Yong Zha

Use of normalized difference built-up index in automatically mapping urban areas from TM imagery

Remotely sensed imagery is ideally used to monitor and detect land cover changes that occur frequently in urban and peri-urban areas as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into land cover map using the existing methods of manual interpretation and parametric image classification digitally. In this paper we propose a new method based on Normalized Difference Built-up Index (NDBI) to automate the process of mapping built-up areas. It takes advantage of the unique spectral response of built-up areas and other land covers. Built-up areas are effectively mapped through arithmetic manipulation of re-coded Normalized Difference Vegetation Index (NDVI) and NDBI images derived from TM imagery. The devised NDBI method was applied to map urban land in the city of Nanjing, eastern China. The mapped results at an accuracy of 92.6% indicate that it can be used to fulfil the mapping objective reliably. Compared with the maximum likelihood classification method, the proposed NDBI is able to serve as a worthwhile alternative for quickly and objectively mapping built-up areas.

Eutrophication of Lake Waters in China: Cost, Causes, and Control

Lake water eutrophication has become one of the most important factors impeding sustainable economic development in China. Knowledge of the current status of lake water eutrophicatoin and determination of its mechanism are prerequisites to devising a sound solution to the problem. Based on reviewing the literature, this paper elaborates on the evolutional process and current state of shallow inland lake water eutrophication in China. The mechanism of lake water eutrophication is explored from nutrient sources. In light of the identified mechanism strategies are proposed to control and tackle lake water eutrophication. This review reveals that water eutrophication in most lakes was initiated in the 1980s when the national economy underwent rapid development. At present, the problem of water eutrophication is still serious, with frequent occurrence of damaging algal blooms, which have disrupted the normal supply of drinking water in shore cities. Each destructive bloom caused a direct economic loss valued at billions of yuan. Nonpoint pollution sources, namely, waste discharge from agricultural fields and nutrients released from floor deposits, are identified as the two major sources of nitrogen and phosphorus. Therefore, all control and rehabilitation measures of lake water eutrophication should target these nutrient sources. Biological measures are recommended to rehabilitate eutrophied lake waters and restore the lake ecosystem in order to bring the problem under control.

Assessment of grassland degradation near Lake Qinghai, West China, using Landsat TM and in situ reflectance spectra data

The severity of grassland degradation near Lake Qinghai, West China was assessed from a Landsat Thematic Mapper (TM) image in conjunction with in situ samples of per cent grass cover and proportion (by weight) of unpalatable grasses (PUG) collected over 1u2009m2 sampling plots. Spectral reflectance at each sampling plot was measured with a spectrometer and its location determined with a Global Positioning System (GPS) receiver. After radiometric calibration, the TM image was geometrically rectified. Ten vegetation indices were derived from TM bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding most closely to those of TM3 and TM4. Regression analyses showed that NDVI and SAVI are the most reliable indicators of grass cover and PUG, respectively. Significant relationships between TM bands-derived indices and in situ sampled grass parameters were established only after the former had been calibrated with in situ reflectance spectra data. Through the established regression models the TM image was converted into maps of grass cover parameters. These maps were merged to form a degradation map at an accuracy of 91.7%. It was concluded that TM imagery, in conjunction with in situ grass samples and reflectance spectra data, enabled the efficient and accurate assessment of grassland degradation inside the study area.

Validation of a Quasi-Analytical Algorithm for Highly Turbid Eutrophic Water of Meiliang Bay in Taihu Lake, China

In many hydrological studies and applications, it is desirable to know the absorption property of water bodies. In order to derive this inherent optical property of waters from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was calibrated and validated for the highly turbid water of Taihu Lake in China. A data set collected on November 8, 2007, from Meiliang Bay of Lake Taihu was first used to calibrate a regional QAA algorithm for this area, and other two independent data sets, which were collected on August 22, 2006, and November 10, 2008, from the same area, were used to further validate the local algorithm. By shifting the reflectance wavelength from the red region to near infrared, the local QAA algorithm works well for this highly turbid water, the percent difference between the derived and measured absorption coefficients is less than 20% for all 13 samples in the data set of 2007, and most of them are less than 10%. The regional calibrated algorithm also has great result in deriving the absorption for the data set of 2008. However, the performance of the local algorithm also has various seasonal properties. It failed in deriving absorption for the data set of August 2006, unless the reference wavelength is shifted to even more long ones. It has been suggested in this paper that the seasonal and regional information is necessary for using the QAA algorithm in different optical property waters.

Specific absorption coefficient and the phytoplankton package effect in Lake Taihu, China

In order to investigate variations of absorption and total chlorophyll-a (TChl-a)-specific absorption coefficient of phytoplankton in Lake Taihu, 57 water samples obtained from Lake Taihu during November 8–22, 2007 were used in this study. Package effect and accessory pigments’ influences on the absorption spectra were also examined. Phytoplankton absorption was measured by quality filter technical, and TChl-a concentration was measured by “hot ethanol” method. Results yielded significant variations in phytoplankton absorption and TChl-a-specific absorption coefficient. Phytoplankton absorption coefficient at 675xa0nm is highly correlated to TChl-a concentration, while absorption at 440xa0nm is less correlated to TChl-a concentration because of great package effect and accessory pigments’ influence. There was an inverse relationship between aph*(λ) and TChl-a concentration. Four types of absorption spectra are identified by normalizing aph*(λ) to aph*(440). The aph*(λ) variation is mainly due to accessory pigments and package effect, whose influence at 675xa0nm ranges from 83.2% to 28%, with an average of 65.3%. Meanwhile, the wide varying ratio of aph*(440) to aph*(675) indicates a large variation range in the ratio of accessory pigment to TChl-a concentration. Those findings are significant to estimate Chl-a concentration based on bio-optical model, estimate primary production from remote sensing, and plan further ecological restoration measures for Lake Taihu.

