As one of the driest and lowest regions in Asia, the vegetation variation in the Aydingkol Lake Basin in Xinjiang, China, reveals the status of its ecological recovery. To more accurately retrieve the fractional vegetation index (FVC) to investigate the vegetation variation in this basin, we proposed an innovative method—the random forest (RF) method—based on multiple remote sensing indices. The retrieval shows a satisfactory accuracy with a mean error, mean square error, mean relative error (RE) percentage, and determination coefficient of 0.01,0.04,14.61% and 0.95, respectively. In the Aydingkol Lake Basin, the average annual FVC peaked in 2015 with a value of 0.08. The annual average value of FVC decreases at a rate of 0.06 * 10 − 2 / a during these eight years. The reduction of FVC is mainly distributed in the central-western part of the basin. In exploring the influence on FVC, average annual precipitation has the most significant impact on the FVC. Its correlation coefficient with FVC is 0.87. With increasing elevation and slope, most FVC showed a decreasing trend. In recent years, the conversion of cropland to barren land and cropland to grassland had the greatest impact on decreasing of the FVC, and the mean FVC decreases from 0.19 to 0.17 before and after the land-use change. Moreover, human impact had a significant influence on the variation in FVC. Due to the establishment of the Aydingkol Lake Basin Wetland Reserve (the SAIC Volkswagen Test Site), the annual average FVC in the corresponding place increases from 0.06 to 0.13 (decreases from 0.148 to 0.005). This study is helpful to guide ecological protection in similar arid regions.
The land surface temperature (LST) derived from thermal infrared satellite images is a meaningful variable in many remote sensing applications. However, at present, the spatial resolution of the satellite thermal infrared remote sensing sensor is coarser, which cannot meet the needs. In this study, LST image was downscaled by a random forest model between LST and multiple predictors in an arid region with an oasis-desert ecotone. The proposed downscaling approach was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The primary result of LST downscaling has been shown that the distribution of downscaled LST matched with that of the ecosystem of oasis and desert. By the way of sensitivity analysis, the most sensitive factors to LST downscaling were modified normalized difference water index (MNDWI)/normalized multi-band drought index (NMDI), soil adjusted vegetation index (SAVI)/ shortwave infrared reflectance (SWIR)/normalized difference vegetation index (NDVI), normalized difference building index (NDBI)/SAVI and SWIR/NDBI/MNDWI/NDWI for the region of water, vegetation, building and desert, with LST variation (at most) of 0.20/-0.22 K, 0.92/0.62/0.46 K, 0.28/-0.29 K and 3.87/-1.53/-0.64/-0.25 K in the situation of ±0.02 predictor perturbances, respectively.
Diverse parameters that are decomposed from quad polarimetric synthetic aperture radar (PolSAR) imagery become the important basis in the target recognition and classification. The selection of effective parameters is a very important research topic. This work aims to explore the algorithm of parameter selection based on the parametric statistics and multidimensional analysis. The proposed algorithm merges the parameters from different decomposed algorithms and the optimal parameters describing the backscattering characters of the targets are explored. The difference of parameters’ locations in three-dimensional spaces is the important basis of target differentiation. Based on the selected parameters, PolSAR images are classified using the object-oriented analysis and decision tree method. The experimental results indicate that the overall accuracy and Kappa coefficient of the classification using the integrated multidimensional parameters were higher than those using Freeman and H/A/α decomposed parameters. The advantage of this algorithm is to select optimal parameter combinations in multidimensional space by integrating many parameters from different decomposed algorithms.
Land surface temperature (LST), vegetation index, and other surface characteristics that obtained from remote sensing
data have been widely used to describe urban heat island (UHI) phenomenon, but through impervious surface area (ISA)
to describe the phenomenon has only used in a few study areas in our country. In a high urbanization and high population
density region like Jiangsu Province, a wide range of extraction of ISA to study its relationship with UHI is less. In this
paper, we use multi-temporal remote sensing images as data sources, and extract ISA from it in a large-scale by using
decision tree classifier (DTC) and linear spectral mixture analysis (LSMA). Then combine the average surface
temperature from the sixth band of Landsat TM by mono-window algorithm for spatial analysis, to assess the change of
the urban heat island temperature amplitude and its relationship with the urban development density, size and ecological
environment. Finally we use statistical methods to analyze the relationship between ISA, LST and UHI. The results show
that ISA has a positive correlation with surface temperature. The ratio of ISA is higher and the difference value of the
temperature is larger, thus the UHI will be more obvious.
This paper mainly discusses the urban design factors how to affect the urban heat environment in urban residential area
by remote sensing. The discussed urban design factors include floor area ratio, building height, green area ratio, and
population density. The results indicate that when the green area ratio in residential area becomes 40%, the effect of
weakening UHI is best. Higher than 40%, the effect of reducing the temperature begins to decline. The higher the
residence buildings are, the higher the mean surface temperature of residential districts is. When floor area ratio ranges
from 1.5 to 3, the change of mean surface temperature is abrupt. When floor area ratio is greater than 3, the growth of
mean surface temperature would be slower. Surface temperature and population density have logarithm relationship.
