Volcano monitoring using remote sensing data

Abstract

The paper brings out the theoretical basis and utility of near-infrared band data sets obtained from Earth resources satellites, in the estimation of very high temperatures witnessed during volcanic eruptions. The Landsat Thematic Mapper (TM) and Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning System (LISS-II) data sets for the period 4 April 1991-4 August 1991 were used for studying the volcanic eruption at Barren Island (India). The effect of the sub-pixel size vent in the estimation of pixel-integrated temperature has been discussed. The volcanic vent temperature on 6 May 1991 was found to be 1084K. The availability of mid-IR bands (1-55-1-75 μm and 2.08-2.35μm spectral region) in Landsat-TM enabled bringing out the vent region in the false colour composite (FCC) generated using these and the near-IR (0.76-0.90μm) band. The very high or saturated values in mid-IR bands brought a good contrast between vent and its surroundings     

水利部旱情遥感监测系统建设与展望

遥感技术以其快速、经济和大空间范围获取的特点,已成为旱情监测的重要手段。介绍国家防汛指挥系统二期工程水利部旱情遥感监测系统的建设情况,包括旱情遥感监测模型、业务流程及系统的设计与开发等。系统实现全国旱情监测逐周生产、区域旱情1~3 d应急快速监测及逐月区域水体监测产品的生产。试运行表明全国旱情监测与国外同类产品结果一致或优于同类产品;区域旱情监测平均精度达到80%以上。最后,对旱情遥感监测系统未来发展进行展望。     

Tropical forest typo mapping and monitoring using remote sensing

Abstract

The Andaman Nicobar group of islands in the Andaman Sea are blessed with luxuriant tropical vegetation. During recent years, vegetation in these islands has been under tremendous pressure due to increased settlement and commercial extraction, Landsat TM data of the Baratang forest division of the Andaman group of islands has been used to prepare forest type maps using visual and digital methods. Digital enhancement techniques have been evaluated to discriminate forest types in a typical environmental set-up. The mapping techniques have been compared with respect to classification and accuracy levels. Finally, the land transformation in the forest division has been studied using past aerial photographs. The study highlights the appropriate methodology required to map forest types.     

Reconciling the global terrestrial water budget using satellite remote sensing

Recent retrievals of multiple satellite products for each component of the terrestrial water cycle provide an opportunity to estimate the water budget globally. In this study, we estimate the water budget from satellite remote sensing over ten global river basins for 2003-2006. We use several satellite and non-satellite precipitation (P) and evapo-transpiration (ET) products in this study. The satellite precipitation products are the GPCP, TRMM, CMORPH and PERSIANN. For ET, we use four products generated from three retrieval models (Penman-Monteith (PM), Priestley-Taylor (PT) and the Surface Energy Balance System (SEBS)) with data inputs from the Earth Observing System (EOS) or the International Satellite Cloud Climatology Project (ISCCP) products. GPCP precipitation and PM (ISCCP) ET have less bias and errors over most of the river basins. To estimate the total water budget from satellite data for each basin, we generate merged products for P and ET by combining the four P and four ET products using weighted values based on their errors with respect to non-satellite merged product. The water storage change component is taken from GRACE satellite data, which are used directly with a single pre-specified error value. In the absence of satellite retrievals of river discharge, we use in-situ gauge measurements. Closure of the water budget over the river basins from the combined satellite and in-situ discharge products is not achievable with errors of the order of 5-25% of mean annual precipitation. A constrained ensemble Kalman filter is used to close the water budget and provide a constrained best-estimate of the water budget. The non-closure error from each water budget component is estimated and it is found that the merged satellite precipitation product carries most of the non-closure error.     

Shallow water bathymetry using integrated airborne multi-spectral remote sensing

An integrated low cost airborne multi-spectral remote sensing system is described and evaluated for remote sensing for shallow water bathymetry. The system consists of: two 35mm motor driven reconnaissance cameras using colour and colour infrared film. Three optically filtered (including removable internal IR cut-off filters), electronically shuttered CCD progressive scan cameras (Sony XC-7500) integrated into an airborne direct digital recording system using a PC processor, a 32-bit RGB analogue to digital conversion card and Zip disk storage. Two CCD based imaging spectrometers providing approximately 10nm bandwidth spectral data across the CCD spectrum (400nm to 1000nm). These CCD cameras were used with a variable interference filter fixed in front of the sensor surface. This provided a 'rainbow' image of the ground varying across the image from 400nm to 700nm (visible) and 700nm to 1000nm. Field studies were undertaken to evaluate the performance of digital multi-spectral (DMV) imagery, supplementary reconnaissance photography (SRP) and VIFIS imaging spectrometry for mapping shallow water bathymetry. The results indicate good performance in shallow water and suggest that with further refinement the system could be used to give a quick comprehensive estimate of shallow water depths     

Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico

The delta of the Colorado River in Mexico supports a rich mix of estuarine, wetland and riparian ecosystems that provide habitat for over 350 species of birds as well as fish, marine mammals, and other wildlife. An important part of the delta ecosystem is the riparian corridor, which is supported by agricultural return flows and waste spills of water originating in the U.S. and Mexico. These flows may be curtailed in the future due to climate change and changing land use practices (out-of-basin water transfers, increased agricultural efficiency, and more optimal management of dams) in the U.S. and Mexico, and resource managers need to monitor the effects of their water management practices on these ecosystems. We developed ground-validated, remote sensing methods to monitor the vegetation status, habitat value, and water use of wetland and riparian ecosystems using multi-temporal, multi-resolution images. The integrated methodology allowed us to project species composition, leaf area index, fractional cover, habitat value, and evapotranspiration over seasons and years throughout the delta, in response to variable water flows from the U.S. to Mexico. Waste spills of water from the U.S. have regenerated native cottonwood and willow trees in the riparian corridor and created backwater and marsh areas that support birds and other wildlife. However, the main source of water supporting the riparian vegetation is the regional aquifer recharged by underflow from U.S. and Mexico irrigation districts. Native trees have a short half-life in the riparian zone due to human-set fires and harvesting for timber. Active management, monitoring, and restoration programs are needed to maintain the habitat value of this ecosystem for the future.     

The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

The Journal of Supercomputing - In order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in...     

Mapping and monitoring conifer mortality using remote sensing in the Lake Tahoe Basin

A prolonged drought in the western United States has resulted in alarming levels of mortality in conifer forests. Satellite remote sensing holds the potential for mapping and monitoring the effects of such environmental changes over large geographic areas in a timely manner. Results from the application of a forest canopy reflectance model using multitemporal Landsat TM imagery and field measurements, indicate conifer mortality can be effectively mapped and inventoried. The test area for this project is the Lake Tahoe Basin Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The difference in cover estimates between the dates forms the basis for stratifying stands into mortality classes, which are used as both themes in a map and the basis of the field sampling design. Field measurements from 61 stands collected in the summer of 1992 indicate 15 % of the original timber volume in the true fir zone died between 1988 and 1992. The resulting low standard error of 11 % for this estimate indicates the utility of these mortality classes for detecting areas of high mortality. Also, the patterns in the estimated mean timber volume loss for each class follow the expected trends. The results of this project are immediately useful for fire hazard management, by providing both estimates of the degree of overall mortality and maps showing its location. They also indicate current remote sensing technology may be useful for monitoring the changes in vegetation that are expected to result from climate change.     

Regularized estimation of bathymetry and water quality using hyperspectral remote sensing

Coastal water mapping from remote-sensing hyperspectral data suffers from poor retrieval performance when the targeted parameters have little effect on subsurface reflectance, especially due to the ill-posed nature of the inversion problem. For example, depth cannot accurately be retrieved for deep water, where the bottom influence is negligible. Similarly, for very shallow water it is difficult to estimate the water quality because the subsurface reflectance is affected more by the bottom than by optically active water components.

Most methods based on radiative transfer model inversion do not consider the distribution of targeted parameters within the inversion process, thereby implicitly assuming that any parameter value in the estimation range has the same probability. In order to improve the estimation accuracy for the above limiting cases, we propose to regularize the objective functions of two estimation methods (maximum likelihood or ML, and hyperspectral optimization process exemplar, or HOPE) by introducing local prior knowledge on the parameters of interest. To do so, loss functions are introduced into ML and HOPE objective functions in order to reduce the range of parameter estimation. These loss functions can be characterized either by using prior or expert knowledge, or by inferring this knowledge from the data (thus avoiding the use of additional information).

This approach was tested both on simulated and real hyperspectral remote-sensing data. We show that the regularized objective functions are more peaked than their non-regularized counterparts when the parameter of interest has little effect on subsurface reflectance. As a result, the estimation accuracy of regularized methods is higher for these depth ranges. In particular, when evaluated on real data, these methods were able to estimate depths up to 20 m, while corresponding non-regularized methods were accurate only up to 13 m on average for the same data.

This approach thus provides a solution to deal with such difficult estimation conditions. Furthermore, because no specific framework is needed, it can be extended to any estimation method that is based on iterative optimization.     

