ABSTRACT
The Wuyi Mountain National Nature Reserve (WYS), established in 1979, is the largest and most intact subtropical forest ecosystem in southeastern China. No study has assessed the vegetation coverage change along with its ecological effect after the protection of the reserve for almost 40 years. In this study, the NDVI data of Landsat Image was corrected using the NDVI data of MODIS, the fractional vegetation cover (FVC) and the remote sensing based ecological index (RSEI) were calculated to assess the change of FVC and ecological quality in WYS with five Landsat images representing a period from 1979 to 2017. The results showed that after protection for nearly 40 years the FVC of the reserve had been significantly increased from 73.6% in 1979 to 89.5% in 2017, which consequently improved ecological quality from 0.801 in 1988 to 0.823 in 2017. In 2017, the area with the good and excellent ecological quality grades accounted for 98.7% of the total. Spatially, the ecologically-improved areas mainly distributed in the northeast core area and the center of the southwest core area. The ecologically-declined areas mostly occurred along roadsides and peaks. Vertically, the highest FVC and ecological quality areas distributed in the elevations between 1300-1900 m. In general, the improvement of FVC and ecological quality in the Wuyi Mountain National Nature Reserve was due largely to the effective policies and the successful protection by local government and people, except for some special year that may be affected mainly by climate conditions.
Subject(s)
Ecosystem , Remote Sensing Technology , China , Climate , Environmental MonitoringABSTRACT
Remote sensing change detection based on fractional vegetation cover (FVC) has become an important way in the research of vegetation and related ecosystems. It is difficult to meet the requirement for optical remote sensing in subtropical areas because of cloudy/rainy weather conditions. Using images from different seasons in the vegetation change detection will inevitably lead to errors in the change detection results due to the seasonal difference. To overcome this problem, we proposed a method for correcting vegetation seasonal variations by taking advantage of high temporal resolution advantage of MODIS remote sensing data and the high spatial resolution of remote sensing data. Based on the relationship between MODIS vegetation data in different seasons via regression analysis, we transformed the vegetation information of the high resolution images of corresponding years to the required season of the years. The method was applied in the Aojiang basin area of Lianjiang County in Fujian Province, China, with good results of vegetation information transformation. The results showed that after transforming vegetation information of the 2007 winter scene and 2013 spring scene of high resolution images to those of summer season, the FVC was enhanced from 66.5% to 79.7% for 2007, and from 58.6% to 77.9% for 2013. Our method effectively removed the seasonal difference of FVC and improved the accuracy of the FVC-based change detection results.
Subject(s)
Climate , Environmental Monitoring , China , Ecosystem , SeasonsABSTRACT
The urban spatial expansion has led to the considerable substitution of natural vegetation-dominated land surfaces by impervious surfaces, especially in large cities, with great impacts on urban ecological quality. Two most heavily populated cities, Shanghai of China and New York of USA, were chosen as the study cases. Based on Landsat images obtained in 1989, 2002, 2015 in Shanghai and in 1991, 2001, 2015 in New York, normalized difference impervious surface index (NDISI) was used to extract impervious surface (IS) information. The remote sensing based ecolo-gical index (RSEI) was then applied to evaluate the changes of urban ecological quality caused by the increased impervious surface. Furthermore, landscape pattern indices were used to analyze the differences of spatial structure of impervious surface between Shanghai and New York and their influences on urban ecological quality. The results showed a significant difference in urban expansion rate and pattern between Shanghai and New York from the early 1990s to 2015. The IS expansion area in Shanghai was 17.4 times as much as that in New York. The annual IS increase rate of Shanghai was 62.2 times as much as that of New York. Shanghai had experienced an expansion from urban center to the surrounding countryside in a concentric ring pattern, whereas New York showed no much expansion but had IS increase mainly within the inner city through space filling pattern. These differences in IS change rate and spatial distribution pattern had resulted in the difference in urban ecological quality of the two cities. The mean RSEI in Shanghai dropped from 0.717 in 1989 to 0.453 in 2015, with a decrease of 36.8%. In contrast, the RSEI of New York had a decline of 6.9% from 0.552 in 1991 to 0.514 in 2015. The poor ecological condition urban area tended to have large IS patches that were well connected and aggregated.
