Water quality monitoring in a slightly-polluted inland water body through remote sensing — Case study of the Guanting Reservoir in Beijing, China

This study focused on the water quality of the Guanting Reservoir, a possible auxiliary drinking water source for Beijing. Through a remote sensing (RS) approach and using Landsat 5 Thematic Mapper (TM) data, water quality retrieval models were established and analyzed for eight common water quality variables, including algae content, turbidity, and concentrations of chemical oxygen demand, total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus, and dissolved phosphorus. The results show that there exists a statistically significant correlation between each water quality variable and remote sensing data in a slightly-polluted inland water body with fairly weak spectral radiation. With an appropriate method of sampling pixel digital numbers and multiple regression algorithms, retrieval of the algae content, turbidity, and nitrate nitrogen concentration was achieved within 10% mean relative error, concentrations of total nitrogen and dissolved phosphorus within 20%, and concentrations of ammonia nitrogen and total phosphorus within 30%. On the other hand, no effective retrieval method for chemical oxygen demand was found. These accuracies were acceptable for the practical application of routine monitoring and early warning on water quality safety with the support of precise traditional monitoring. The results show that performing the most traditional routine monitoring of water quality by RS in relatively clean inland water bodies is possible and effective.     

Detecting deforestation with a spectral change detection approach using multitemporal Landsat data: a case study of Kinabalu Park, Sabah, Malaysia

Tropical deforestation is occurring at an alarming rate, threatening the ecological integrity of protected areas. This makes it vital to regularly assess protected areas to confirm the efficacy of measures that protect that area from clearing. Satellite remote sensing offers a systematic and objective means for detecting and monitoring deforestation. This paper examines a spectral change approach to detect deforestation using pattern decomposition (PD) coefficients from multitemporal Landsat data. Our results show that the PD coefficients for soil and vegetation can be used to detect deforestation using change vector analysis (CVA). CVA analysis demonstrates that deforestation in the Kinabalu area, Sabah, Malaysia has significantly slowed from 1.2% in period 1 (1973 and 1991) to 0.1% in period 2 (1991 and 1996). A comparison of deforestation both inside and outside Kinabalu Park has highlighted the effectiveness of the park in protecting the tropical forest against clearing. However, the park is still facing pressure from the area immediately surrounding the park (the 1km buffer zone) where the deforestation rate has remained unchanged.     

Landsat ETM+ Images in the Estimation of Seasonal Lake Water Quality in Boreal River Basins

We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (Z(SD)) in lakes of two river basins located in southern Finland. The ETM+ images were acquired in May, June, and September 2002 and were corrected for atmospheric disturbance using the simplified method of atmospheric correction (SMAC) model. The in situ measurements consisted of water sampling in the largest lake of the region, routine monitoring results for the whole study area, and Z(SD) observations made by volunteers. The ranges of the water quality variables in the dataset were as follows: turbidity, 0.6-25 FNU; absorption coefficient of CDOM at 400 nm, 1.0-12.2 m(-1); Z(SD), 0.5-5.5 m; and chlorophyll a concentration, 2.4-80 mug L(-1). The estimation accuracies of the image-specific empirical algorithms expressed as relative errors were 23.0% for turbidity, 17.4% for CDOM, and 21.1% for Z(SD). If concurrent in situ measurements had not been used for algorithm training, the average error would have been about 37%. The atmospheric correction improved the estimation accuracy only slightly compared with the use of top-of-atmospheric reflectances. The accuracy of the water quality estimates without concurrent in situ measurements could have been improved if in-image atmospheric parameters had been available. The underwater reflectance simulations of the ETM+ channel wavelengths using water quality typical for Finnish lakes (data from 1113 lakes) indicated that region-specific algorithms may be needed in other parts of the country, particularly in the case of Z(SD). Despite the limitations in the spectral and radiometric resolutions, ETM+ imagery can be an effective aid, particularly in the monitoring and management of small lakes (<1 km(2)), which are often not included in routine monitoring programs.     

Changes in Forest Area Along Stream Networks in an Agricultural Catchment of the Great Barrier Reef Lagoon

Scenes from the series of multispectral sensors on the Landsat satellites were used to map recent changes (between 1972 and 2004) in forest cover within and adjacent to stream networks of an intensively farmed region of the southern Great Barrier Reef catchment (Australia). Unsupervised ISODATA classifications of Tasseled-Cap transformed data (at 57 m ground resolution) mapped forest and cleared areas within 150 m of Pisoneer catchment waterways with 72.2% overall accuracy (K(hat) = 0.469), when adjusted for the size of each class. Although the user's accuracy was higher for the forest class (82.1 +/- 8.4% at alpha = 0.05), large errors of commission (34.2 +/- 8.3%) substantially affected map accuracy for the cleared class. The main reasons for misclassification include: (1) failure to discriminate narrowly vegetated riparian strips; (2) misregistration of scenes; and (3) spectral similarity of ground cover. Error matrix probabilities were used to adjust the mapped area of classes, resulting in a decline of forest cover by 12.3% and increase of clearing by 18.5% (22.4 km(2) change; 95% confidence interval: 14.3-29.6 km(2)) between 1972 and 2004. Despite the mapping errors, Landsat data were able to identify broad patterns of land cover change that were verified from aerial photography. Most of the forest losses occurred in open forest to woodland habitat dominated by Eucalyptus, Corymbia, and Lophostemon species, which were largely replaced by sugarcane cropping. Melaleuca communities were similarly affected, though they have a much smaller distribution in the catchment.     

