection and volume estimation from remote sensing data
Rusnák, Samo ; Brodský, Lukáš (advisor) ; Šobr, Miroslav (referee)
Supraglacial lakes detection and volume estimation from remote sensing data Abstract Supraglacial lakes play an important role in understanding glacier dynamics, including their response to climate change. This thesis explores the problematics of estimating lake depth and volume using a physical model. This brings challenges in considering the influence of various factors, such as cryoconite on glacier surface and suspended particular matter, which influences physical model, which is in research mostly neglected. Regression analysis of the g parameter of a physical model, representing light attenuation coefficient, and supervised classification of supraglacial lakes is applied in this thesis. The results reveal the variability of parameter Ad, representing lake bottom albedo reflectance, and its impact on predicted supraglacial lakes depth and volume. The results highlight the problem of global parameterisation of the physical model of supraglacial lakes and the need for further research to improve its accuracy and explore future possibilities in this field. Keywords: supraglacial lake, remote sensing, machine learning, physical model, depth estimation, regression analysis
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