Spectral discrimination of coral mortality states following a severe bleaching event

Although relatively easy to distinguish the spectra of bleached and living coral, once corals have died their skeletons remained bleached (white) for only a short period. Rapid colonisation by algae can give rise to pigmentation that may be similar to that of living coral. Thus, by the time remotely sensed imagery has been acquired, discrimination of live and dead corals is no longer facile. Field measurements of spectral reflectance of live and algal-colonised dead corals (arising from different mortality events) were made in French Polynesia. Derivative analysis revealed wavelengths and slope characteristics that could be used to discriminate between mortality states with an accuracy of ～ 85%. These results encourage application of hyperspectral remote sensing to quantitatively assess the extent of coral bleaching events.     

Quantification of two decades of shallow-water coral reef habitat decline in the Florida Keys National Marine Sanctuary using Landsat data (1984–2002)

The loss of coral reef habitats has been witnessed at a global scale including in the Florida Keys and the Caribbean. In addition to field surveys that can be spatially limited, remote sensing can provide a synoptic view of the changes occurring on coral reef habitats. Here, we utilize an 18-year time series of Landsat 5/TM and 7/ETM+ images to assess changes in eight coral reef sites in the Florida Keys National Marine Sanctuary, namely Carysfort Reef, Grecian Rocks, Molasses Reef, Conch Reef, Sombrero Reef, Looe Key Reef, Western Sambo and Sand Key Reef. Twenty-eight Landsat images (1984–2002) were used, with imagery gathered every 2 years during spring, and every 6 years during fall. The image dataset was georectified, calibrated to remote sensing reflectance and corrected for atmospheric and water-column effects. A Mahalanobis distance classification was trained for four habitat classes (‘coral’, ‘sand’, ‘bare hardbottom’ and ‘covered hardbottom’) using in situ ground-truthing data collected in 2003–2004 and using the spectral statistics from a 2002 image. The red band was considered useful only for benthic habitats in depths less than 6 m. Overall mean coral habitat loss for all sites classified by Landsat was 61% (3.4%/year), from a percentage habitat cover of 19% (1984) down to 7.6% (2002). The classification results for the eight different sites were critically reviewed. A detailed pixel by pixel examination of the spatial patterns across time suggests that the results range from ecologically plausible to unreliable due to spatial inconsistencies and/or improbable ecological successions. In situ monitoring data acquired by the Coral Reef Evaluation and Monitoring Project (CREMP) for the eight reef sites between 1996 and 2002 showed a loss in coral cover of 52% (8.7%/year), whereas the Landsat-derived coral habitat areas decreased by 37% (6.2%/year). A direct trend comparison between the entire CREMP percent coral cover data set (1996–2004) and the entire Landsat-derived coral habitat areas showed no significant difference between the two time series (ANCOVA; F-test, p = 0.303, n = 32), despite the different scales of measurements.     

Fusion of multisensor multitemporal satellite data for land cover mapping

Efficient integration of remote sensing information with different temporal, spectral and spatial resolutions is important for accurate land cover mapping. A new temporal fusion classification (TFC) model is presented for land cover classification, based on statistical fusion of multitemporal satellite images. In the proposed model, the temporal dependence of multitemporal images is taken into account by estimating transition probabilities from the change pattern of a vegetation dynamics indicator (VDI). Extension of this model is applicable to Synthetic Aperture Radar (SAR) images and integration of multisensor multitemporal satellite images, concerning both temporal attributes and reliability of multiple data sources. The feasibility of the new method is verified using multitemporal Landsat Thematic Mapper (TM) and ERS SAR satellite images, and experimental results show improved performance over conventional methods.     

