Asphalt road reflectance spectra change as pavement ages. This provides the possibility for remote sensing to be used to monitor a change in asphalt pavement conditions. However, the relatively narrow geometry of roads and the relatively coarse spatial resolution of remotely sensed imagery result in mixtures between pavement and adjacent landcovers (e.g., vegetation, buildings, and soil), increasing uncertainties in spectral analysis. To overcome this problem, multiple endmember spectral mixture analysis (MESMA) was used to map the asphalt pavement condition using Worldview-2 satellite imagery in this study. Based on extensive field investigation and in situ measurements, aged asphalt pavements were categorized into four stages—preliminarily aged, moderately aged, heavily aged, and distressed. The spectral characteristics in the first three stages were further analyzed, and a MESMA unmixing analysis was conducted to map these three kinds of pavement conditions from the Worldview-2 image. The results showed that the road pavement conditions could be detected well and mapped with an overall accuracy of 81.71% and Kappa coefficient of 0.77. Finally, a quantitative assessment of the pavement conditions for each road segment in this study area was conducted to inform road maintenance management.
The vegetation cover is a major feature for an ecological system, especially in arid and semi-arid areas. Percent
vegetation cover (PVC) is an integrated index that can indicate vegetation community dynamics. This study aims to use
MODIS and TM data to characterize the spatio-temporal dynamics of vegetation covers in Shihezi area, Xinjiang /
China. The 16-day composite NDVI of the second half July of 2001 to 2008 was extracted from the MODIS bands. The
land cover data was derived from TM data to get better spatial resolution. The dimidiate pixel model was applied to
estimate the PVC and the PVC images were classified into five grade categories based on the value of each pixel, and the
area for each category was also calculated. The results show that: 1) the area of the low vegetation covers and the middle
vegetation covers in the study area in July 2008 reduced 9.18% and 18.53%, respectively with respect to that of 2001
while the area of high vegetation covers is 0.66 times bigger than that of 2001; 2) although the fluctuation of the
vegetation covers was observed, the main trend indicates that the green vegetation cover has been recovered ecologically
from 2001 to 2008; 3) the natural precipitation has larger impact on the sparsely vegetated areas with a correlation
coefficient of 0.82 between the PVC and precipitation in the study area. Finally this case study also demonstrates the
usefulness of the MODIS data in the monitoring of vegetation cover dynamics and ecological rehabilitation.
A 'fused' method may not be suitable for reducing the dimensionality of data and a band/feature selection method needs
to be used for selecting an optimal subset of original data bands. This study examined the efficiency of GA in band
selection for remote sensing classification. A GA-based algorithm for band selection was designed deliberately in which
a Bhattacharyya distance index that indicates separability between classes of interest is used as fitness function. A binary
string chromosome is designed in which each gene location has a value of 1 representing a feature being included or 0
representing a band being not included. The algorithm was implemented in MATLAB programming environment, and a
band selection task for lithologic classification in the Chocolate Mountain area (California) was used to test the proposed
algorithm. The proposed feature selection algorithm can be useful in multi-source remote sensing data preprocessing,
especially in hyperspectral dimensionality reduction.
Ecological vulnerability evaluation has important real significance and scientific value. In this study, under the support of
Remote Sensing and Geographic Information System, we use TM images, distribution map of sand desertification and
soil salinization, and related geographic information, and adopt a combined landscape pattern and ecosystem sensitivity
approach to access the ecological vulnerability of Duerbete County. We consider the following five factors to develop the
model: (1) reciprocal of fractal dimension (FD'), (2) isolation (FI), (3) fragmentation (FN), (4) sensitivity of sand
desertification (SD), and (5) sensitivity of soil salinization (SA). Then we build the evaluation model and calculate the
vulnerability of landscape type of Duerbete. Through Kriging interpolation, we get the regional eco-environment
vulnerability of whole county. Then we evaluate this cropping-pastoral interlacing region-Duerbete County. The
conclusions are: (1) The vulnerability of all landscape types is in the following decreasing order: grassland > cropland >
unused area > water area > construction area > wattenmeer > reed bed > woodland > paddy field; (2) There are
significant positive relationships between VI and
FN, VI and SD, SD and FN, SA and FN. This suggests that FN and SD have considerable impact on the eco-environmental vulnerability; (3) With the combination of FN, SD and SA, the regional eco-environment vulnerability can be evaluated well. The result is reasonable and can
support ecological construction.
Ecological capital of an ecosystem is the total value of the direct biological products in the system and the value of ecological service. The assessment of ecological capital is a new research area emerged from the challenge in the interdisciplinary research of ecology and social development. It is fundamental to establish a green national economy accounting system. Scientific evaluation of ecological capital is helpful for considering ecological cost in making the decision for economic development, and it is demanded for sustainable development. In this study, a quantitative assessment model of ecological property has been developed based on the analysis of per unit yield in the conventional ecology together with the utilization of remote sensing data from the Landsat TM, CBERS, MODIS, and NOAA database, land use and land cover data, and field measurements. The study area covers Changji Autonomous District, Xinjiang, China on the northern slope of Tianshan Mountain that is located in a typical arid area. Dynamic monitoring of ecological capital was performed using remote sensing techniques. Spatial distribution and temporal variation of ecological properties were characterized. The effects of land cover and land use as well as climate change on those variation and distribution were analyzed. The results show a significant increase in the ecological capital during 1990-2003. The spatial distribution of ecological properties is characterized by a negative gradient from higher altitudes to lower altitudes (plains) and from oases to deserts, which is consistent with the zonal distribution of vegetation in arid areas. Due to global warming, the climate in Xinjiang has been changed into a warmer and wetter environment during the last 50 years, which improves the plant growing conditions in the alpine regions, piedmont hilly regions, and the oases. On the other hand, the natural environment in the arid and semiarid regions in northwest China becomes more severe, and the stress to the natural ecosystems becomes more and more serious. Human activities affect the quality and the area of ecosystems and change the service functions of ecosystems. Consequently the fluctuation of ecological capital occurs.
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