针对华北地区地下水河湖生态补水成效的卫星遥感动态监测

为评估华北地区地下水超采治理河湖生态补水成效,基于现有国产卫星遥感影像覆盖能力,收集 2020 年华北地区 22 条(个)河湖流域卫星遥感动态监测影像数据,以相关水利基础数据为依据,构建一套完整的针对华北地区地下水河湖生态补水成效的水体遥感监测方案,将影像进行正射、融合、裁剪等预处理后,运用水体指数和深度学习 2 种方法对预处理后的遥感影像进行解译分析,提取河湖流域水面面积和有水河段长度,并结合河湖生态补水量进行验证,分析年内变化规律。监测结果表明：2020 年 7—12 月期间华北地区补水河湖水面面积和有水河段长度减少趋势减缓,并出现增加趋势。通过生态补水有利于缓解超采区水面面积减少趋势,验证通过统筹多种水源、调节时空布局,开展河湖回补地下水的方案是确有成效的。     

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.     

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin,USA

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R ² = 0.90).     

Flood monitoring using microwave passive remote sensing (AMSR-E) in part of the Brahmaputra basin,India

In this work, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) signatures were analysed over some critical sites in Lakhimpur District in Brahmaputra basin, India, characterized by a high frequency of flooding events. The site is mostly covered by paddy fields. Results obtained were compared with water level measurements in three stations close to the main channel of the river. Information about surface temperature, which allowed us to estimate the emissivity, was also available. Investigations were carried out at the C, X, and K_a bands of the AMSR-E channel. A multi-frequency analysis indicated that the X band would represent a good compromise between resolution and sensitivity requirements, while at the C band the resolution was too coarse and at the K_a band the signatures were affected by raindrops. Samples collected during rain were eliminated using techniques based on the 89.0 GHz channel. However, even after this correction, the K_a band showed poor sensitivity due to higher attenuation by vegetation. The correlations between different pairs of variables, viz. polarization index (PI), water level (WL), and fractional water surface area (F _WS), were also investigated. At the X band, the water level was better correlated with the PI than with emissivity and other parameters defined in the literature. The correlation was good in cases of slow variation in WL. In cases of sudden variation in the river, the PI followed the variations with some time delay related to the propagation of water within the covered AMSR-E pixel.     

Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data

In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. Estimates of AGB are relevant for sustainable forest management, monitoring global change, and carbon accounting. This is particularly true for the Qilian Mountains, which are a water resource protection zone. We combined forest inventory data from 133 plots with TM images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) V2 products (GDEM) in order to analyse the influence of the sun-canopy-sensor plus C (SCS+C) topographic correction on estimations of forest AGB using the stepwise multiple linear regression (SMLR) and k-nearest neighbour (k-NN) methods. For both methods, our results indicated that the SCS+C correction was necessary for getting more reliable forest AGB estimates within this complex terrain. Remotely sensed AGB estimates were validated against forest inventory data using the leave-one-out (LOO) method. An optimized k-NN method was designed by varying both mathematical formulation of the algorithm and remote-sensing data input, which resulted in 3000 different model configurations. Following topographic correction, performance of the optimized k-NN method was compared to that of the regression method. The optimized k-NN method (R² = 0.59, root mean square error (RMSE) = 24.92 tonnes ha^–1) was found to perform much better than the regression method (R² = 0.42, RMSE = 29.74 tonnes ha^–1) for forest AGB retrieval over this montane area. Our results indicated that the optimized k-NN method is capable of operational application to forest AGB estimates in regions where few inventory data are available.     

A study of the endangered plants of the West Ordos Plateau,China using remote sensing data

This paper presents the results of a study of endangered plants using remote sensing data. The aim of this study was to investigate and assess spatial distribution changes and temporal coverage changes of the following endangered rare plants: Tetraena mongolica, Helianthemum soongolicum, Reaumuria trigyna, Reaumuria soongorica, Potaninia mongolica, and Ammopiptanthus mongolicus, in the West Ordos Plateau National Conservation Region in Inner Mongolia, China. Using ETM+ (August 1999) remote sensing data, DEM data, and data from 25 field samples collected from Gangdeger Mountain in July 1999, digital maps highlighting endangered plant populations (1994) were processed using the ‘MPH feature extraction method’ to view the current spatial distribution pattern of the endangered plants. The ‘radiometric normalization’ and ‘vegetation change detection’ methods were then used to reveal temporal coverage changes of the endangered plants, using TM (September 1987) and ETM+ (August 1999) data.     

