Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: pomiary hiperspektralne

Sortuj według:

Ogranicz wyniki do:

Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik

Określenie składu gatunkowego lasów Góry Chojnik (Karkonoski Park Narodowy) z wykorzystaniem lotniczych danych hiperspektralnych APEX

100%

Raczko E., Zagajewski B., Ochtyra A., Jarocinska A., Marcinkowska-Ochtyra A., Dobrowolski M.

Sylwan

2015

tom 159

nr 07

We used hyperspectral data from APEX scanner (288 spectral bands in 380−2500 nm spectral range; 3,5 m spatial resolution) to classify five tree species occurring in the area of Mt. Chojnik in the Karkonoski National Park (south−western Poland). Data used to delimit learning and veri− fication polygons were acquired during field research in August 2013, when ground truth polygons were acquired using device equipped with GPS receiver. Raw APEX data went through radio− metric and geometric correction at VITO office. To reduce processing time, 40 most informative bands were selected using information content analysis. The Support Vector Machines (SVM) algorithm was used for classification of the following tree species: Fagus sylvatica L., Betula pendula Roth, Pinus sylvestris L., Picea alba L. Karst and Larix decidua Mill. Final classification had 78.66% overall accuracy with Kappa coefficient equal to 0.71. The best classified species included beech (87.09%) and pine (83.96%), while the worst results were obtained for larch (60.29%). Low accuracy for larch could be caused by the fact that most of larch trees in the research area grow in small patches, which made it hard to specify large enough sample of training data. All classified tree species had producer's accuracy of at least 60%, with the highest value reaching 87%. User's accuracies were from 53% for pine to 85% for beech. It is possible to classify tree species using hyperspectral data with moderate to high accuracy even if the data used lacked atmospheric correction. Further work will focus on improving the classification accuracy and use of neural networks based classification methods. Results from this paper will serve as basis for tree species map of the Karkonoski National Park.

Ocena stopnia porażenia liści żyta przez rdzę brunatną (Puccinia recondita f. sp. recondita Roberge) na podstawie polowych pomiarów hiperspektralnych

61%

Piekarczyk J., Wojtowicz A.

Progress in Plant Protection

2010

tom 50

nr 2

One of the main purpose of the research on remote sensing in plant protection is to develop simple and cheap method to detect symptoms of diseases and pests of agricultural crops in early stages of development. The aim of this study was to determine the possibility of using an ASD Field Spec3 hyperspectral radiometer to detect and monitor the development of Puccinia recondita f. sp. recondita Roberge on winter rye. The highest values of a coefficient of determination (R2 = 0.84) was obtained for the relationship between degree of infestation of leaves and values of the NDVI1 and ARI indices, which were calculated on the basis of reflectance of green (550 nm) and red (700 nm) wave lengths.

Ograniczanie wyników

1 Progress in Plant Protection

1 Sylwan

1 Dobrowolski M.

1 Jarocinska A.

1 Marcinkowska-Ochtyra A.

1 Ochtyra A.

1 Piekarczyk J.

1 Raczko E.

1 Wojtowicz A.

1 Zagajewski B.

1 2015

1 2010

Wyniki wyszukiwania

Określenie składu gatunkowego lasów Góry Chojnik (Karkonoski Park Narodowy) z wykorzystaniem lotniczych danych hiperspektralnych APEX

Ocena stopnia porażenia liści żyta przez rdzę brunatną (Puccinia recondita f. sp. recondita Roberge) na podstawie polowych pomiarów hiperspektralnych