Suspended sediment concentrations in the Yangtze River estuary retrieved from the CMODIS data

The Chinese Moderate Resolution Imaging Spectroradiometer (CMODIS) was loaded on the China's SZ‐3 spacecraft. Using an empirical line method, the CMODIS radiance is converted to the water‐leaving reflectance, and is applied to inversion of the suspended sediment concentrations in the Yangtze River estuary. The concentrations ranging between 0 mg/L and 1000 mg/L are well validated by the field measurement data. This study demonstrates an example for the feasibility of the CMODIS data for concentration retrieval of the suspended sediment.     

Water quality mapping from Landsat digital data

Landsat multispectral scanner (MSS) data, and U-2 colour and colour infrared photographs were combined with in situ data for the assessment of water quality parameters within the San Francisco Bay-Delta. The water quality parameters of interest included turbidity and suspended solids. The U-2 photography and water quality samples were obtained simultaneously and coincidently with Landsat overpass. Regression models were developed between each of the water quality parameter measurements and Landsat digital data for 29 pre-selected sample sites. These regression models were then extended to the entire study area for mapping the water quality parameters of interest. The results included a series of colour-coded maps, each pertaining to one of the water quality parameters, and the statistical summaries. Areas of relatively high biological activity were clearly discernible on digitally enhanced Landsat MSS data.     

Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data

Estimates of sediment load are required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods for simulating the suspended sediment load. In this study artificial neural networks (ANNs) are employed to estimate the daily total suspended sediment load on rivers. Two different ANN algorithms, the feed-forward back-propagation (FFBP) method and the radial basis functions (RBF), were used for this purpose. The neural networks are trained using rainfall flow and suspended sediment load data from the Juniata Catchment, USA. The simulations provided satisfactory simulations in terms of the selected performance criteria comparing well with conventional multi-linear regression. Similarly, the simulated sediment load hydrographs obtained by two ANN methods are found closer to the observed ones again compared with multi-linear regression.     

Estimation of insect infestation dynamics using a temporal sequence of Landsat data

The current outbreak of mountain pine beetle (Dendroctonus ponderosae) in western Canada has been increasing over the past decade and is currently estimated to be impacting 9.2 million hectares, with varying levels of severity. Large area insect monitoring is typically undertaken using manual aerial overview sketch mapping, whereby an interpreter depicts areas of homogenous insect attack conditions onto 1:250,000 or 1:100,000 scale maps. These surveys provide valuable strategic data for management at the provincial scale. The coarse spatial and attribute resolution of these data however, make them inappropriate for fine-scale monitoring and operational planning. For instance, it is not possible to estimate the initial timing of attack and year of stand death. In this study, we utilise eight Landsat scenes collected over a 14 year period in north-central British Columbia, Canada, where the infestation has gradually developed both spatially and temporally. After pre-processing and normalising the eight scenes using a relative normalisation procedure, decision tree analysis was applied to classify spectral trajectories of the Normalised Difference Moisture Index (NDMI). From the classified temporal sequence of images, key parameters were extracted including the presence of beetle disturbance and timing of stand decline. The accuracy of discriminating beetle attack from healthy forest stands was assessed both spatially and temporally using three years of aerial survey data (1996, 2003, and 2004) with results indicating overall classification accuracies varying between 71 and 86%. As expected, the earliest and least severe attack year (1996), recorded the lowest overall accuracy. The relationship between the timing of stand attack (i.e. moderate to severe beetle infestation) and NDMI (initial year of detected disturbance) was also explored. The results suggest that there is potential for deriving regional estimates of the year of stand death using Landsat data and decision tree analysis however, a higher temporal frequency of images is required to quantify the timing of mountain pine beetle attack.     

