Mapping hurricane damage: A comparative analysis of satellite monitoring methods

Wetlands are the second-most valuable natural resource on Earth but have declined by approximately 70 % since 1900. Restoration and conservation efforts have succeeded in some areas through establishment of refuges where anthropogenic impacts are minimized. However, these areas are still prone to wetland damage caused by natural disasters. Severe storms such as Hurricane Irma, which made landfall as a Category 3 hurricane in southwest Florida (USA) on September 11, 2017, can cause the destruction of mangroves and other wetland habitat. Multispectral images from commercial satellites provide a means to assess the extent of the damage to different wetland habitat types with high spatial resolution (2 m pixels or finer) over large areas. Using such images presents a number of challenges, including deriving consistent and accurate classification of wetland and non-wetland vegetation. Machine learning methods have demonstrated high-accuracy mapping capabilities on small spatial scales, but require a large amount of robust training data. Meanwhile, ambitious efforts to map larger areas at finer resolutions may use hundreds of thousands of images, and therefore encounter Big-Data processing challenges. Large-scale efforts face the dilemma of adopting traditional mapping methods that may lend themselves to Big Data analytics but may result in accuracies that are inferior to new methods, or move to machine learning methods, which require robust training data. Given these considerations, we describe a version of the traditional Decision Tree (DT) approach and compare two common machine learning methods to derive land cover classes using a WorldView-2 image collected on November 12, 2018 to include one growing season after Hurricane Irma affected this area. Specifically, we compared the Support Vector Machine [SVM] and Neural Network [NN] methods, trained and validated with separate ground-truth datasets collected during a robust field campaign. Overall accuracies were only marginally different (85 % NN vs 83 % each DT and SVM), but healthy mangroves were more accurately identified with the DT (91 % vs 88 % NN and 86 % SVM), and degraded mangroves were more accurately identified with NN (62 % vs 57 % NN and 38 % DT). These results, combined with their respective training requirements, have implications for the direction with which large-scale high-resolution mapping of coastal habitats proceeds.     

Vegetation monitoring for Guatemala: a comparison between simulated VIIRS and MODIS satellite data

The advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data are being widely used for vegetation monitoring across the globe. However, sensors will discontinue collecting these data in the near future. National Aeronautics and Space Administration is planning to launch a new sensor, visible infrared imaging radiometer suite (VIIRS), to continue to provide satellite data for vegetation monitoring. This article presents a case study of Guatemala and compares the simulated VIIRS-Normalized Difference Vegetation Index (NDVI) with MODIS-NDVI for four different dates each in 2003 and 2005. The dissimilarity between VIIRS-NDVI and MODIS-NDVI was examined on the basis of the percent difference, the two-tailed student's t-test, and the coefficient of determination, R ². The per cent difference was found to be within 3%, the p-value ranged between 0.52 and 0.99, and R ² exceeded 0.88 for all major types of vegetation (basic grains, rubber, sugarcane, coffee and forests) found in Guatemala. It was therefore concluded that VIIRS will be almost equally capable of vegetation monitoring as MODIS.     

Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda

Background  

Assessing biomass is gaining increasing interest mainly for bioenergy, climate change research and mitigation activities, such as reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). In response to these needs, a number of biomass/carbon maps have been recently produced using different approaches but the lack of comparable reference data limits their proper validation. The objectives of this study are to compare the available maps for Uganda and to understand the sources of variability in the estimation. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset.     

Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R² ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.     

Baseline estimation with semidynamic and geometric satellite methods

Accurate relative positioning via dynamic satellite methods is a complicated process. In an attempt to simplify this process a semidynamic method has been investigated in a real data environment. In this method quasi-simultaneous observations from pairs of stations are transformed to Simultaneous Range Differences(SRD's). With this transformation it is anticipated to reduce the effects of orbital and observational residual biases and, therefore, to obtain baselines the accuracy of which is less sensitive to the overall orbital accuracy and yet compatible to that of the observations. Using laser range observations to Lageos collected during theMERIT Main Campaign, baselines have been estimated via both theSRD and the geometric methods. Baselines estimated via the geometric method are independent of orbital errors and any inconsistencies affecting the implementation of the Terrestrial Reference Frame, and therefore they have been used in the present study as standards of comparison. From this comparison it was concluded that for baselines of regional extent, theSRD method is very efficient and at least as accurate as the more complex dynamic methods.     

