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.     

NEAR REAL-TIME MAPPING OF THE 1975 MISSISSIPPI RIVER FLOOD IN LOUISIANA USING LANDSAT IMAGERY1

ABSTRACT: The project described in this report was undertaken by the Louisiana State Planning Office to establish the extent of backwater flooding in Louisiana in April 1975. Band 7 Landsat imagery, enlarged to a scale of 1:250,000 was used to visually identify flooded areas. Inundated areas were delineated on overlays keyed to 1:250,000 U.S. Geological Survey topographic quadrangles. Tabular data identifying acres flooded, according to land use type, were derived by merging the flood map overlays with computerized 1972 land use data. Approximately 1.12 million acres of the state were inundated by flood waters. The total acreage and land use types affected by flooding were determined within 72 hours from the time the flood areas were imaged. Flooded maps were prepared for 26 parishes. Field observations were made by Louisiana Cooperative Extension Service county agents in order to determine the accuracy of parish flood maps and flood acreage figures by land use type. Results indicated that this was a fast, accurate, and relatively inexpensive method of compiling flood data for disaster planning and postflood analysis.     

National aeronautics and space administration

The United States National Aeronautics and Space Administration is an important real and potential source of global environmental data. This paper describes some of its capabilities as they relate to the their R&D and their data centers.     

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.     

RESERVOIR TROPHIC STATE EVALUATION USING LANISAT TM IMAGES1

ABSTRACT: Reservoir water quality is traditionally monitored and evaluated based on field data. Collecting and analyzing field water quality data are costly and time consuming tasks, and whether a limited number of field data truly characterize the spatial variation of the trophic state within a vast water body is often disputed. In this study we utilize Landsat TM data to estimate the water quality and trophic state of the Te‐Chi reservoir in Central Taiwan. A modified multi‐parameter model of Carlson's trophic state index (TSI) was developed for the Te‐chi reservoir. Water quality parameters (concentration of chlorophyll‐a, total phosphorous measurement, and secchi disk depth) required by the model are found to have high correlations with combinations of TM bands. Therefore, TM data are used to map the trophic state of the reservoir. TM‐derived TSI maps of the reservoir reveal that, in summer, the trophic state in the reservoir generally improves from upstream to downstream and that zones of distinct trophic state exist. A trophic state index based on secchi disk depth may give erroneous values in the upstream section of the reservoir pool due to high sediment concentration in the reservoir inflow. We conclude that the Te‐Chi reservoir is eutrophic or worse in summer and meso‐eutrophic in winter. Implementation of best management practices to reduce nonpoint source pollution in the upstream watershed is highly recommended. This study demonstrates the capability of mapping the trophic state in impounded water bodies using the Landsat TM data.     

厦门市绿色植被降温服务功能核算及其时空动态特征

为了准确核算厦门市绿色植被降温服务功能,收集厦门市2010年和2015年18个气象站点数据,采用30 m空间分辨率Landsat卫星数据和250 m空间分辨率、16 d合成的MODIS植被指数产品,在已有基于能量平衡估算模型的基础上,通过考虑植被覆盖及降温服务时长,构建了绿色植被降温服务功能核算的改进模型,并对厦门市2010—2015年绿色植被降温服务功能时空动态特征进行分析.结果表明:①改进模型能够较为准确且合理地描述绿色植被降温服务功能的时空变化特征.②厦门市北部山区由于高植被覆盖度降温服务功能高于南部城市建成区,而城市建成区中的城市绿地也具有明显的降温作用.③2010—2015年各区降温服务功能实物量整体呈增加趋势.其中,同安区降温服务功能实物量变化量最多,为166.12×106 kW·h;湖里区变化量最少,为9.72×106 kW·h;其余各区变化量都在40×106~75×106 kW·h范围内.④森林在降温服务中贡献最大,达60%以上.相比2010年,2015年降温服务功能实物量除了灌木林地变化率为-4.29%外,其余绿色植被类型均呈增长趋势,如森林和农田的增长率为11.97%、14.23%,草地和城市绿地的增长率为87.45%、92.11%.研究显示,厦门市2010—2015年的绿色植被降温服务功能总体呈明显增强趋势,其中城市绿地的降温服务功能增强尤为明显.      

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.     

基于多源同步数据的闽江下游悬浮物定量遥感

利用2006-09-18的同步Landsat TM数据、水面实测光谱数据和现场水样数据,研究了闽江下游的悬浮物,在这3种同步数据的基础上建立了分别基于实测光谱与影像光谱的悬浮物遥感预测模型.结果表明,实测光谱数据在690nm波长处,归一化光谱反射率与悬浮物浓度达到最大正相关,由690nm和530nm二处的反射率构成的比值预测模型与实测悬浮物浓度的拟合精度最高,最佳拟合模型可表达为SS=116.2(R690/R530)-33.4.TM影像各波段中以(TM2 TM3)2波段组合与实测悬浮物浓度的相关性最佳.由其所建立的影像光谱预测模型与实测悬浮物浓度的拟合精度最高.最佳拟合模型可表达为SS=3793.7(RTM3 RTM2)2-16.5.模型的精度评价表明,实测光谱模型的预测能力要强于影像光谱模型.但二者差异不大.在缺乏地面实测光谱数据时.基于影像光谱的遥感定量模型仍不失为一种预测悬浮物浓度的有效方法,其对闽江下游悬浮物浓度的反演结果能较准确地反映出该区域悬浮物浓度分布的空间差异,具有较高的实用性.     

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。