Subject(s)
Ecology , Environmental Monitoring , China , Cities , New YorkABSTRACT
This paper proposed a vegetation health index (VHI) to rapidly monitor and assess vegetation health status in soil and water loss region based on remote sensing techniques and WorldView-2 imagery. VHI was constructed by three factors, i.e., the normalized mountain vegetation index, the nitrogen reflectance index and the reflectance of the yellow band, through the principal component transformation, in order to avoid the deviation induced by subjective method of weighted summation. The Hetian Basin of Changting County in Fujian Province, China, was taken as a test area to assess the vegetation health status in soil and water loss region using VHI. The results showed that the VHI could detect vegetation health status with a total accuracy of 91%. The vegetation of Hetian Basin in good, moderate and poor health status accounted for 10.1%, 49.2% and 40.7%, respectively. The vegetation of the study area was still under an unhealthy status because the soil was poor and the growth of newly planted vegetation was not good in the soil and water loss region.
Subject(s)
Soil , Water , China , Plants , Remote Sensing TechnologyABSTRACT
Since China's reform and opening-up, the rapid growth of China's economy has greatly accelerated the expansion of built-up land, which has affected regional ecological environment to a great extent. Taking Jinjiang County of Fujian Province, one of the fastest economic-developing counties in the coastal areas of southeastern China, as a case study area, this paper focused on analyzing the rapid built-up land expansion process of the county and its impact on county's ecological quality using remote sensing techniques. Based on two Landsat images of 1996 and 2015 of Jinjiang, the built-up land of the county was extracted using the index-based built-up index (IBI) and its change was analyzed. In the meantime, the ecological status of Jinjiang was evaluated with a recently-proposed remote sensing based ecological index (RSEI) and the relationship between the built-up land dynamics and the ecological status changes of the county was quantitatively examined. The results showed that during the period from 1996 to 2015, the area of built-up land of Jinjiang had a net increase of 68.54 km2, a growth of 45%, and the expansion intensity was 0.55. The expansion of the built-up lands has caused overall degradation of the county's ecological quality. The mean value of RSEI of the county had declined from 0.532 in 1996 to 0.460 in 2015, a drop of13.5%. The area proportion of high ecological-quality grades also significantly fell from 39% in 1996 to 21% in 2015. The built-up land expansion intensity was negatively correlated with the ecological quality change.
Subject(s)
Conservation of Natural Resources , Ecosystem , China , EcologyABSTRACT
The satellite thermal infrared image has been an important data source for the acquisition of the earth's surface temperature. The thermal infrared sensor (TIRS) Landsat 8 satellite newly launched onboard has added valuable data for this mission. However, the calibration parameters for the two bands of the TIRS, i.e., TIRS Bands 10 and 11, had been modified several times since its launch. This finally led the United States Geological Survey (USGS) to reprocess all achieved Landsat 8 data starting from February 2014. In order to examine the calibration accuracy of the reprocessed TIRS data, this paper crossly compares Landsat 8 TIRS data with synchronized, well-calibrated Landsat 7 ETM+thermal infrared data. A total of three date-coincident image pairs of western United States, downloaded from USGS Earth Explorer website, were used for the cross comparison. Three test sites were selected respectively from the three image pairs for the comparison, which representing moderate vegetation-cover area (test site 1), low vegetation-cover area (test site 2), and bare soil area (test site 3). The thermal infrared data of the three image pairs of both sensors had been firstly converted to at-sensor temperature. A band-by-band comparison and a regression analysis were then carried out to investigate the relationship and difference between the two sensor thermal data. The results show a very high degree of agreement between the three compared Landsat 8 TIRS and Landsat 7 ETM+thermal infrared image pairs because the correlation coefficients between the retrieved at-sensor temperature of the two sensors are generally greater than 0.95. Nevertheless, the cross comparison also reveals differences between the thermal infrared data of the two sensors. Compared with retrieved at-sensor temperature of Landsat 7 ETM+Band 6, TIRS Band 10 shows an overestimation, which can be up to 1.37 K, whereas TIRS Band 11 underestimates the temperature, with a difference reaching to -3 K. This suggests that in spite of the reprocessing of Landsat 8 thermal infrared data, the calibration parameters for the satellite's TIRS data are still unstable, especially for TIRS Band 11. It was found that the at-sensor temperature difference between ETM+Band 6 and TIRS Band 10 was enhanced with the decrease in vegetation coverage from test site 1 to test site 3. The at-sensor temperature difference of test site 1 is 0.07 K and increased to 1.37 K in test site 3, a net increase by 1.3 K. While the at-sensor temperature difference between ETM+Band 6 and TIRS Band 11 had an inverse performance. With the decrease in vegetation coverage from test site 1 to test site 3, the at-sensor temperature difference was reduced from ~-3.0 to -0.4 K. Therefore, in bare soil dominated test site 3, the temperature difference was 1.37 K for TIRS Band 10 and -0.4 K for TIRS Band 11. The RMSE of TIRS Band 11 is also much lower than that of TIRS Band 10. This suggests that TIRS Band 11 can perform batter in bare soil area than TIRS Band 10 though the latter shows an overall batter performance than TIRS Band 11. The study also found that in low vegetation cover areas like in test sites 2 and 3, taking an averaged at-sensor temperature of TIRS Bands 10 and 11, the difference between the two sensors' at-sensor temperature can be reduced to less than -0.5 K.