Doppler satellite observations of polar motion

Observations on Navy navigation satellites made by thirteen Doppler receiving stations have been used to determine the position of the earth's pole daily for a six month period of time. A precision of one meter has been obtained on the basis of forty-eight hours of observations on one satellite. No bias is apparant between computations based on different satellites, but differences of about a meter exist with respect to values published by the Bureau International de l'Heure on the basis of astronomical observations.     

Tracing high time-resolution fluctuations in dissolved organic carbon using satellite and buoy observations: Case study in Lake Taihu,China

Field measurements of dissolved organic carbon (DOC) concentration and remote-sensing reflectance were conducted to develop a regional, empirical red-blue algorithm to retrieve surface DOC from Geostationary Ocean Color Imager (GOCI) data for Lake Taihu, China. The auxiliary data (in-situ observations of the optical properties and water quality, buoy measurements of hydrodynamic data and water chemical parameters) were used to investigate the spatial and temporal variations in DOC. GOCI was shown to be capable of successfully obtaining hourly variations in DOC, with a root mean square error percentage (RMSP) of 17.29% (RMSE = 0.69 mg/L) for the match-up data. The GOCI-derived DOC in Lake Taihu confirms that the highest DOC concentration is in northwest Lake Taihu, followed by Meiliang Bay, Gonghu Bay and northeast Lake Taihu. Hourly DOC variation is significant and presents a different trend for each lake segment due to the variety of influencing factors. Discharge of DOC from surrounding rivers is an important factor to the variation of DOC in northeast Lake Taihu. However, organic products of algae will be the primary contributor to DOC when algal bloom occurred. During the period of algal bloom, high DOC levels in Lake Taihu can lead to hypoxia when coupled with high temperatures and low disturbance.     

Wheat growth profile: satellite monitoring and crop yield modelling

The most important advantage of the low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA AVHRR) data is its high temporal frequency and high radiometric sensitivity which helps in vegetation detection in the visible and near-infrared spectral regions. In areas where most of the crop cultivation is in large contiguous areas, and if the AVHRR data are selected for time period such that the crop of interest is well discriminated from other crops, these data can be used for monitoring vegetative growth and condition very effectively. The present study deals with the application of AVHRR data for the monitoring of the wheat crop in its seventeen main growing districts of the Rajasthan state. The fourteen date AVHRR data covering the entire growth period have been used to generate the normalized difference vegetation index (NDV1) growth profile for the crop by masking the non-crop pixels following the two-date NDVI change method. The growth profile parameters and other derived parameters, such as post-anthesis senescence rate and areas under the entire growth profile or under selected growth periods have been related to the district average wheat yield through statistical regression models. Various methods adopted for wheat pixels masking have been critically evaluated. It is found that the wheat yield can be predicted well by the area under the profile in different growth periods.     

Combination of GNSS and SLR observations using satellite co-locations

Satellite Laser Ranging (SLR) observations to Global Navigation Satellite System (GNSS) satellites may be used for several purposes. On one hand, the range measurement may be used as an independent validation for satellite orbits derived solely from GNSS microwave observations. On the other hand, both observation types may be analyzed together to generate a combined orbit. The latter procedure implies that one common set of orbit parameters is estimated from GNSS and SLR data. We performed such a combined processing of GNSS and SLR using the data of the year 2008. During this period, two GPS and four GLONASS satellites could be used as satellite co-locations. We focus on the general procedure for this type of combined processing and the impact on the terrestrial reference frame (including scale and geocenter), the GNSS satellite antenna offsets (SAO) and the SLR range biases. We show that the combination using only satellite co-locations as connection between GNSS and SLR is possible and allows the estimation of SLR station coordinates at the level of 1–2 cm. The SLR observations to GNSS satellites provide the scale allowing the estimation of GNSS SAO without relying on the scale of any a priori terrestrial reference frame. We show that the necessity to estimate SLR range biases does not prohibit the estimation of GNSS SAO. A good distribution of SLR observations allows a common estimation of the two parameter types. The estimated corrections for the GNSS SAO are 119 mm and −13 mm on average for the GPS and GLONASS satellites, respectively. The resulting SLR range biases suggest that it might be sufficient to estimate one parameter per station representing a range bias common to all GNSS satellites. The estimated biases are in the range of a few centimeters up to 5 cm. Scale differences of 0.9 ppb are seen between GNSS and SLR.     

