Jay Gao

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.

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.

Quantification of grassland properties: how it can benefit from geoinformatic technologies?

The emergence of advanced geoinformatic techniques raises the feasibility of successfully quantifying grassland properties. Accurate quantification of these parameters faces opportunities and challenges, both of which are reviewed critically in this paper. The principle of quantifying grassland properties is presented first, together with the requirements, followed by a review of the grassland properties (percentage grass cover, grassland biomass and grassland degradation) that have been quantified. Assessment of quantification accuracy has evolved from reliance on the R 2 value of regression analysis to comparison against independent samples, with the highest accuracy being 89%. Achievement of higher accuracy is hindered by three obstacles, namely positional uncertainty of in situ samples, differential ground and image sampling intervals, and temporal irreversibility of historical satellite images. It is proposed that the global positioning system (GPS) be used to handle the first challenge, and hyperspatial resolution images to minimize disparity in the sampling intervals. The third challenge should be tackled through radiometric calibration of historic images based on invariant ground targets. With the emergence of hyperspectral imagery (e.g. AVIRIS and CASI), more grassland features (e.g. grassland productivity and carrying capacity) can be quantified in the future in a geographic information system (GIS). It is concluded that advances in the geoinformatic technology will enable more grassland properties to be quantified more accurately.

Mapping of land degradation from space: a comparative study of Landsat ETM+ and ASTER data

The purpose of this study is to compare the role of spectral and spatial resolutions in mapping land degradation from space‐borne imagery using Landsat ETM+ and ASTER data as examples. Land degradation in the form of salinization and waterlogging in Tongyu County, western Jilin Province of northeast China was mapped from an ETM+ image of 22 June 2002 and an ASTER image recorded on 24 June 2001 using supervised classification, together with several other land covers. It was found that the mapping accuracy was achieved at 56.8% and higher for moderately degraded (e.g. salinized) farmland, and over 80% for severely degraded land (e.g. barren) from both ASTER and ETM+ data. The spatial resolution of the ASTER data exerts only a negligible effect on the mapping accuracy. The 30 m ETM+ outperforms the ASTER image of both 15 m and 30 m resolution in consistently generating a higher overall accuracy as well as a higher users accuracy for barren land. The inferiority of ASTER data is attributed to the highly repetitive spectral content of its six shortwave infrared bands. It is concluded that the spectral resolution of an image is not as important as the information content of individual bands in accurately mapping land covers automatically.

A comparative study on the heavy metal solidification/stabilization performance of four chemical solidifying agents in municipal solid waste incineration fly ash.

Investigated in this paper were the content, specification distribution, and risk assessment code (RAC) determination of six targeted heavy metals and potentially toxic metals in fly ashes from a municipal solid waste incinerator in China. Contained in it is a comparison of the solidification/stabilization performance of two novel solidifying agents of sixthio guanidine acid (SGA) and tetrathio bicarbamic acid (TBA) with sodium dimethyldithiocarbamate (SDD) and Na2S, and analysis of their leachability in accordance with TCLP 1311 of the US EPA and the extraction procedures of China (HJ/T 299-2007 and HJ/T300-2007). The total concentration of Zn, Cu, Ni, Pb, Cr, Cd is 37383.47, 3080.77, 1583.92, 1356.43, 566.15, and 77.83 mg/kg, respectively. Cr (3.7%) and Pb (7.50%) pose low risk; and Ni (12.93%) and Zn (15.45%) have a medium risk; while Cu (69.84%) and Cd (82.5%) have a very high risk according to their RAC score. Compared with SDD and Na2S, SGA and TBA show an excellent overall solidifying performance due to their multiply hydrosulfide groups that bind with heavy metals very efficiently. The obtained results indicate that the leaching content of Cd, Ni, Pb and Zn is higher than the thresholds prescribed in GB5085.3-2007, and the excessive acetic acid makes its binding capacity stronger in HJ/T 300-2007 than in TCLP 1311.

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.

Restoration Prospects for Heitutan Degraded Grassland in the Sanjiangyuan

In many ecosystems ungulates have coexisted with grasslands over long periods of time. However, high densities of grazing animals may change the floristic and structural characteristics of vegetation, reduce biodiversity, and increase soil erosion, potentially triggering abrupt and rapid changes in ecosystem condition. Alternate stable state theory provides a framework for understanding this type of dynamic. In the Sanjiangyuan atop the Qinghai-Tibetan plateau (QTP), grassland degradation has been accompanied by irruptions of native burrowing animals, which has accentuated the loss of ground cover. Severely degraded areas of alpine meadows are referred to as ‘Heitutan’. Here, using the framework of alternate stable state theory, we describe the proximate and ultimate drivers of the formation of Heitutan on the QTP, and we assess prospects for recovery, in relation to the degree of biophysical alteration, of these alpine meadows. Effective rehabilitation measures must address the underlying causes of degradation rather than their symptoms. Heitutan degradation is not uni-causal. Rather it reflects different mechanisms operating at different spatio-temporal scales across this vast region. Underlying causes include overly aggressive exploitation of the grasslands (e.g. overgrazing), amplification of grazing and erosion damage by small mammals when outbreaks occur, and/or climate change. Given marked variability in environmental conditions and stressors, restorative efforts must vary across the region. Restoration efforts are likely to yield greatest success if moderately and severely degraded areas are targeted as the first priority in management programmes, before these areas are transformed into extreme Heitutan.

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).