ABSTRACT
The retrieval of impervious surface is a hot topic in the remote sensing field in the past decade. Nevertheless, studies on retrieving impervious surface from hyperspectral image and the comparison of the performances in retrieving impervious surface between hyperspectral and multispectral images are rarely reported. Therefore, The present paper focuses on the characteristics of hyperspectral (EO-1 Hyperion) and multispectral (Landsat TM/ETM+) images and implements a complementary study on the comparison based on the retrieved impervious surface information between Hyperion and TM/ETM+ data. For up to 242 bands of Hyperion image, a further study was carried out to select feature bands for impervious surface retrieving using stepwise discriminant analysis. As a result, 11 feature bands were selected and a new image named Hyperion' was thus composed. The new Hyperion' image was used to investigate whether this band-reduced image could obtain higher accuracy in retrieving impervious surface. The three test regions were selected from Fuzhou, Guangzhou and Hangzhou of China, with date-coincident or nearly coincident image pairs of the used sensors. The linear spectral mixture analysis (LSMA) was employed to retrieve impervious surface and the results were accessed for their accuracy. The comparison shows that the Hyperion image has higher accuracy than TM/ETM+, and the Hyperion' composed of the selected 11 feature bands has the highest accuracy. The advantages of Hyperion in spectral and radiometric resolutions over TM/ETM+ are believed to be the main factors contributing to the higher accuracy. The high spectral and radiometric resolutions of Hyperion image allow the sensor to have higher sensitivity in distinguishing subtle spectral changes of ground objects. While, the highest accuracy the 11-band Hyperion' image achieved is owing to the significant reduction of the band dimension of the image and thus the band redundancy.
ABSTRACT
The present paper investigates the quantitative relationship between the NDVI and SAVI vegetation indices of Landsat and ASTER sensors based on three tandem image pairs. The study examines how well ASTER sensor vegetation observations replicate ETM+ vegetation observations, and more importantly, the difference in the vegetation observations between the two sensors. The DN values of the three image pairs were first converted to at-sensor reflectance to reduce radiometric differences between two sensors, images. The NDVI and SAVI vegetation indices of the two sensors were then calculated using the converted reflectance. The quantitative relationship was revealed through regression analysis on the scatter plots of the vegetation index values of the two sensors. The models for the conversion between the two sensors, vegetation indices were also obtained from the regression. The results show that the difference does exist between the two sensors, vegetation indices though they have a very strong positive linear relationship. The study found that the red and near infrared measurements differ between the two sensors, with ASTER generally producing higher reflectance in the red band and lower reflectance in the near infrared band than the ETM+ sensor. This results in the ASTER sensor producing lower spectral vegetation index measurements, for the same target, than ETM+. The relative spectral response function differences in the red and near infrared bands between the two sensors are believed to be the main factor contributing to their differences in vegetation index measurements, because the red and near infrared relative spectral response features of the ASTER sensor overlap the vegetation "red edge" spectral region. The obtained conversion models have high accuracy with a RMSE less than 0.04 for both sensors' inter-conversion between corresponding vegetation indices.
Subject(s)
Environmental Monitoring/methods , Plants , Remote Sensing Technology , Models, Theoretical , Satellite CommunicationsABSTRACT
Up to present, no study has been published with respect to the cross-comparison between ASTER and Landsat-7 ETM+ imagery. Therefore, the present paper has implemented the complementary study on the images between these two sensors. The study firstly conducted the sensors characteristics comparison, including orbit characteristic, sensor scanning mode and imagery spectral characteristic. Further comparison was implemented to get the relation equations between corresponding VNIR and SWIR bands of these two sensors based on the apparent reflectance of the three pairs of synchronization images and large common ground regions. The validation has been done to verify the effectiveness of the proposed corresponding bands relation equations and matching coefficients. The result shows that the provided relation equations have high accuracy.