Estimation of chlorophyll‐a concentration in Lake Tai, China using in situ hyperspectral data

Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll‐a (chl‐a) concentration. Twenty‐eight spectral reflectance curves were standardized and correlated with chl‐a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl‐a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R 667), chl‐a concentration was not accurately estimated at R 2 = 0.494. Accuracy of estimation was improved to R 2 = 0.817 using the maximum reflectance. A higher accuracy of 0.837 was achieved using the peak reflectance at 719 nm (R 719) because it does not drift with the level of chl‐a concentration. The highest accuracy of estimation was achieved at R 2 = 0.868 using R 719/R 667.

Assessment of the effectiveness of desertification rehabilitation measures in Yulin, north-western China using remote sensing

In China tremendous efforts have gone into the rehabilitation of desertified land for productive use such as grazing, crop cultivation and afforestation. In this study the effectiveness of these agro-ecological measures to combat desertification in Yulin, Shaanxi Province of north-western China, is evaluated from multi-temporal aerial photographs in a geographical information system (GIS). The trend of desertification between 1960 and 1987 is modelled from changes in other land covers. It is found that desertified areas have decreased by 717.91 ha during the study period as a result of rehabilitation efforts. Plantation of grass is the least effective measure for halting sand dune encroachment whereas the planting of shrubs is the most effective. The trend of desertification is most accurately modelled from the changes in grassland and farmland at an R 2 value of 0.90.

Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters

Remote sensors designed specifically for studying the atmosphere have been widely used to derive timely information on air pollution at various scales. Whether the satellite-generated aerosol optical thickness (AOT) data can be used to monitor air pollution, however, is subject to the effect of a number of meteorological parameters. This study analyses the influence of four meteorological parameters (air pressure, air temperature, relative humidity, and wind velocity) on estimating particulate matter (PM) from MODIS AOT data for the city of Nanjing, China during 2004–2006. After the PM data were correlated with the AOT data that had been divided into four chronological seasons, a minimum correlation coefficient of 0.47 was found for the winter season, but a much stronger correlation (r > 0.80) existed in summer and autumn. Similar analyses were carried out after all observations were clustered into four groups based on their meteorological similarity using the K-Means analysis. Grouping caused more observations to be useable in the monitoring of air pollution than season-based analysis. Of the four groups, three had a correlation coefficient higher than 0.60. Grouping-based analysis enables the pollution level to be determined more accurately from MODIS AOT data at a higher temperature and relative humidity, but a lower air pressure and wind velocity. The accuracy of monitoring air pollution is inversely related to the pollution level. Thus, remote sensing monitoring of air pollution has its limits.

Monitoring of SO2 column concentration change over China from Aura OMI data

The spatiotemporal variation in sulphur dioxide (SO2) concentration during 2005–2008 over China was monitored from the planetary boundary layer (PBL) SO2 column concentration retrieved from Aura ozone monitoring instrument (OMI) data, in this study. The obtained results indicate that SO2 concentration has an imbalanced spatial distribution with the highest level in central East China. Of the 31 provinces/municipalities/autonomous regions in mainland China, 23 still exhibited a rising trend of SO2 concentration during 2005–2008, against eight that showed a sign of decrease. The correlation coefficient between the mean SO2 concentration during 2005–2008 and coal consumption per unit area is as high as 0.760 if Shanghai is excluded from the analysis. With the exception of Beijing, all other provinces increased their coal consumption per unit area. Therefore, there is much more work to be done to bring down SO2 concentration.

Temporal filtering of successive MODIS data in monitoring a locust outbreak

The emergence of high temporal resolution satellite data such as MODIS enables timely monitoring of locust outbreaks from space. This monitoring is hampered by the effect of random atmospheric variations on satellite imagery, which may be suppressed through temporal filtering. This paper aims to evaluate the utility of temporally filtering successive MODIS data in monitoring an outbreak in East China. Of the eight vegetation indices examined, the commonly used NDVI was the most indicative of varying vegetation conditions caused by locust infestation inside the study area. The averaging of three successive days of satellite data improves the R 2 value of NDVI regression models by 0.227 over single‐day data. It also outperforms the data averaged from two successive days (a broader window size was not attempted due to the short span of the study period). Temporally, NDVI changed at varying rates daily during the outbreak. Early in the outbreak it increased at a reduced pace until 7.5 days. Afterwards it started to decrease at an accelerated rate. If temporally filtered with a proper window size, successive MODIS data allow the outbreak to be monitored accurately (R 2 = 0.696).