There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satelliteimagetimeseries (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.
In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satelliteimagetimeseries based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.
Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance ortho- images are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the timeseries. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satelliteimage sequences
Contrary to airborne sensors and as illustrated in Figure 1, it is difficult to acquire satellite images without any cloud, especially in areas such as the French overseas territories (e.g. Guyane, Martinique, Guadeloupe) where the cloud cover is known to be important all year round. To limit this problem, providers of satellite data generally wait for a favourable weather window so that the cloud cover is minimal. However, this solution is not operable in areas (e.g. Guyane) that are almost always nebulous. Here, the solution, already mentioned above, consists in acquiring images regardless the weather conditions. That results in obtaining timeseries i.e. a pile of satellite images with a high number of images, as depicted in Figure 2 for our Hispaniola test area. If this solution involves difficult pos-procedures for generating a virtual cloud-free satelliteimage from those contained in timeseries, it also presents the advantage of maximizing the acquisition capability of the satellite. This is the main reason why this acquisition configuration is considered by an increasing number of satellite data providers. In our project, we assumed to have such multi- temporal satellite images and we built our cloud detection system upon this hypothesis.
series biasanya lebih sering digunakan untuk suatu peramalan/prediksi. Dalam tehnik peramal an dengan timeseries ada 2 kategori utama yang perlu dilakukan pengujian, yaitu pemulusan (smoothing) dan dekomposisi (decomposition). Metode pemulusan mendasarkan ramalannya dengan prinsip rata-rata dari kesalahan masa lalu (Averaging smoothing past errors) dengan menambahkan nilai ramalan sebelumnya dengan persentase kesalahan (percentage of the errors) antara nilai sebenarnya (actual value) dengan nilai ramalannya (forecasting value). Metoda dekomposisi mendasarkan prediksinya dengan membagi data timeseries menjadi beberapa komponen dari Trend, Siklis, Musiman dan pengaruh Random; kemudian mengkombinasikan prediksi dari komponen-komponen tersebut (kecuali pengaruh random yang sulit diprediksi). Pendekatan lain untuk peramalan adalah metoda causal atau yang lebih dikenal dengan sebutan regresi.
Researchers of various countries are actively involved in the issues of drought detection, risk identification and assessment of the damage (Seiler and al., 2010; Zhang and al., 2010; Shcherbenko, 2011; Savin and al., 2010). The mapping of areas with a greatest risk is one of the important tasks. Remote sensing data are widely used as the main source of information. Currently, satellite data are generally accepted, objective and reliable source of information for a wide range of investigations and vegetation monitoring.
Fig.1. Spatial pattern of annual primary productivity over cropland in India during 2003-04 from VPM 3.2 Temporal pattern of GPP over major crop rotations Time-evolution of carbon uptake in agro-ecosystem differs from natural ecosystems and is a key to understand influence of phenology, environmental and cropping practice such as crop rotation and C4/C3 crop mixture. Henceforth, the time-series of modeled GPP on decadal time scale extracted for various crop rotation practiced dominantly in selected districts and presented in figure 2a & b. Well managed agro-ecosystems in Trans-Gangetic and upper Indo-Gangetic plains had two distinct peaks with
During the past few decades the Greenland and Antarctic ice sheets have lost ice at accelerating rates, caused by increasing surface temperature. The melting of the two big ice sheets has a big impact on global sea level rise. If the ice sheets would melt down entirely, the sea level would rise more than 60 m. Even a much smaller rise would cause dramatic damage along coastal regions. In this paper we report about a major upgrade of surface elevation changes derived from laser altimetry data, acquired by NASA’s Ice, Cloud and land Elevation Satellite mission (ICESat) and airborne laser campaigns, such as Airborne Topographic Mapper (ATM) and Land, Vegetation and Ice Sensor (LVIS). For detecting changes in ice sheet elevations we have developed the Surface Elevation Reconstruction And Change detection (SERAC) method. It computes elevation changes of small surface patches by keeping the surface shape constant and considering the absolute values as surface elevations. We report about important upgrades of earlier results, for example the inclusion of local ice caps and the temporal extension from 1993 to 2014 for the Greenland Ice Sheet and for a comprehensive reconstruction of ice thickness and mass changes for the Antarctic Ice Sheets.