Tracking bamboo dynamics in Zhejiang,China, using time-series of Landsat data from 1990 to 2014

Bamboo is an important vegetation type and provides a number of critical ecosystem services. Reliable and consistent information on bamboo distribution is required to better estimate its effect on climate change mitigation and socio-economic development. However, such information is rare over a large spatial area. In this study, we evaluate the contribution of different features in the identification of bamboo stands and determine a more discriminative set of features. We propose a bamboo mapping system including feature extraction and feature selection and derive the long-term trends of bamboo distribution in Zhejiang Province, China, using time-series of Landsat data from 1990 to 2014, with an increment of 5 years (1990, 1995, 2000, 2005, 2010, and 2014). The resultant maps of bamboo in the six epochs were evaluated using independent validation samples. The overall accuracies (OAs) of all six epochs range from 85.9% to 90.7%. We found that bamboo distribution in Zhejiang substantially increased from 1990 to 2014, particularly during the 2000s. Based on the produced maps, the area of bamboo in this region increased from 5363 ± 490 km² in 1990 to 11671 ± 653 km² in 2014, which is consistent with the National Forest Resource Inventory (NFRI) data. Our study demonstrates the capability of time-series of Landsat data for continuous monitoring of bamboo at a large spatial scale.     

应用Landsat TM影像估算渤海叶绿素a和总悬浮物浓度

利用23个实测样点的渤海叶绿素a和总悬浮物浓度数据及同步Landsat TM影像数据,分别分析了Landsat TM离水辐射亮度对渤海叶绿素a和总悬浮物浓度的敏感性,选择合适的波段,通过回归分析构建了基于Landsat TM离水辐射亮度的渤海叶绿素a和总悬浮物浓度反演模型。结果表明,TM1、TM2和TM3波段对叶绿素a的敏感性较高,以TM4/TM1和TM3/TM2的对数为自变量,以叶绿素a浓度的对数为因变量的线性估算模型可以有效反演渤海叶绿素a浓度,决定系数R2达到0.97;TM3波段对悬浮物的敏感性最高,以TM2、TM3和TM3/TM2为自变量,以总悬浮物浓度的以10为底的对数为因变量的多元线性模型获得的结果最佳,决定系数R2达到0.91。     

Chlorophyll distribution in Lake Kinneret determined from Landsat Thematic Mapper data

Chlorophyll distribution in Lake Kinneret was estimated at a time of low chlorophyll concentrations (3-7 mgm^?3). Landsat Thematic Mapper (TM) data were acquired three days after the acquisition of high spectral resolution radiometric measurements in the range 400 to 750 nm, chlorophyll and suspended matter concentrations, and Secchi disk transparency at 22 stations. The radiometric data were used to create an algorithm for estimation of chlorophyll concentration from the TM data. The radiance in channel TM3 (620-690 nm) was primarily dependent upon non-organic suspended matter concentration. Radiance in this channel was substracted from radiance in TM1 (450-520 nm) to correct for the additional radiance caused by scattering of non-pigmented suspended particles and (TM1 – TM3)/TM2 was found to be a useful index for estimating chlorophyll concentration. The concentrations calculated from atmospherically corrected TM data were compared to chlorophyll extracted from lake water samples. The estimation error of chlorophyll concentration was less than 0.85 mgm^?3.     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

High spatial resolution sea surface climatology from Landsat thermal infrared data

High spatial and temporal resolution maps of sea surface temperature (SST) have numerous applications in coastal and estuarine systems. A climatology map, tracking SST as a function of year-day, was produced at Southern New England using 53 Landsat TM and ETM+ thermal infrared data. A recursive curve-fitting algorithm was used to fit these data and eliminate cloud contamination, resulting in an average daily temperature at every 60-m pixel. The climatology was validated against long-term in situ records that were analyzed with the same techniques. The results show, as expected, that isolated and shallow water bodies undergo more extreme temperature variation (−2 to 25 °C) than deeper, well-connected embayments (1 to 21 °C) or the coastal ocean (4 to 18 °C). The coastal ocean is shown to lag insolation and shallow lakes by up to 44 days, with embayments showing a gradation between these extremes. Despite the subtle temperature range variation, there is rich detail in the spatial patterns which are relevant to the applied sciences of coastal and estuarine systems. The spatial pattern of the climatology reveals anomalous patterns, such as occur where anthropogenic forcing alters climatological patterns. The heat budget of Mount Hope Bay in northeast Narragansett Bay has anthropogenic thermal input from a large power plant, and this input is reflected in the climatology. From the results, it is seen that Narragansett Bay has, on average, a mean annual temperature of 11.86±0.41 °C, while the Mount Hope Bay system is consistently warmer at 12.30±0.21 °C and shows a delayed response to autumn cooling. The long history of Landsat data acquisition can be used to create a climatology of coastal and estuarine scale dynamics at an order of magnitude finer scale resolution than AVHRR climatologies.     