Forest carbon stocks and fluxes in physiographic zones of India

Background  

Reducing carbon Emissions from Deforestation and Degradation (REDD+) is of central importance to combat climate change. Foremost among the challenges is quantifying nation's carbon emissions from deforestation and degradation, which requires information on forest carbon storage. Here we estimated carbon storage in India's forest biomass for the years 2003, 2005 and 2007 and the net flux caused by deforestation and degradation, between two assessment periods i.e., Assessment Period first (ASP I), 2003-2005 and Assessment Period second (ASP II), 2005-2007.     

Simulations of VLBI observations of a geodetic satellite providing co-location in space

We performed Monte Carlo simulations of very-long-baseline interferometry (VLBI) observations of Earth-orbiting satellites incorporating co-located space-geodetic instruments in order to study how well the VLBI frame and the spacecraft frame can be tied using such measurements. We simulated observations of spacecraft by VLBI observations, time-of-flight (TOF) measurements using a time-encoded signal in the spacecraft transmission, similar in concept to precise point positioning, and differential VLBI (D-VLBI) observations using angularly nearby quasar calibrators to compare their relative performance. We used the proposed European Geodetic Reference Antenna in Space (E-GRASP) mission as an initial test case for our software. We found that the standard VLBI technique is limited, in part, by the present lack of knowledge of the absolute offset of VLBI time to Coordinated Universal Time at the level of microseconds. TOF measurements are better able to overcome this problem and provide frame ties with uncertainties in translation and scale nearly a factor of three smaller than those yielded from VLBI measurements. If the absolute time offset issue can be resolved by external means, the VLBI results can be significantly improved and can come close to providing 1 mm accuracy in the frame tie parameters. D-VLBI observations with optimum performance assumptions provide roughly a factor of two higher uncertainties for the E-GRASP orbit. We additionally simulated how station and spacecraft position offsets affect the frame tie performance.     

On monitoring a vertical datum with satellite altimetry and water-level gauge data on large lakes

Using TOPEX satellite altimetry, water-level-gauge data, and a geoid model, the geopotential, W ₀, of the International Great Lakes Datum of 1985 (IGLD85) is determined. This is compared to an analogous determination using GPS and leveling data in the region. The two sets of data yield generally consistent results at the few-decimeter level and both indicate a tilt of about 33 cm in the computed datum across the region. On the basis of this and other studies, it is conjectured that the source of the tilt is a regional error in G99SSS. Further analysis of the altimetry and water-level data indicate that the geoid model, G99SSS, is in error by up to 20 cm at scales of about 100–150 km. In addition, the analysis of 8 years of altimeter and water-level data shows varying trends (up to 2 mm/yr) in crustal uplift throughout the region, generally consistent with an independent post-glacial rebound (PGR) model, ICE-4G. AcknowledgmentThis research was supported in part by the Ohio Sea Grant Program, grant no. NA86RG0053 (R/CE-5). A. Mainville kindly provided data and information for Canadian stations. The authors are grateful to M. Bursa, M. Poutanen, D. Zilkoski, and an anonymous reviewer for contributing significantly to the improvement of the paper.     

