This study analyzes the change of Normalized Difference Vegetation Index (NDVI) and precipitation for forest in
different ecological zones in China and their correlation over the period of 1982-2006. The specific aim of this paper was
to identify the changing trends of NDVI and precipitation and understand their relations, especially, on which duration
the precipitation influence NDVI strongly during growing season of forest in different ecological aspects. The results
showed that 1) the break points of NDVI and precipitation appeared in different years in most ecological zones, but in
temperate continental forest and temperate mountain system, they have a high degree of consistency; 2) the NDVI in
boreal coniferous forest, temperate mountain system and tropical moist deciduous forest showed a increasing trend
during 1982-2006 and the lowest value were appeared in different time and the precipitation in boreal coniferous forest
and temperate mountain system showed a decreasing trend; 3) the forest in different ecological zones has different
patterns with different periods and lags and the peak value of pearson correlation coefficients were showed in different
duration and lag, and NDVI and precipitation generally have the negative but weak relation.
Forest cover maps are essential for current researches of biomass estimation and global change, but traditional methods
to derive forest maps are complex. These methods usually need training samples or other ancillary data as input, and are
time- and labor- consuming for large scale applications. To make the process of forest cover mapping simple and rapid,
in this paper, a simple spectral index called forest index (FI) was proposed to highlight forest land cover in Landsat
scenes. The FI is derived from three bands, green, red and near-infrared (NIR) bands and an FI image can be classified
into forest/non-forest map with a threshold. The overall accuracies of classification maps in the two study areas were
97.8% and 96.2%, respectively, which indicates that the FI is efficient at highlighting forest cover.
Knowing the inherent optical properties (IOPs) of water bodies is useful for many water environment studies and applications. To derive the IOPs from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was modified and validated for the highly turbid Poyang Lake in China. In order to supplement and expand the dynamic variation range of the measured water optical properties, a Hydrolight simulated dataset was generated to develop a regional QAA (QAA710) for this area. The QAA710 model was then validated with simulated data, simulated data with random noise, and in situ data. The results show that the effects of random noise (within ±20%) of remote-sensing reflectance on the derived total absorption coefficients (at) and the particulate backscattering coefficients (bbp) by the QAA710 model are insignificant (a band-averaged mean relative error of 4.1% and 12.0%, respectively). The validation of in situat shows a 28.6% mean relative difference. The model process, modeling data, and validation data introduce uncertainties into the derived results. These analyses demonstrate that the QAA710 model, based on the characterization of local environments, performs well in retrieving Poyang Lake’s IOPs.
C factor, known as cover and management factor in USLE, is one of the most important factors since it represents the
combined effects of plant, soil cover and management on erosion, whereas it also most easily changed variables by men
for it itself is time-variant and the uncertainty nature. So it's vital to compute C factor properly in order to model erosion
effectively. In this paper we attempt to present a new method for calculating C value using Vegetation Index (VI)
derived from multi-temporal MODIS imagery, which can estimate C factor in a more scientific way. Based on the theory
that C factor is strongly correlated with VI, the average annual C value is estimated by adding the VI value of three
growth phases within a year with different weights. Modified Fournier Index (MFI) is employed to determine the weight
of each growth phase for the vegetation growth and agricultural activities are significantly influenced by precipitation.
The C values generated by the proposed method were compared with that of other method, and the results showed that
the results of our method is highly correlated with the others. This study is helpful to extract C value from satellite data
in a scientific and efficient way, which in turn could be used to facilitate the prediction of erosion.
Spatial autocorrelation has been proved to be a useful tool in many fields, including spatial heterogeneity research and spatial structure investigation. With the increasing of remote sensors, images of different resolutions are being acquired and put into usage. So how to select images of appropriate spatial resolution becomes to be a great challenge. Therefore, it's necessary to investigate the scale dependence of the spatial autocorrelation in remotely sensed images, as Jupp et al (1989) has declared that the spatial autocorrelation in an image is related with the spatial resolution. In this paper, panchromatic band of the QuickBird imagery is aggregated into a series of images of coarser spatial resolution and used to investigate the scaling effects. Both global and local spatial autocorrelation measures at different scales are calculated.
