We present a stripmap-mode raw data generator that accounts for trajectory deviations with a nonzero squint angle for an extended scene. The approach is attempted under the acquisition Doppler (AD) geometry, rather than a standard cylindrical reference system. It essentially utilizes one-dimensional azimuth Fourier domain processing, followed by range time-domain integration. As a result, the proposed algorithm is more computationally efficient compared to the time-domain method and yet maintains high accuracy. The effectiveness and efficiency of the proposed algorithm are demonstrated by numerical simulations.
Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don’t have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.
Cloud detection is a key work for the estimation of solar radiation from remote sensing. Particularly, the
detection of thin cirrus cloud and the edges of thicker cloud is critical and difficult. To obtain accurate
estimates of cloud cover of MTSAT-1R image, we propose an effective cloud detection algorithm for
improving the detection of thin cirrus cloud and the edges of thicker cloud. Using the brightness
temperature difference (BTD) and lookup table to identify cloud-free and cloud-filled pixels is not
sufficient for MTSAT-1R data on the region of China. Therefore, a new lookup table (LUT) is made by
extending the original one. On the basis of the exiting method, in order to apply to the MTSAT-1R
satellite data in China region, we expand the scope of the latitude and extend the applicable scope of
satellite zenith angle. We change the interpolation method from linear mode to nonlinear mode. The
evaluation results indicate that our proposed method is effective for the cirrus and the edges of thicker
cloud detection of MTSAT-1R in China region.
Spatial heterogeneity of the animal-landscape system has three major components: heterogeneity of
resource distributions in the physical environment, heterogeneity of plant tissue chemistry,
heterogeneity of movement modes by the animal. Furthermore, all three different types of
heterogeneity interact each other and can either reinforce or offset one another, thereby affecting
system stability and dynamics. In previous studies, the study areas are investigated by field sampling,
which costs a large amount of manpower. In addition, uncertain in sampling affects the quality of field
data, which leads to unsatisfactory results during the entire study. In this study, remote sensing data is
used to guide the sampling for research on heterogeneity of vegetation coverage to avoid errors caused
by randomness of field sampling. Semi-variance and fractal dimension analysis are used to analyze the
spatial heterogeneity of vegetation coverage at Heihe River Basin. The spherical model with nugget is
used to fit the semivariogram of vegetation coverage. Based on the experiment above, it is found,
(1)there is a strong correlation between vegetation coverage and distance of vegetation populations
within the range of 0～28051.3188m at Heihe River Basin, but the correlation loses suddenly when the
distance greater than 28051.3188m. (2)The degree of spatial heterogeneity of vegetation coverage at
Heihe River Basin is medium. (3)Spatial distribution variability of vegetation occurs mainly on small
scales. (4)The degree of spatial autocorrelation is 72.29% between 25% and 75%, which means that
spatial correlation of vegetation coverage at Heihe River Basin is medium high.
Aerosol Optical Depth (AOD) is one of the key parameters which can not only reflect the characterization of atmospheric
turbidity, but also identify the climate effects of aerosol. The current MODIS aerosol estimation algorithm over land is
based on the “dark-target” approach which works only over densely vegetated surfaces. For non-densely vegetated
surfaces (such as snow/ice, desert, and bare soil surfaces), this method will be failed. In this study, we develop an algorithm
to derive AOD over the bare soil surfaces. Firstly, this method uses the time series of MODIS imagery to detect the “
clearest” observations during the non-growing season in multiple years for each pixel. Secondly, the “clearest”
observations after suitable atmospheric correction are used to fit the bare soil’s bidirectional reflectance distribution
function (BRDF) using Kernel model. As long as the bare soil’s BRDF is established, the surface reflectance of “hazy”
observations can be simulated. Eventually, the AOD over the bare soil surfaces are derived. Preliminary validation results
by comparing with the ground measurements from AERONET at Xianghe sites show a good agreement.