A new image classification approach for mapping coral density in State of Kuwait using high spatial resolution satellite images

Remote sensing technology can be a valuable tool for mapping coral reef ecosystems. However, the resolution capabilities of remote sensors, the diversity and complexity of coral reef ecosystems, and the low reflectivity of marine environments increase the difficulties in identifying and classifying their features. This research study explores the capability of high spatial resolution (WorldView-2 (WV-2) and Pleiades-1B) and low spatial resolution (Land Remote-Sensing Satellite (Landsat 8)) multispectral (MS) satellite sensors in quantitatively mapping coral density. The Kubbar coral reef ecosystem, located in Kuwait’s southern waters, was selected as the research site. The MS imagery of WV-2, Pleiades-1B and Landsat 8 were, after geometric and radiometric assessment and corrections, subjected to new image classification approach using a Multiple Linear Regression (MLR) analysis. The new approach of MLR coral density analysis used the dependent variable of coral density percentage from ground truth and independent variables of spectral reflectance from selected imagery, depth (as estimated from a surface derived from bathymetric charts) and distance to land or reef unit centre. Accuracy assessment using independent ground truth was performed for the selected approach and satellite sensors to determine the quality of the information derived from image classification processes. The results showed that coral density maps developed using the MLR coral density model proved to have some level of reliability (radiometrically corrected WV-2 image (the coefficient determination denoted as R-squared (R²) = 0.5, Root-Mean-Square Error (RMSE) = 10) and radiometrically corrected Pleiades-1B image (R² = 0.8, RMSE = 10)). This study suggested using high spectral resolution data and including additional factors (variables) (e.g. water turbidity, temperature and salinity) could contribute to improving the accuracy of coral density maps produced by application of the MLR model; however, all of these would add cost and effort to the mapping process. The outcomes of this research study provide coral reef ecosystem researchers, managers, and decision makers a tool to determine and map coral reef density in more detail than in the past. It will help quantify coral density at particular points in time leading to estimates of change, and allow coral reef ecologists to identify the current coral reef habitat health status, distribution and extent.     

Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa

With increased availability of satellite data products used in mapping surface energy balance and evapotranspiration (ET), routine ET monitoring at large scales is becoming more feasible. Daily satellite coverage is available, but an essential model input, surface temperature, is at 1 km or greater pixel resolution. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different crop covers is severely hampered. The effect of sensor resolution on model output for an agricultural region in central Iowa is examined using Landsat data collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This study was conducted in concert with the Soil Moisture Experiment 2002 (SMEX02). Two images collected during a rapid growth period in soybean and corn crops are used with a two-source (soil+vegetation) energy balance model, which explicitly evaluates soil and vegetation contributions to the radiative temperature and to the net turbulent exchange/surface energy balance. The pixel resolution of the remote sensing inputs are varied from 60 m to 120, 240, and 960 m. Model output at high resolution are first validated with tower and aircraft-based flux measurements to assure reliability of model computations. Histograms of the flux distributions and resulting statistics at the different pixel resolutions are compared and contrasted. Results indicate that when the input resolution is on the order of 1000 m, variation in fluxes, particularly ET, between corn and soybean fields is not feasible. However, results also suggest that thermal sharpening techniques for estimating surface temperature at higher resolutions (∼250 m) using the visible/near infrared waveband resolutions could provide enough spatial detail for discriminating ET from individual corn and soybean fields. Additional support for this nominal resolution requirement is deduced from a geostatistical analysis of the vegetation index and surface temperature images.     

一种稳健的多时相遥感图像相对辐射校正方法

变化检测是通过分析多时相遥感图像实现土地利用动态监视的一种有效方法，但在变化检测 分析前，需要经过辐射校正消除光照等因素对地物光谱辐射的影响，使同一地物在不同时相 影像中具有相同的辐射量。根据地物在不同时相遥感图像中的光谱特性满足线性关系的 特点，提出一种自动实现多时遥感图像相对辐射校正的稳健方法，首先通过最小差分回归找 出非变化地物在多时相遥感图像中的辐射关系 |然后利用变化区域证实过程消除变化区域对 辐射校正处理的影响 |最后通过循环迭代实现图像间的辐射校正。提出的方法不仅可以自动 地实现多时相遥感图像的相对辐射校正，而且能够保证图像的辐射分辨率不会因为辐射校正而降低。     

Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing

Coral reef benthic communities are mosaics of individual bottom-types that are distinguished by their taxonomic composition and functional roles in the ecosystem. Knowledge of community structure is essential to understanding many reef processes. To develop techniques for identification and mapping of reef bottom-types using remote sensing, we measured 13,100 in situ optical reflectance spectra (400-700 nm, 1-nm intervals) of 12 basic reef bottom-types in the Atlantic, Pacific, and Indian Oceans: fleshy (1) brown, (2) green, and (3) red algae; non-fleshy (4) encrusting calcareous and (5) turf algae; (6) bleached, (7) blue, and (8) brown hermatypic coral; (9) soft/gorgonian coral; (10) seagrass; (11) terrigenous mud; and (12) carbonate sand. Each bottom-type exhibits characteristic spectral reflectance features that are conservative across biogeographic regions. Most notable are the brightness of carbonate sand and local extrema near 570 nm in blue (minimum) and brown (maximum) corals. Classification function analyses for the 12 bottom-types achieve mean accuracies of 83%, 76%, and 71% for full-spectrum data (301-wavelength), 52-wavelength, and 14-wavelength subsets, respectively. The distinguishing spectral features for the 12 bottom-types exist in well-defined, narrow (10-20 nm) wavelength ranges and are ubiquitous throughout the world. We reason that spectral reflectance features arise primarily as a result of spectral absorption processes. Radiative transfer modeling shows that in typically clear coral reef waters, dark substrates such as corals have a depth-of-detection limit on the order of 10-20 m. Our results provide the foundation for design of a sensor with the purpose of assessing the global status of coral reefs.     

Multitemporal spectral analysis for cheatgrass (Bromus tectorum) classification

Operational satellite remote sensing data can provide the temporal repeatability necessary to capture phenological differences among species. This study develops a multitemporal stacking method coupled with spectral analysis for extracting information from Landsat imagery to provide species‐level information. Temporal stacking can, in an approximate mathematical sense, effectively increase the ‘spectral’ resolution of the system by adding spectral bands of several multitemporal images. As a demonstration, multitemporal linear spectral unmixing is used to successfully delineate cheatgrass (Bromus tectorum) from soil and surrounding vegetation (77% overall accuracy). This invasive plant is an ideal target for exploring multitemporal methods because of its phenological differences with other vegetation in early spring and, to a lesser degree, in late summer. The techniques developed in this work are directly applicable for other targets with temporally unique spectral differences.     

A review of regional science applications of satellite remote sensing in urban settings

This paper reviews the potential applications of satellite remote sensing to regional science research in urban settings. Regional science is the study of social problems that have a spatial dimension. The availability of satellite remote sensing data has increased significantly in the last two decades, and these data constitute a useful data source for mapping the composition of urban settings and analyzing changes over time. The increasing spatial resolution of commercial satellite imagery has influenced the emergence of new research and applications of regional science in urban settlements because it is now possible to identify individual objects of the urban fabric. The most common applications found in the literature are the detection of urban deprivation hot spots, quality of life index assessment, urban growth analysis, house value estimation, urban population estimation and urban social vulnerability assessment. The satellite remote sensing imagery used in these applications has medium, high or very high spatial resolution, such as images from Landsat MSS, Landsat TM and ETM+, SPOT, ASTER, IRS, Ikonos and QuickBird. Consistent relationships between socio-economic variables derived from censuses and field surveys and proxy variables of vegetation coverage measured from satellite remote sensing data have been found in several cities in the US. Different approaches and techniques have been applied successfully around the world, but local research is always needed to account for the unique elements of each place. Spectral mixture analysis, object-oriented classifications and image texture measures are some of the techniques of image processing that have been implemented with good results. Many regional scientists remain skeptical that satellite remote sensing will produce useful information for their work. More local research is needed to demonstrate the real potential and utility of satellite remote sensing for regional science in urban environments.     

