XXII ISPRS Congress 2012: Technical Commission III

Bibliographic data

Monograph

Persistent identifier:: 837898897

Author:: Berthelot, Marcellin

Title:: Praktische Anleitung zur Ausführung thermochemischer Messungen

Scope:: XII, 111 Seiten

Year of publication:: 1893

Place of publication:: Leipzig

Identifier (digital):: 837898897

Signature of the source:: Chem. 82

Language:: German

Publisher of the original:: Barth

Usage licence:: Public Domain Mark 1.0

Translator:: Siebert, G.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2015

Document type:: Monograph

Collection:: Chemistry

Cover

Document type:: Monograph

Structure type:: Cover

XXIX-B3, 2012 MODIS DATA VORKS and Vietnam. These ty. Yearly rice crop plans to ensure food DIS data. Data were iage classification of ] reference data. The f 80.3% and Kappa have been proven to n tasks. In this work, atterns in the study . Statistical learning 294). levelop an approach outheast Asia from velet transform and | countries: Vietnam, n 5.62-23.45 N and a is approximately ] land occupies ce was a main crop )s such as sugarcane, s. Monitoring rice y due to the official d according to their Basically, there are ingle-cropped rice, ole cropping system ) was often planted Ist double and triple ss (90-100 days) are ekong River Delta Phraya River Delta ands and our study mountainous areas 1 were masked out J RTM) 90 m digital International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 98 402 406 410 L 1 1 24 ZA T 2 12 4 Chao Phraya River Delta y d ROAST. Pe-^ Mekohg Riv vo) M amos g River a T : Delta Sa z wo} 3 1 $ I- o d^; s : X 0 100 200 400 Kitometers o: ——— A $n a 8" RN ^ : E Areas higher 500 m P^ Mekong River KE Sampling areas T T T T 98 102 106 110 Figure 1. Map of the study area showing the sampling areas (ground reference data) used for extracting training data used in ANNs and evaluating accuracy classification results. 3. DATA MODIS/Terra surface reflectance 8-day L3 global 500 m SIN grid v005 (MODO9A1 product) acquired from NASA for 2010 were used in this study. This data product has seven spectral bands. The spatial resolution is 500 m x 500 m. The data include quality control lags for image artifacts. It has also been validated for stage 2 and is ready for use in scientific publications (Vermote et al., 2008). Other data including the Lower Mekong Basin land-use map (MRC, 2010), the 2000 VMD land-use map (Sub-NIAPP, 2002), and the 2000 Thailand land-use map (LDD, 2000) were also collected and used for field investigation and preparation of ground reference data. 4. METHODS 4.1 Time-series NDVI data noise filtering The time-series NDVI data were created by first calculating every 8-day MODIS scene. They were then stacked into one 8-day composite scene with 46 bands. Time-series MODIS NDVI data are often obscured by noise due to the cloud cover commonly seen in the tropical climate. Thus, it is essential to mitigate such noise before the data can be used for classification. For this reason, we first masked out thick cloud cover using the blue band, where its reflectance value was greater than 0.2 (Xiao et al., 2006). The missing values were replaced with new values from the time-series profile using linear interpolation. The wavelet transform was then applied to filter noise from the time-series NDVI data. The wavelet transform W(s,t) of a signal x(t) is defined as follows: W(s, 1) = S x(t)w (=) dt, (1) where s > 0 and x € R, x(t) is the analyzed input signal; V(t) is the mother wavelet; and s and c are scaling and translation parameters. In this study, we used Coiflet wavelet (order 4) (Torrence and Compo, 1998). This wavelet function has been demonstrated to give the best results among Daubechies and Symlet wavelet functions for determining rice crop phenology (Sakamoto et al., 2005). 4.2 Image classification The ANNs back-propagation algorithm was utilized for classification. This algorithm uses the delta rule of a steepest descent to adjust weights based on the backward propagation of errors in the network (Paola and Schowengerdt, 1995). The ANNs has a learning process where training signatures are randomly selected and fed to adjust the internal weight matrix. This is made by a repetition of back-propagations of the answer into the weight matrix. When the learning process is complete, the weight matrix is ready to process any new signature from the imagery dataset. In this study, we designed ANN architecture: one input layer, one output layer and one hidden layer. The number of 93 neurons were calculated based on the existing literature recommendations: 2m + 1 (Atkinson and Curran, 1995), where n is the number of bands. The commonly-used back-propagation algorithm using tansig function was utilized to train the network. The training samples used to train ANNS for classification were extracted from the ground reference data prepared after field investigation. A total of samples of 14,602 (single-cropped rice: 2,333, double-cropped rice: 3,638, triple-cropped rice: 1,336, field crops: 1,020, forests/orchards/perennial trees: 5,741, and built-up areas: 534) seems to most of variability of the study was used. Water bodies were excluded from the analysis. We identified water bodies and masked them out by analysis of smooth NDVI and land surface water index (LSWI) profiles (Xiao et al., 2006). The training samples were divided into three parts: training samples (50% of the total samples), validation samples (25% of the total samples), and testing samples (25% of the total samples). The network’s performance is measured using a mean squared error (MSE) and a confusion matrix. The training process was carried out until the mean squared error (MSE) reached 0.00044 at the epoch 366. The confusion matrix shows the percentages of correct classification of 98.8%. There was significant correlation between testing samples and outputs R= 0.99). The classification results values range from 0 to 1. The winner-take-all algorithm was used to get the class indices as the position of the highest element in each output vector of ANNs. 4.3 Accuracy assessment The classification results were compared with the field data. A total of 2,000 pixels for each class were randomly extracted from the ground reference data. The classification accuracy assessment was performed such that these ground reference pixels were compared with that of the classified map using the confusion matrix. Kappa coefficient and other parameters (i.e., overall, producer and user accuracies) were used to measure the classification accuracy.

Access restriction

Copyright

Object: Technical Commission III (B3)

Monograph

Cover

Table of contents

Downloads

Full record

This page

Image fragment

Formats and links

Links

Cite

Full record

This page

Citation recommendation

Search results