Results show that global autocorrelation increases as the resolution becomes coarser and lag distance decreases. Local autocorrelation shows dependence on scale and the land cover type. It's necessary to combine global and local measures together to explore the intrinsic of spatial autocorrelation.
Spatial database is an essential component of Geographic Information System (GIS). With the development of modern remote sensors and data acquiring instruments, the amount of spatial data increases with geometric series. Retrieval required data in such massive database is a challenging issue to database engineers. Therefore, building efficient index is significant to spatial database. In this paper, bitmap index technology, which is rarely used in spatial database, is taken into consideration. In this paper, TM/ETM+ images covered main land of China are selected to establish a spatial database. In order to rapidly inquire and retrieval required data from the spatial database, an effective spatial index is very important. A bitmap index solution for original TM/ETM+ image is advanced in this paper. The bitmap index schema, which indexes field by building "0" and "1" binary bit vectors, is designed based on analyzing its principle and
applicable conditions. In the image database, the strategy is programmed to implementation and applied for data retrieval.
So that complex querying operations can be transformed to bitwise logical operations. A users' interface is developed based on building such bitmap index for original TM/ETM+ images database. Then the paper probes into the bitmap index update mechanism to address problems resulted from inserting and deleting images operation. In order to address the problem of high cardinality, an encoded bitmap index technology is proposed as well. At last, a simple comparison
and efficiency analysis is carried out to illuminate its applicability.
In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support
system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a
detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP)
help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total
distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of
nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial
bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact
algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic
algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming
and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based
on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is
tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system
algorithm is an effective and efficient method for solving MDVSP problems.
As wetland has been recognized as an important component of ecosystem, it is received ever-increasing attention worldwide. Poyang Lake wetlands, the international wetlands and the largest bird habitat in Asia, play an important role in biodiversity and ecologic protection. However, with the rapid economic growth and urbanization, landscape patterns in the wetlands have dramatically changed in the past three decades. To better understand the wetland landscape dynamics, remote sensing, geographic information system technologies, and the FRAGSTATS landscape analysis program were used to measure landscape patterns. Statistical approach was employed to illustrate the driving forces.
In this study, Landsat images (TM and ETM+) from 1989 and 2000 were acquired for the wetland area. The landscapes in the wetland area were classified as agricultural land, urban, wetland, forest, grassland, unused land, and water body using a combination of supervised and unsupervised classification techniques integrated with Digital Elevation Model (DEM). Landscape indices, which are popular for the quantitative analysis of landscape pattern, were then employed to analyze the landscape pattern changes between the two dates in a GIS. From this analysis an understanding of the spatial-temporal patterns of landscape evolution was generated. The results show that wetland area was reduced while fragmentation was increased over the study period. Further investigation was made to examine the relationship between landscape metrics and some other parameters such as urbanization to address the driving forces for those changes. The urban was chosen as center to conduct buffer analysis in a GIS to study the impact of human-induced activities on landscape pattern dynamics. It was found that the selected parameters were significantly correlated with the landscape metrics, which may well indicate the impact of human-induced activities on the wetland landscape pattern dynamics and account for the driving forces.
Water environment is associated with many disciplinary fields including sciences and management which makes it difficult to study. Timely observation, data getting and analysis on water environment are very important for decision makers who play an important role to maintain the sustainable development. This study focused on developing a plateform of water environment management based on remote sensing and GIS technology, and its main target is to provide with necessary information on water environment through spatial analysis and visual display in a suitable way. The work especially focused on three points, and the first one is related to technical issues of spatial data organization and communication with a combination of GIS and statistical software. A data-related model was proposed to solve the data communication between the mentioned systems. The second one is spatio-temporal analysis based on remote sensing and GIS. Water quality parameters of suspended sediment concentration and BOD5 were specially analyzed in this case, and the results suggested an obvious influence of land source pollution quantitatively in a spatial domain. The third one is 3D visualization of surface feature based on RS and GIS technology. The Pearl River estuary and HongKong's coastal waters in the South China Sea were taken as a case in this study. The software ARCGIS was taken as a basic platform to develop a water environmental management system. The sampling data of water quality in 76 monitoring stations of coastal water bodies and remote sensed images were selected in this study.
