Bands Of Satellite Data Research Articles

Urban Expansion and Rising Surface Temperature: A Multi Temporal Assessment in Kochi Metropolis, Kerala

The process of urbanization and urban growth has resulted in rapid, uncontrolled conversion of natural land cover and spreading of impervious concrete surfaces in many parts of the world leading to serious environmental concerns. There are a myriad of micro climatic effects due to urban growth. One of the significant consequences is the rising urban temperature. Urban surface temperature responds to changes in land use/land cover dynamics and urban expansion. This would ultimately lead to local climatic changes and would accentuate global warming. Geo spatial technology provides sophisticated tools for analyzing urban features and micro climates. Land surface temperature (LST) or the radiant energy from earth surface can be calculated using thermal bands of satellite data. Kochi, the largest urban agglomeration in Kerala, is undergoing hasty developments and land modifications. This study attempts to monitor changes in land surface temperature from 2000 to 2020 along with changes in built up and vegetation cover. The results revealed that the average surface temperature of Kochi recorded an increase of 0.60C and the loss of green cover has contributed to the increased temperature. Moreover, the LST exhibits a strong positive correlation with built up land expansion. The study findings stand helpful for urban planning and sustainable development of the city.

Model for Estimation of Global Horizontal Irradiance in the Presence of Dust, Fog, and Clouds

The poor spatial resolution of available ground data provides an opportunity to estimate solar irradiation from computational models based on the satellite data. The developed model employs various spectral bands of the satellite data and focuses on distinguish weather signatures, such as: dust, aerosols, fog, and clouds. These parameters affect the solar radiation availability all over the world. However, for a tropical country like India, which is potentially rich in solar energy resources, the study of these parameters is of crucial importance. A modified version of the HELIOSAT model is developed and an all-day index based on various bands of satellite data is introduced. Results are validated against the ground data recorded at various stations of the Indian Meterological Department as well as with the Meteosat East data.

Comparison of split-window algorithms for the retrieval of surface temperatures from AVHRR data in southern Quebec

Land surface temperature is a significant parameter for energy and water cycles in the earth–atmosphere system. Many studies have proposed various algorithms, such as the split-window method, for retrieving surface temperatures from two different thermal infrared bands of satellite data. Each algorithm is developed for a limited study area and application. Here, as part of developing an optimal split-window method in southern Quebec, a comparison of algorithms is carried out to evaluate the performance of each algorithm and examine correlation and differences between them. Furthermore, the deviation of surface temperature derived from various algorithms is compared with the brightness temperature of the thermal infrared band at 11 µm as a sensitivity test for atmospheric effects.

ニューラルネットワークを利用したマルチスペクトル衛星データの次元縮小型圧縮

In recent years, more and more multi spectral satellite data have become available, and accordingly, the method to transform multi-dimensional data into less dimensional ones (called as “dimensional compression”) is desired for the purpose of effective and comprehensive use of all the data. Principal Component Analysis (PCA) is one of the most traditional method for the dimensional compression in remote sensing field, but its limitation is proved that PCA cannot function so flexibly because of linear transformation. Neural Network (NN) models can behave like a non-linear transform function as a whole, because non-linear functions are embedded in a model and the complexity of those functions connection can implement non-linear performance as a whole. In this paper, authors examined the feasibility of the NN model for dimensional compression, which had been theoretically reported to be superior to PCA and had been examined only by applying for simulated data. Firstly, the architecture of the NN model for dimensional compression is described. Then, the NN model is applied for the compression of 6 bands of satellite data, or Lansdat TM data into 1, 2 or 3 dimensional ones. The compression efficiency of the NN model is compared with PCA's one by two indices, correlation coefficient and standard deviation of errors between the original data and the restored one. The result demonstrates that in the case of compression into 1 and 2 dimensions, the NN model is more appropriate for the dimensional compression than PCA, and that in the case of compression into 3 dimensions, there is no meaningful difference between both methods.