Uji stasioner dilakukan untuk mengetahui apakah data deret waktu yang digunakan bersifat stasioner atau tidak stasioner. Sifat kestasioneran (stasionary) sangat penting bagi data timeseries, karena jika suatu data timeseries tidak stasioner maka hanya dapat mempelajari prilakunya pada waktu tertentu, sedangkan untuk peramalan (forecasting) akan sulit untuk dilakukan. Pengujian terhadap keberadaan unit root untuk semua variabel yang dimasukkan dalam model menunjukkan bahwa seluruh variabel pda level tidak mempunyai unit root atau dapat dikatakan semua variabel stasioner. Hasil pengujian unit root dengan menggunakan Kwiatkowski-Phillips- Schmidt-Shin (KPSS).
The first appearance of spectral analysis in the study of macroeconomic timeseries dates motivated by the requirement of a more insightful knowledge of the series structure and supported by the contemporaneous progress in spectral estimation and computation. The first works focused on the problem of seasonal adjustment procedures and on the general spectral structure of economic data. Cross spectral methods were pointed out from the outset as being important in discovering and interpreting the relationships between economic variables. After the early years, the range of application of such analysis was extended to the study of other econometric issues, among which the controversial trend-cycle separation, the related problem of business cycles extraction and the analysis of co-movements among series, usefiil in the study of international business cycles. In particular, cross spectral analysis allows a detailed study of the correlation among series. An empirical investigation about the possibility that the market is in a self-organized critical state (SOC) show a power law behaviour in the avalanche size, duration and laminar times during high activity period (Bartolozzi, Leinweber and Thomas, 2005).
Thirdly, like in the Jordan network, self-recurrent loops in the state layer can be introduced. The weights of these loops, and the weights of the feedback copies resulting from the recurrent one-to-one connections, are chosen such as to scale the theoretically maximum input to each unit in the state layer to 1, and to give more or less weight to the feedback connections or self-recurrent loops, respectively. If, for instance, 75 % of the total activation of a unit in the state layer comes from the hidden layer feedback, and 25 % comes from self-recurrency, the state vector will tend to change considerably at each time step. If, on the other hand, only 25 % come from the hidden layer feedback, and 75 % from the self-recurrent loops the state vector will tend to remain similar to the one at the previous time step. 47] speaks of exible and sluggish state spaces, respectively. By introducing several state layers with dierent such weighting schemes, the network can exploit both the information of rather recent time steps and a kind of average of several past time steps, i.e. a longer, averaged history.
Peramalan memegang peranan yang penting dalam kehidupan, suatu kejadian yang belum diketahui dapat diprediksi dengan menggunakan data-data historis dari kejadian tersebut. Analisis timeseries sering digunakan dalam melakukan peramalan terhadap data-data historis, sebagai contoh dalam mengamati kecepatan angin, tekanan darah dalam tubuh dan transaksi bursa saham baik domestik maupun internasional, kebutuhan listrik, dan lain sebagainya.
Ti ngkat kesalahan fuzzy t ime ser ies Hsu sebesar 0,6 %, sedangkan tingkat kesal ahan metode holt double exponenti al smoot hi ng adal ah 2,25 %. Ber dasar kan hasil per hi tungan di at as dapat di tar i k kesi mpulan bahw a, tingkat kesalahan per amalan ni lai tukar r upiah ter hadap dolar Amer ika menggunakan met ode fuzzy t ime series hsu. l ebi h kecil dibandingkan met ode holt double exponent ial smoot hing . Hasil per amalan ni lai tukar r upiah menggunakan met ode fuzzy ti me er i es hsu adalah untuk t anggal 21, 22, 23, 24, dan 25 pada bulan juni secar a ber tur ut-tur ut adal ah Rp. 13355, Rp. 13375, Rp. 13395, Rp. 13465, Rp 13.475.