In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed sensor with given spatial resolution are a mixture of soil and vegetation spectra, so vegetation covering on soil influences the accuracy of soils surveying by remote sensing. Mixed pixel spectra are described as a linear combination of endmember signature matrix with appropriate abundance fractions correspond to it in a linear mixture model. According to the principle of this model, abundance fractions of endmembers in every pixel were calculated using unsupervised fully constrained least squares(UFCLS) algorithm. Then the signature of vegetation correspond to its abundance fraction was eliminated, and other endmember signatures covered by vegetation were restituted by scaling their abundance fractions to sum the original pixel total and recalculating the model. After above processing, de-vegetated reflectance images were produced for soils surveying. The accuracies of paddy soils classified using these characteristic images were better than that of using the raw images, but the accuracies of zonal soils were inferior to the latter. Compared to many other image processing methods, such as K-T transformation and ratio bands, the linear spectral unmixing to removing vegetation produced slightly better overall accuracy of soil classification, so it was useful for soils surveying by remote sensing.
Image fusion is an important content for digital image processing. For the past research, the method would be good at either the low frequency information or the high frequency information. For example, the fusion method based on high-pass filter of wavelet transform (HPFWT) is good at retaining detail information, and the method based on local deviation of wavelet transform (LDWT) is specialize in preserving multi-spectral information. It would be great if the two methods are combined. Therefore, the paper combines local deviation and high-pass filter to fuse image. The result indicates that this method can improve the detail information comparing with LDWT, enhance the spectral information comparing with HPFWT.
The paper researches texture extraction using wavelet transform. After introducing the wavelet transform and the texture analysis methods, the image is decomposed by wavelet transform, and the sub-images are gained. Secondly, the paper takes entropy and mean as texture parameter, so the texture image is an entropy or mean image. Finally, the image is classified by the spectral and texture information. The size of the texture calculating window and the treatment to the sub-image are researched in this paper. On condition that the spectral classification adding with texture feature, the precision will improve 4% averagely. Wavelet transform can decomposed image at several levels, so it can provide many information to classify and extract, which is helpful to those applications. Because of the texture window, texture image has fuzzy edge, it will lead to error for the image that have fine object or the area with different objects interleaved.
The Pearl River estuary and Hong Kong's coastal waters were selected to study the ocean color categories related to water quality. Three ocean color sensitive parameters: turbidity, suspended sediments (SS) and chlorophyll-a concentration (Chl-a), in 58 monitoring stations were selected to evaluate the water quality. A dataset with 88 samples was picked up from the monitoring stations and the successfully retrieved points of SS and Chl-a from SeaWiFS, 66 of the 88 samples were used at training data and the other 22 as testing data. The normalized difference water index was extracted from the Landsat TM image on Dec. 22, 1998 and the threshold segmentation was used to retrieve the waters from the image for further analysis. The methods of maximum likelihood, neural network and support vector machine were employed for ocean color classification of the selected Landsat TM image. Five classes of water quality could be well interpreted for all the methods. The results showed spatial variation from the west turbid waters to the east relative clear waters and suggested that the turbid wsters could be well classified using Landsat TM data.
The Arabian Sea surface chlorophyll data derived from the Sea Wifs sensor during the post ENSO (1997-98) months analyzed with reference to its corresponding monthly climatic data. The monthly color response computed through differentiation and normalization with reference to its climatic value and represented in percentages. The trends observed in inter and intra-annual mode for the months of 1998 to 2000 analyzed in relation to similar observation made with sea surface temperature (SST) over the area. Analysis also carried out in relation with the changes in weather and climate over the area and the prevailing oceanic processes. The color response indicated about 30% change in surface chlorophyll over the period. The lower concetraiton of sruface chlorophyll observed in 1998 increased from 40% in 1999 to 60% in 2000, respectively. The annual change observed in ocean color response was about 23.7% in 1999 and 3.4% in 200 over their previous years. The average annual change in surface chlorophyll over the period 1998-2000 was about 11.32%, while peak rates of monthly change recorded to an extent of -38.6% in April 1998 to a maximum value in 40.07% in February 1999. Similar responses also observed in the SST of the area. The thermal response observed in the Arabian Sea was compared with those in the central and the eastern Pacific Ocean. These changes were attributed to the changes in ocean and atmospheric processes during summer and winter months, including that of 1997-98 ENSO phenomena.
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