Browsing by Subject "Sentinel-2"

Now showing 1 - 5 of 5

Access status: Open Access ,
An application of the »traffic lights« idea to crop control in integrated administration control system
(Wydawnictwa AGH, 2021) Hejmanowska, Beata; Twardowski, Mariusz; Żądło, Anna
The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of »traffic lights« is introduced. Classification into a given »traffic lights« color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of »traffic lights«, a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.
Access status: Open Access ,
Forest community mapping using hyperspectral (CHRIS/PROBA) and Sentinel-2 multispectral images
(Wydawnictwa AGH, 2022) Głowienka, Ewa; Zembol, Nicole
The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer's accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.
Access status: Open Access ,
The application of remote sensing techniques and spectral analyzes to assess the content of heavy metals in soil - A case study of Barania Góra Reserve, Poland
(Wydawnictwa AGH, 2022) Sobura, Szymon; Hejmanowska, Beata; Widłak, Małgorzata; Muszyńska, Joanna
The understanding of the spatial and temporal dynamics of farmland processes is essential to ensure the proper crop monitoring and early decision making needed to support efficient resource management in agriculture. By creating appropriate crop management strategies, one can increase harvest efficiency while reducing costs, waste, chemical spraying, and inhibiting the impact of biotic and abiotic factors on crop stress. Only reliable spatial information makes it possible to comprehend the influence of various factors on the environment. The main objective of the research presented in the paper was to assess the possibility of using maps of vegetation and soil indices, such as NDVI, SAVI, IRECI, CIred-edge, PSRI and HMSSI, calculated on the basis of images from the Sentinel-2 satellite, to qualitatively determine the increased amount of heavy metals in the soil in the areas of small agricultural plots around the Barania Góra nature reserve in Poland. The conducted pilot project shows that the spectral indices: NDVI, SAVI, IRECI, CIred-edge, PSRI, and HMSSI, calculated on the basis of images from Sentinel-2, have the potential to assess the content of nickel zinc, chromium and cobalt in the soil on agricultural plots. However, the confirmation of the obtained results requires continuation of the research.
Access status: Restricted ,
Wykorzystanie darmowych danych geoprzestrzennych w monitorowaniu działalności rabunkowej na stanowiskach archeologicznych
(Data obrony: 2020-09-29) Lisowska, Anna
Wydział Geodezji Górniczej i Inżynierii Środowiska
Access status: Restricted ,
Wykorzystanie obrazów satelitarnych Sentinel-2 na potrzeby badania zmienności typów pokrycia terenu
(Data obrony: 2020-11-12) Mochocka, Marta
Wydział Geodezji Górniczej i Inżynierii Środowiska