Cover Discrimination of areas susceptible to earthquake-induced liquefaction from Landsat data

Abstract

This paper presents a method for navigating AVHRR images in realtime without resetting to ground control points. The satellite orbital elements used for the orbit extrapolation are calculated by the ARGOS Data Collection and Location System. The method has been tested by ground control point location on ten NOAA-9 AVHRR images and gives satisfactory results; the positioning error varies from 1-6 to 10-4km with a mean value of 4-7km.     

Estimating suspended sediment concentrations from spectral reflectance data

Abstract

A method for estimating sediment concentrations at different depths in coastal waters from measured spectral reflectance above the sea is proposed. The water backscattering coefficient is obtained by integrating a scattering cross section derived from Mie theory over a Junge type particle-size distribution. The results for turbid water, near an arid coast, with vertical mixing, shows a maximum of particle concentration in the middle layer, while the same method applied to reflectance data for ‘blue’ water, gives a relative concentration of sediment that gradually increases with depth.     

Validation of North American Forest Disturbance dynamics derived from Landsat time series stacks

The North American Forest Dynamics (NAFD) study is a core project of the North American Carbon Program (NACP). The NAFD project is evaluating forest disturbance patterns and rates of disturbance by integrating U.S. Department of Agriculture (USDA) Forest Service Inventory and Analysis (FIA) field observations with temporally dense time series Landsat imagery. In Phase I of NAFD forest disturbance history was derived for 23 U.S. sample locations over the time period 1984 to 2005 from biennial Landsat time series stacks (LTSS). This study evaluates the accuracy of these Phase I NAFD disturbance history maps for 6 selected sample locations. We evaluate the disturbance maps using 2 reference datasets: 1) a design-based approach incorporating visual analysis of the LTSS in tandem with high resolution imagery and 2) the USDA FIA field observations. Overall accuracy for the NAFD disturbance product assessed at the individual time step level range from 77% to 86%. We examine the success rates of the mapping approach for capturing different types of disturbance and find that 82% of stand clearing events were detected. When we aggregate the data into change and no change categories the accuracy of stand clearing disturbance samples improved to over 92%. The majority of error in the disturbance maps was due to misclassification of partial disturbance as unchanged forest. We analyze the resulting errors of commission and omission as related to both reference datasets for each LTSS and present examples to illustrate the strengths and weaknesses of Phase I NAFD approach. In addition, we discuss the map biases observed in this work and what this may imply for estimating national forest disturbance rates with this approach.     

Suspended solids analysis using ERTS-A data

The magnetic digital tapes of the imagery obtained by ERTS-A on September 23, 1972, have been analyzed for selected areas of the Potomac River. A statistical analysis of all four bands has been carried out. The results show that band III is useful in determining the water-to-land interface. Data on bands II suggest the existence of three distinct types of water—those having low, medium, and high reflecti vity. From previously published results and ground truth measurements the areas of hight reflectivity were identified as containg high concentrations of suspended solids. Areas of low reflectivity were identified as having relatively lower concentrations of suspended solids. A commonly used computer technique with some additional refinements has been used to generate thematic maps which identify the above areas and show their geographical distribution.     