基于GPS卫星测量的远距离滑坡监测应用研究

利用GPS进行远距监控滑坡区并做适用性的评估研究,整合系统中含供电系统(室内及室外电源设置)、无线传输(利用GSM传输)、GPS资料后处理软件及监控软件等。所组合的系统将应用在某滑坡区,进行长时间持续监测。试验方式包含现场模拟试验、滑坡模拟测试,同时以远近两不同距离的主控站来评估此监测系统因基线长短不同所造成的精度影响,以期能通过此研究提出GPS监测系统设置的经验。根据研究成果指出,GPS监测系统应用在滑坡监测上可行,利用无线传输(GSM)可成功将现场的资料传给远程的操作者。试验结果显示,利用近基站观测可提高观测的精度及解算的成功率,但不论在远基线或近基线的试验,GPS监测系统的精度均可达到公分等级。     

基于中巴卫星影像的土地利用分类精度评价

本文对某地区的中巴地球资源一号02星CCD数据进行监督分类,并将影像分类结果进行精度评价,得到了中巴影像分类传统的精度报告。在此基础上,为了使精度可信性更高,提出了一种新的精度评价方法;即在中巴影像分类后结果图上随机抽取其图斑总数的5%,将其与研究地区土地利用数据库结合ArcGIS,进行叠加比较,对抽取出的各图斑类别进行面积对比,在一定程度上评价了中巴影像的分类精度,并对分类结果进行了量化的精度评价,从而丰富了精度评价的内容。从研究结果表明,经过面积对比,可以较精确地看出中巴影像分出的沙地、城市用地及冬小麦的精度比较高,但村庄和有些耕地光谱特征相似难以区分。     

Mapping and monitoring of Rann (tidal) ingress in Banni Plains,Kachchh, Gujarat using multi-temporal satellite data

The Rann ingress into the ‘Banni Plains’ of Kachchh district, Gujarat which is famous for the sprawling grasslands supporting significant cattle population is taking place at an alarming rate. This has resulted in the desertion of many villages due to intrusion of Rann waters. In the present investigation multi-date satellite data has been analysed to map and monitor the Rann ingress during the period 1975 to 1989. Detailed analysis has brought out that the inundation is quite rapid affecting about 244 sq. km area during 1960–1989 period. Mapping of the creeks has also been done to understand the possible mechanism of ‘Rann ingress’.     

Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms

This work is aimed at the environmental remote sensing community that uses UAV optical frame imagery in combination with airborne and satellite data. Taking into account the economic costs involved and the time investment, we evaluated the fit-for-purpose accuracy of four positioning methods of UAV-acquired imagery: 1) direct georeferencing using the onboard raw GNSS (GNSSNAV) data, 2) direct georeferencing using Post-Processed Kinematic single-frequency carrier-phase without in situ ground support (PPK1), 3) direct georeferencing using Post-Processed Kinematic double-frequency carrier-phase GNSS data with in situ ground support (PPK2), and 4) indirect georeferencing using Ground Control Points (GCP). We tested a multispectral sensor and an RGB sensor, onboard multicopter platforms. Orthophotomosaics at <0.05 m spatial resolution were generated with photogrammetric software. The UAV image absolute accuracy was evaluated according to the ASPRS standards, wherein we used a set of GCPs as reference coordinates, which we surveyed with a differential GNSS static receiver. The raw onboard GNSSNAV solution yielded horizontal (radial) accuracies of RMSEr≤1.062 m and vertical accuracies of RMSEz≤4.209 m; PPK1 solution gave decimetric accuracies of RMSEr≤0.256 m and RMSEz≤0.238 m; PPK2 solution, gave centimetric accuracies of RMSEr≤0.036 m and RMSEz≤0.036 m. These results were further improved by using the GCP solution, which yielded accuracies of RMSEr≤0.023 m and RMSEz≤0.030 m. GNSSNAV solution is a fast and low-cost option that is useful for UAV imagery in combination with remote sensing products, such as Sentinel-2 satellite data. PPK1, which can register UAV imagery with remote sensing products up to 0.25 m pixel size, as WorldView-like satellite imagery, airborne lidar or orthoimagery, has a higher economic cost than the GNSSNAV solution. PPK2 is an acceptable option for registering remote sensing products of up to 0.05 m pixel size, as with other UAV images. Moreover, PPK2 can obtain accuracies that are approximate to the usual UAV pixel size (e.g. co-register in multitemporal studies), but it is more expensive than PPK1. Although indirect georeferencing can obtain the highest accuracy, it is nevertheless a time-consuming task, particularly if many GCPs have to be placed. The paper also provides the approximate cost of each solution.     

Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series

In the context of growing populations and limited resources, the sustainable intensification of agricultural production is of great importance to achieve food security. As the need to support management at a range of spatial scales grows, decision-support tools appear increasingly important to enable the timely and regular assessment of agricultural production over large areas and identify priorities for improving crop production in low-productivity regions. Understanding productivity patterns requires the timely provision of gapless, spatial information about agricultural productivity. In this study, dense 30-m time series covering the 2004–2014 period were generated from Landsat and MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images over the irrigated cropped area of the Fergana Valley, Central Asia. A light-use efficiency model was combined with machine learning classifiers to assess the crop yield at the field level. The classification accuracy of land cover maps reached 91% on average. Crop yield and acreage estimates were in good agreement (R² = 0.812 and 0.871, respectively) with reported yields and acreages at the district level. Several indicators of cropland intensity and productivity were derived on a per-field basis and used to highlight homogeneous regions in terms of productivity by means of clustering. Results underlined that regions with lower water-use efficiency were not only located further away from irrigation canals and intake points, but also had limited access to markets and roads. The results underline that yield could be increased by roughly 1.0 and 1.4 t/ha for cotton and wheat, respectively, if the access to water would be optimized in some of the regions. The minimum calibration requirement of the method and the fusion of multi-sensor data are keys to cope with the constraints of operational crop monitoring and guarantee a sustained and timely delivery of the agricultural indicators to the user community. The results of this study can form the baseline to support regional land- and water-resource management.     

“天绘一号”传输型摄影测量与遥感卫星

介绍了“天绘一号”卫星的总体方案、技术特点、研制历程和应用概况, 重点介绍了“天绘一号”卫星的技术特点和应用前景, 以便用户了解、熟悉“天绘一号”卫星, 更好使用其各种影像产品, 更好发挥卫星的应用效能。“天绘一号”卫星摄影定位与测图系统采用LMCCD(Line Matrix Charge Coupled Device)测绘体制, 由前视、正视和后视3台全色CCD相机, 3台星敏感器以及1台GPS接收机组成;其中正视相机焦平面上还设计有4个小面阵CCD器件, 用于提高摄影定位的高程精度。卫星摄影测量基高比为1。卫星通过一个高强度、高刚度和高稳定度的测绘光学平台, 将3台测绘相机、3台星敏感器和1台多光谱相机集成为一体, 满足了测绘任务对星敏感器与相机间几何角度关系的高精度及高稳定度要求。在轨测试结果表明, 卫星摄影定位精度优于任务指标要求, 可满足无地面控制点条件下测制全球1:50000比例尺地图要求。     

Mapping species of submerged aquatic vegetation with multi-seasonal satellite images and considering life history information

Spatial information of the dominant species of submerged aquatic vegetation (SAV) is essential for restoration projects in eutrophic lakes, especially eutrophic Taihu Lake, China. Mapping the distribution of SAV species is very challenging and difficult using only multispectral satellite remote sensing. In this study, we proposed an approach to map the distribution of seven dominant species of SAV in Taihu Lake. Our approach involved information on the life histories of the seven SAV species and eight distribution maps of SAV from February to October. The life history information of the dominant SAV species was summarized from the literature and field surveys. Eight distribution maps of the SAV were extracted from eight 30 m HJ-CCD images from February to October in 2013 based on the classification tree models, and the overall classification accuracies for the SAV were greater than 80%. Finally, the spatial distribution of the SAV species in Taihu in 2013 was mapped using multilayer erasing approach. Based on validation, the overall classification accuracy for the seven species was 68.4%, and kappa was 0.6306, which suggests that larger differences in life histories between species can produce higher identification accuracies. The classification results show that Potamogeton malaianus was the most widely distributed species in Taihu Lake, followed by Myriophyllum spicatum, Potamogeton maackianus, Potamogeton crispus, Elodea nuttallii, Ceratophyllum demersum and Vallisneria spiralis. The information is useful for planning shallow-water habitat restoration projects.