Satellite remote sensing of waste water reservoirs

SPOT multi-spectral images of nearly 100 water reservoirs were analysed. The image analysis was supported by ground measurements which included water sampling and detailed laboratory analysis of the water constituents and concentration. The analysed reservoirs covered a wide range, from open drinking water reservoirs to polluted hypertrophic ones. Classification of reservoirs by their water quality is achieved by the study of the spectral distribution of the reflected solar radiation, taking into account the atmospheric effects. The techniques included chromatic coordinates analysis and principal component analysis. An optical model of the volume reflectance of the water body was constructed, based on the radiative transfer equation which includes the scattering properties of the main water constituents. Numerical simulations, based on the model, support the experimental findings regarding the reservoir c1assilication according to water quality and composition.     

水体遥感影像处理的研究

研究了遥感图片的水体类型解译识别问题。水体图形的准确提取是遥感图片解译识别的关键问题。综合应用敏感因子组合法、种子点增长法和数学形态学的方法提取水体,在对提取的水体图像分析基础上,进一步确定了特征向量和采用模糊识别理论进行识别,并对识别结果进行了分析,实验结果显示提取的水体获得较满意的识别效果。     

Thermal remote sensing of near-surface water vapor

In this study, four approaches to estimate atmospheric water vapor from Advanced Very High Resolution Radiometer (AVHRR) observations were tested with data from the Boreal Ecosystem–Atmosphere Study (BOREAS) and the Oklahoma Mesonetwork. The approaches studied were (i) the split-window difference of the thermal channels (Channel 4: 10.3–11.3 μm and Channel 5: 11.5–12.5 μm) by Dalu [Int. J. Remote Sens. 7 (1986) 1089.], (ii) the ratio of variances by Jedlovec [J. Appl. Meteorol. 29 (1990) 863.], (iii) the regression slope by Goward et al. [Ecol. Appl. 4 (1994) 322.], and (iv) a look-up table derived from radiative transfer model output. Although these techniques were primarily developed to estimate total column precipitable water, we used them to estimate near-surface water vapor, within a few meters of the surface. Near-surface water vapor is needed for hydrologic and biospheric modeling. Analysis showed the total column precipitable water to be highly correlated (r²=.79) with near-surface absolute humidity for clear-sky conditions at the BOREAS and the Oklahoma study sites. Correlation of all the retrieval techniques with ground observations was very low. For the split-window approach, water vapor can only be estimated on a per pixel basis and is ambiguous for anything but a single site. The regression slope and variance ratio techniques showed very little correlation with ground observations with r²=.02 when compared with data from BOREAS, and .17 for the variance ratio and .24 for the regression slope when compared with Mesonet data. The spatial variability of water vapor across the landscape hampers the use of these contextual approaches. The highest correlation was for the look-up table approach, with r²=.36 when compared with data from the BOREAS site. The look-up table was applied using AVHRR Channels 4 and 5 brightness temperatures, surface temperature, and near-surface air temperature. Surface temperature and air temperature were both estimated from the satellite readings. Combining the satellite data with air temperature measured at meteorological ground stations improved the correlation to .50. The relatively low r² values were at least partly due to spatial and temporal mismatches between surface and satellite measurements. Simulation of Moderate Resolution Imaging Spectrometer (MODIS) thermal Channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) brightness temperatures showed that Channels 31 and 32 provide similar information as AVHRR Channels 4 and 5. The additional thermal information provided by Channel 29 shows promise for future water vapor detection efforts.     

Microwave remote sensing of plant water stress

The sensitivity of microwave (MW) emission to physical conditions of vegetation has been assessed by means of ground-based microwave and infrared radiometers. Measurements on corn and wheat have shown an inverse correlation between the normalized brightness temperature (T_N) from the Ka band (36 GHz) and the atmospheric water vapor pressure (VP) at the top of vegetation. From this observation, we show that a crop water stress index can be calculated by means of down-looking MW sensors, provided air temperature is known. A polarization index (PI) dependent only on microwave measurements was shown to be related to crop water stress.     

Satellite remote sensing of atmospheric water vapour

Abstract

The Advanced Very High Resolution Radiometer (AVHRR-2) has two channels in the 10-13/mi window region. These channels are used for remote sensing of the sea surface temperature corrected for atmospheric absorption. The brightness temperature difference between the channels can be directly related to the atmospheric absorption due to water vapour. The problem of water-vapour retrieval from satellite data is examined in detail. The best evaluation of the water-vapour content is obtained from the spectrometric data of the Infrared Interferometer Spectrometer (IRIS), with an error of about + 3kg/m². It is, however, feasible to obtain the water-vapour content from AVHRR data with an algorithm derived from radiative transfer model simulations. The retrieved water vapour has an error of ±5kg/m² when compared with ship data. It is possible to use the remotely sensed water vapour for the inference of the boundary-layer structure. The information is, however, limited for water vapour contained near the surface.