Long-term monitoring of citrus orchard dynamics using time-series Landsat data: a case study in southern China

ABSTRACT

Citrus orchard planting is a typical land-use change process that can impact terrestrial ecosystem services both locally and globally. Long-term monitoring of citrus orchard dynamics is critical for understanding its change patterns as well as the potential driving factors. Satellite remote-sensing imagery has been a primary data source for this purpose. However, most previous studies with multi-year intervals only captured some, but not all detailed information on citrus orchard expansion. In this study, we developed a framework for mapping annual citrus orchard extent and track its long-term dynamics in Xunwu County, China, using the historical Landsat repository from 1990 to 2016. The results suggested that the average overall accuracy of original annual mapping was 87.73%, and its performance was significantly improved after the temporal filtering approach (91.46%). Several features (e.g. elevation, slope, normalized difference vegetation index) played more important roles in citrus orchard identification. With the achieved annual mapping layers, we found a rapid citrus orchard expansion trend during the study interval (i.e. from 22.18 to 697.21 km²). Moreover, this expansion process was unevenly distributed in time. Spatially, emerging citrus orchards were primarily transformed from forests and croplands and mainly distributed in areas with elevations from 200 to 500 m and slopes range from 5° to 20°. This study demonstrated the potential of mapping citrus orchard dynamics at a higher temporal frequency with remote-sensing time-series, which can contribute to providing reference for sustainable land-use policy.     

Modeling inland water quality using Landsat data

The water quality parameters turbidity and algal pigment concentration of freshwater lakes have been modeled and predicted using Landsat multispectral scanner data as multiple linear predictors. Satellite data for an area in South East Australia from seven occasions during 1978 and 1979 were used along with concurrent ground-based measurements from sampling sites on three lakes covering a wide range of water quality regimes. Date-independent models for turbidity and algal pigment were obtained using the satellite multispectral data and the water quality data from up to 21 sampling sites on one lake on six occasions. The sun elevation at the time of satellite overpass was included in the models to account for differences between dates, and the time of sample collection was included to compensate for diurnal variations in pigment fluorescence. These models were used to successfully predict these water quality parameters for this lake on a new occasion and for the two other lakes on three occasions.     

Sensor-induced temporal variability of Landsat MSS data

Landsat-1 and Landsat-2 multispectral scanner (MSS) data were studied to determine the consistency of the calculated reflectance values over time. Data from six spectrally stable targets were collected over a 3-year period (1975–1977). Reflectance values calculated from the digital numbers were regressed against time to note any long term changes. Results indicate that, over a 1000-day period beginning 1 January 1975, MSS 2 reflectances were stable. MSS 1 reflectances over that same period decreased approximately 25–32%. The observed decreases are most likely due to a deterioration of the transmissive qualities of the Landsat-1 scanner's internal optical path. Although MSS temporal variation may be unique to MSS 1, time (days in orbit) should be considered as an independent variable when intersatellite calibration equations are computed. A case is made for a stable, monitored calibration system which would permit the calculation of true top-of-the-atmosphere reflectance measures.     

Spectral unmixing of Landsat Thematic Mapper data

The observed spectral signature of pixels in remote sensing imagery in most cases is the result of the reflecting properties of a number of surface materials constituting the area of a pixel. Despite this knowledge most image classification techniques aim at labelling a pixel according to a singular surface category. An alternative product can be generated using spectral unmixing: a technique that strives to find the surface abundances of a number of spectral components together causing the observed spectral reflectance at a pixel. A stepwise approach to implement spectral unmixing in Landsat Thematic Mapper image analysis is proposed: (1) atmospheric calibration of the image data, (2) preselection of a large number of ‘candidate’ endmembers, (3) reduction to the most important spectral endmembers using spectral angle mapping, (4) finding the relative abundances of the endmembers through spectral unmixing analysis, (5) combining the abundance estimates into a final product comparable to a classified image, and (6) accuracy assessment. A Landsat Thematic Mapper image from southern Spain covering a large peridotite body with adjacent limestone and low-grade metamorphic rocks is used as an example to demonstrate the usefulness of unmixing.     