ABSTRACT
The water's Inherent Optical Properties (IOPs), including absorption and scattering coefficients of water components, are the essential parameters for bio-optical model and retrieval of water quality using the semi-analytical method. Nevertheless, the application of the bio-optical model in river water studies is still very rare. Therefore, taking the lower Jinjiang River of Fujian, SE China as an example, this study measured and calculated the bio-optical properties of river water and concentrations of optically active substances based on in situ water samples collected from river in 2007. It shows that R(0(-))753, R(0(-))702/R(0(-))680 and R(0(-))670/R(0(-))423 can be used to estimate total suspended solids (TSS) concentration, phytoplankton pigment (PP) concentration and the CDOM absorption at 440 nm, respectively. The determination coefficients (R2) of the retrieval model of TSS, PP and CDOM are 0.953, 0.8205 and 0.6213, respectively. The corresponding relative errors of the models (RE) are 6.1%, 21.87% and 22.18%. The results show that the model for estimating TSS can achieve the highest accuracy, the PP-estimating model has the second highest accuracy and the CDOM-estimating model has the lowest. The relatively lower concentration of phytoplankton pigments, narrow characterized spectral range of CDOM and influence of CDOM's R(0(-)) by TSS and PP within this spectral range contributed to their relatively lower accuracy.
Subject(s)
Eutrophication , Fresh Water/analysis , Models, Theoretical , Water Pollution/analysis , China , Environmental Monitoring/methods , Optics and Photonics , Photochemistry , Phytoplankton/growth & development , Rivers , Water Pollutants, Chemical/analysisABSTRACT
Three synchronal data collected on 2006-09-18 have been used in the study of the suspended solid concentration (SSC) of the lower Min River, which are in situ sampled water data, field-spectrometer measured spectral data and Landsat TM spectral data. Two models for predicting SSC have been proposed, one of which is based on field-spectrometer measured data and the other is on Landsat TM data. The statistical analysis of the field-spectroreter measured data has revealed that the reflectance of the SSC at the 690 nm has the strongest correlation with the in situ-sampled SSC data. The regression model can be expressed as SS = 116.2 (R690/R530) - 33.4. Furthermore, the model built upon the ratio of the reflectance at 690 nm to 530 nm has the best fitness with the in situ sampled SSC data. While the best predicting model for the Landsat TM data is achieved using the band combination of (TM2 + TM3)2 and is defined as SS = 3793.7 (R(TM3) + R(TM2)2 - 16.5. The assessment of the two models shows that the model on the field-spectrometer data has higher accuracy than that on the Landsat TM data but the difference is not big. This suggests that the Landsat TM data are still valuable in the prediction of the SSC if the field-spectrometer data are not available. Consequently, the predicting model based on the Landsat data has been applied in the study of the SSC of the lower Min River. The result shows that the model can efficiently reveal the SSC with its spatial distributional pattern features.
Subject(s)
Environmental Monitoring/methods , Fresh Water/analysis , Satellite Communications , Water Pollutants/analysis , China , Models, Theoretical , Particle Size , Rivers , Water Pollutants/chemistryABSTRACT
World-wide urbanization has significantly modified the landscape, which has important climatic implications across all scales due to the simultaneous removal of natural land cover and introduction of urban materials. This resulted in a phenomenon known as an urban heat island (UHI). A study on the UHI in Xiamen of China was carried out using remote sensing technology. Satellite thermal infrared images were used to determine surface radiant temperatures. Thermal remote sensing data were obtained from band 6 of two Landsat TM/ETM+ images of 1989 and 2000 to observe the UHI changes over 11-year period. The thermal infrared bands were processed through several image enhancement technologies. This generated two 3-dimension-perspective images of Xiamen's urban heat island in 1989 and 2000, respectively, and revealed heat characteristics and spatial distribution features of the UHI. To find out the change of the UHI between 1989 and 2000, the two thermal images were first normalized and scaled to seven grades to reduce seasonal difference and then overlaid to produce a difference image by subtracting corresponding pixels. The difference image showed an evident development of the urban heat island in the 11 years. This change was due largely to the urban expansion with a consequent alteration in the ratio of sensible heat flux to latent heat flux. To quantitatively compare UHI, an index called Urban-Heat-Island Ratio Index (URI) was created. It can reveal the intensity of the UHI within the urban area. The calculation of the index was based on the ratio of UHI area to urban area. The greater the index, the more intense the UHI was. The calculation of the index for the Xiamen City indicated that the ratio of UHI area to urban area in 2000 was less than that in 1989. High temperatures in several areas in 1989 were reduced or just disappeared, such as those in old downtown area and Gulangyu Island. For the potential mitigation of the UHI in Xiamen, a long-term heat island reduction strategy of planting shade trees and using light-colored, highly reflective roof and paving materials should be included in the plans of the city planers, environmental managers and other decision-makers to improve the overall urban environment in the future.