Remote Assessment of Forest Health in Southern Arizona, USA: Evidence for Ozone-Induced Foliar Injury

This paper examines possible ozone-induced foliar injury to ponderosa pine areas in the Rincon Mountains of southern Arizona from 1972 to 1992. Spatiotemporal differences in a satellite-derived vegetation index (VI) are examined with respect to antecedent moisture conditions, temporal variations in ozone exposure levels, and measured foliar injury values from 1985. Seasonal ozone exposure levels (SUM60 and W126) increased from 1982 to 1998 and were significantly correlated (r = 0.49 and 0.53, α= 0.05) with annual population totals in the Tucson area. Extensive masking of satellite images from 1972, 1986, and 1992 resulted in two optimal change detection areas, with one site, TVWMica, exposed mostly to the Tucson air pollution plume, while the other site, EMica, was more protected from Tucson-derived pollution. An overall increase in VI from 1972 to 1992 at both sites appears to have been caused by an increase in moisture availability. Larger foliar injury values in 1985 were associated with a smaller increase in VI (i.e., a smaller increase in green leaf biomass) from 1972 to 1986. From 1972 to 1986 and from 1986 to 1992, VI values at TV/WMica increased at a slower rate compared to those at EMica. The reduced increase in “green-up” may have been caused partially by ozone-induced foliar injury and resulting decreases in green leaf biomass. However, these spatial differences in VI values may have also been caused by a number of other factors. Results nevertheless reveal the strong possibility of distinct, topographically based, spatial variations in ozone-induced foliar injury within the Rincons.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.     

Water quality assessment at Ömerli Dam using remote sensing techniques

Water quality at Omerli Dam, which is a vital potable water resource of Istanbul City, Turkey was assessed using the first four bands of Landsat 7-ETM satellite data, acquired in May 2001 and water quality parameters, such as chlorophyll-a, suspended solid matter, secchi disk and total phosphate measured at several measurement stations at Omerli Dam during satellite image acquisition time and archived at the Marine Pollution and Ecotoxicology laboratory of the Marmara Research Center, where this study was carried out. Establishing a relationship between this data, and the pixel reflectance values in the satellite image, chlorophyll-a, suspended solid matter, secchi disk and total phosphate maps were produced for the Omerli Dam.     

SCS URBAN CURVE NUMBERS FROM A LANDSAT DATA BASE1

Estimating the Curve Numbers used in the Soil Conservation Service hydrologic models is a tedious and costly task. Recent advances in remote sensing and data processing have led to the development of readily available land cover data bases for many areas of the United States. This study evaluated the potential of using a Landsat data base to make the Curve Number estimation process more cost-effective and less tedious. Ten watersheds in the Washington, D.C., area were evaluated using a Landsat land cover data base developed by the U.S. Geological Survey. Results showed that these data can be useful. Predictions can be improved if ancillary data on residential lot size are included. It was concluded that this type of data base must be examined carefully before implementation.     

遥感数据时间分辨率对土地覆盖变化监测的影响

由于土地覆盖通常存在年际变化和季节变化,区分人为活动引起的变化与这种自然变化需要两期数据时间一致。论文利用黑龙江省1975、1990、2000、和2005代表年Landsat数据,分析了数据时间差在16 d MODIS-NDVI时间序列的误差。研究发现,Landsat数据4个代表年的年际差分别为1975±4 a,1990±4.5 a,2000±1.5 a,2005±1.5 a,季节差为47 d。Landsat季节差对MODIS-NDVI的影响是:当季节差为16~48 d时,东北地区耕地与草地以生长高峰期为参照的MODIS-NDVI变化最高可达0.4以上,林地MODIS-NDVI变化0.1~0.2;当时间差<16 d时,如果仍以生长高峰期为绝对参照,东北大部分地区误差<0.1,只有少数耕地误差0.1~0.2。如果以两个时间为相对参照,则<16 d的误差也可能导致较大的MODIS-NDVI误差,其大小有明显的季节性。在生长高峰期7月12日至8月28日,8 d±8 d的时间差导致的NDVI差大于0.1的像元仅2.93%,是变化检测最佳时期;但在除此之外的6月10日至9月29日间,8 d±8 d时间差导致NDVI差大于0.1的像元比例超过11.42%,选择Landsat必须避开这一敏感期。结论:利用高时间分辨率MODIS-NDVI可在3个方面提高Landsat变化检测效果:①辅助选择最佳时间;②当无法选择最佳时间时,评估时间差可能引起的误差和方向;③直接使用生物物理参数作为变化检测的指标之一。