Sea and lake surface temperature retrieval from Landsat thermal data in Northern Germany

Landsat thermal data are employed to derive lake and sea surface temperatures. The limitations of this approach are obvious, since the calculation of surface temperatures based solely on image data requires at least two thermal bands to compensate the atmospheric influence which is mainly caused by water vapour absorption. However, the 1 km spatial resolution of currently available multi‐band thermal satellite sensors (NOAA‐AVHRR, MODIS) is often not appropriate for lake and coastal zone applications. Therefore, it is worthwhile investigating the accuracy which can be obtained with single‐band thermal data using radiosonde information of the atmospheric water vapour column from meteorological stations in the study area. In addition, standard atmospheres from the MODTRAN code were considered that are based on seasonal climatologic values of water vapour, e.g. mid‐latitude summer, mid‐latitude winter, etc.

The study area of this investigation comprises various lakes and coastal zones of the Baltic Sea in NE Germany. Landsat‐7 ETM+ imagery of nine acquisition dates was selected covering the time span from February to November 2000. Results of derived lake and sea surface temperatures were compared with in situ measurements and with an empirical model of the Deutscher Wetterdienst (Germany's National Meteorological Service, DWD). RMS deviations of 1.4 K were obtained for the satellite‐derived lake surface temperatures with respect to in situ measurements and 2.2 K with respect to the empirical DWD model. RMS deviations of 1.6 K were obtained with respect to in situ bulk temperatures in coastal zones of the Baltic Sea. This level of agreement can be considered as satisfactory given the principal constraints of this approach. A better accuracy can only be obtained with high spatial resolution (<100 m) multi‐band thermal instruments delivering imagery on an operational basis.     

Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data

The derivation of leaf area index (LAI) from satellite optical data has been the subject of a large amount of work. In contrast, few papers have addressed the effective model inversion of high resolution satellite images for a complete series of data for the various crop species in a given region. The present study is focused on the assessment of a LAI model inversion approach applied to multitemporal optical data, over an agricultural region having various crop types with different crop calendars. Both the inversion approach and data sources are chosen because of their wide use. Crops in the study region (Barrax, Castilla-La Mancha, Spain) include: cereal, corn, alfalfa, sugar beet, onion, garlic, papaver. Some of the crop types (onion, garlic, papaver) have not been addressed in previous studies. We use in-situ measurement sets and literature values as a priori data in the PROSPECT + SAIL models to produce Look Up Tables (LUTs). Those LUTs are subsequently used to invert Landsat-TM and Landsat-ETM+ image series (12 dates from March to September 2003). The Look Up Tables are adapted to different crop types, identified on the images by ground survey and by Landsat classification. The retrieved LAI values are compared to in-situ measurements available from the campaign conducted in mid July-2003. Very good agreement (a high linear correlation) is obtained for LAI values from 0.1 to 6.0. LAI maps are then produced for each of the 12 dates. The LAI temporal variation shows consistency with the crop phenological stages. The inversion method is favourably compared to a method relying on the empirical relationship between LAI and NDVI from Landsat data. This offers perspectives for future optical satellite data that will ensure high resolution and high temporal frequency.     

Assessing wildfire effects with Landsat thematic mapper data

We evaluated Landsat Thematic Mapper (TM) imagery to map forest survival after a wildfire using single-date and multi-date TM imagery. In addition, landscape patterns were measured to describe the wildfire effects on successional stage patterns, and their impacts on wildlife habitat. The study site was the 1991 Warner Creek Burn, covering 3669 ha, on the Willamette National Forest in western Oregon, USA. Regressions of TM band transformations were used to estimate forest survival. Single-date TM 4/5 accounted for 73% (P 0.0001) of the variation in post-burn canopy cover, whereas the TM difference (by ratio) imagery with stratification by pre-fire tasseled cap (TC) wetness explained 78% (P 0.0001). Verification of the best models using additional data in observed versus predicted post-burn canopy cover confirmed these results. The pre-fire landscape had a matrix of closed mature/old-growth stands comprising 77% of the area. After the burn, the early seral/rock stage expanded, the open mature/old-growth stage was created, and the closed mature/old-growth stage was reduced and fragmented. Overall habitat diversity and edge increased after the burn, but interior habitat decreased.