Overall, planners have the opportunity to gain significant insight into the physical manifestations of planning policies
within cities by integrating quantitative analysis of electromagnetic energy measurements collected by remote sensing
systems. Remote sensing would be a useful tool for planners to make scientific decisions.
The polarization feature of the target could be expressed by both the scattering matrix and the Stokes matrix. In the
Back Scattering Alignment (BSA) system, scattering matrix meets the principle of reciprocity and every element of it is
a complex number. Stokes matrix is a transformed format of scattering matrix and reflects the relationship between SAR
received power and transceiver antenna polarization status. For a deterministic target, there is a one-to-one
correspondence between the scattering matrix and the Stokes matrix. Since the Stokes matrix is always a real symmetric
matrix and has the nature of normal matrix. It is usually used to save polarized scattering data. With the development of
polarization technology, polarization synthesis has already become one of the most important tools for polarization data
analysis. An optimal polarization status and the maximum reception power must exist through different parameters
combinations. That means target's optimal polarization. Traditional target's optimal polarization theory was based on
the scattering matrix. But the scattering matrix is usually obtained difficultly, so the calculating process always be much
complex. In this paper, we deduce the formulae optimal receive power based on Stokes matrix and polarization
synthesis. The algorithm could be carried out easily and the programming process is much directly. Some experiments
proof that ideal results could received by proposed algorithm.
This paper presents a novel method for image fusion that integrates improved HIS and curvelet transform, and uses it to
fuse the IKONOS images. Firstly, red band is added to panchromatic band with weights to obtain a new panchromatic
band, and blue, green and near-infrared bands are stacked to form the RGB space, which is used for converting to HIS
space later. Secondly, the new panchromatic band and intensity component carry on curvelet transform respectively.
Then fuse the coefficients in the corresponding scales to generate a new intensity component. Finally, the inverse HIS
transform is applied to generate the fusion image. To prove the superiority of this method, this paper uses several
parameters to assess the image comparing with other fusion images. The results show that the proposed method can
increase the information entropy, decrease the spectrum distortion of the fused image, and improve the structural
similarity between the fused image and the original multispectral image. So all above prove that the integrated method
can enhance the fusion quality efficiently.
This paper takes Nanjing city as an example, analyzes diurnal and seasonal characteristics of UHI by eight granule and
sixteen scenes MODIS, respectively. The land cover index (LCI) has been constructed to get a quantitative analysis
about the changes of land use/land cover how to affect the distributional characteristics of urban thermal space. The
results indicate the diurnal intensity of UHI is stronger than night's no matter whichever season it happens, but different
season has different UHI intensity. The strongest intensity of UHI happens in autumn, the second in summer, the third in
spring, the last in winter. The most extensive in scope occurs in summer, the second in autumn, the third in spring, the
last in winter. There are three centers of heat island in Nanjing, mainly locating in industrial region, not in commercial or
residential region. The spatial distribution pattern of land use/land cover affects wholly the distributional pattern of the
urban heat space. The difference of surface material's thermal and biologic feature is the essential reasons of surface
temperature distribution difference. Artificial heat has important effect on heat island. The LCI can reflect surface soil
water content and vegetation cover and explains the essential reasons that each land use/land cover contributes
differentially to urban heat island. Such an index can allow changes in land use at neighborhood-scale to be input in the
initialization of atmospheric and hydrological models, as well as provides a new approach for urban heat island analysis.
The LCI of urban land use is smaller than that of water, forest and cropland. Smaller is LCI, stronger the intensity of
urban heat island is. For a special region, LCI will increase gradually per unit area with higher urbanization level. At last,
remote sensing scale how to affect UHI time and space character is discussed. The intensity and scope of urban heat
island results are different with different remote scale. The intensity and scope using ETM+ are all lager than that using
MODIS.
How to cull shadows and extract needed information accurately is particularly significant. For major remote sensing applications, it may be preferable that shadows are minimized and the detailed information in high-resolution satellite imagery is clear. Firstly this paper reviews some of basic methods of detecting and removing shadows, and outlines their disadvantages. Then taking Nanjing city as study area, we propose a novel method combing spatial-distribution relation with classification to detect building shadows from IKONOS imagery. When detecting and extracting shadows, a majority index based on neighborhood analysis is provided, and a 5-meter buffer analysis is operated after supervised classification. When removing the shadows, a piecewise linear contrast stretch and histogram match are used. The results show that the accuracy of shadows detection and extraction is 92.3%, but texture analysis is 88.1%, and the detail information within shadows regions is enhanced, and there are no bright edges around shadows regions by applying the techniques developed in this paper.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.