The utility of multi-sensor data for mapping eroded lands

The availability of remote sensing data with improved spatial, spectral and radiometric resolution is now available to fully exploit their potential for a specific application subject to the relative merits and the limitations of each sensor's data. Presented here is a case study where Landsat MSS and TM; and SPOT MLA data for part of the Bijapur district, southern India, which were acquired on the same day, have been evaluated for mapping eroded lands. The approach involves the geometric registration of all three data to a common map grid using tie points and third order polynomial transform; and resampling the MSS and TM data to a 20m by 20 m pixel dimension and radiometric normalization. Thematic maps showing eroded lands were generated on a micro-VAXbased DIPIX system using a maximum likelihood classifier. Accuracy estimates were made for the thematic maps following stratified unaligned random sampling technique, and subsequently, computing overall accuracy and Kappa coefficient. Spectral separability and classification accuracy was maximum from SPOT-MLA data followed by a combination of Landsat MSS band 1, SPOT-MLA band 2 and Landsat TM band 4; Landsat TM, a combination of Landsat MSS, TM and SPOT MLA; and Landsat MSS data.     

Cover. ERS-1 SAR interferogram of Mount Vesuvio (Italy)

A pixel block intensity modulation (PRIM) method has been developed to add spatial detail to Landsat Thematic Mapper (TM) thermal band TM6 images in regions with sufficient topography. The method uses 30 m resolution TM reflective spectral band images (TM1-5 and 7) to modulate the relevant TM6 image on the basis of its 120m resolution thermal pixel blocks. Topographic detail in each 120m resolution pixel block of the TM6 image is thus recovered, without altering the average thermal digital number level of the block, by the spatial information recorded in the reflective spectral bands at 30m resolution. Tests confirm that the PBIM can effectively integrate topographic spatial detail from reflective spectral bands with TM6 images while retaining the fidelity of the original thermal spectral information. PBIM is also applicable, as a general method, for data fusion of multispectral and panchromatic images with different spatial resolutions. Bearing in mind that, for space-borne remote sensing, the spatial resolution of the thermal band will continue to be lower than that of VISSWIR and panchromatic bands in the multispectral sensor systems of the next generation, such as Enhanced Thematic Mapper Plus, the PBIM method will remain a useful technique for enhancing thermal imagery data for some time.     

Estimation of aboveground grassland phytomass with a growth model using Landsat TM and climate data

Estimation of aboveground phytomass in meadow grasslands was carried out using multitemporal satellite data of fine resolution but of low frequency from Landsat TM observations in 1984-1990. We developed two growth models for the estimation of the first-cut yield, based on an exponential plant growth of which initial phytomass was determined using NDVI or TM2/TM3 on the Landsat observation date together with the effective cumulative temperature during the growth period after the observation date. Validations of these models with different data sets of Landsat TM in 1990-1994 indicated that the measured and estimated yields agreed well, suggesting the great potential of applying fine resolution satellite data coupled with a growth model to phytomass study, in spite of the low frequency of observation.     

Combining Landsat images with historic records to estimate the live coral cover of Luhuitou fringing reef in Northern South China Sea

As an efficient indicator of coral reef health, live coral cover (LCC) is regularly surveyed and recorded by many coral reef documents. However, there usually exist some blanks for the historic records, while current in-field surveys are impossible to fill the blanks. To overcome such difficulties, we focus on exploiting the potential of optical satellite images. The purpose is to fill the blanks of the records over the past and further estimate the LCC in future. As historic records were usually lack of accurate geographical locations to match to the satellite images, a spectral index was defined based on the mean of the subsurface remote sensing reflectance. The index was then used to link the LCC with the satellite images by a cubic polynomial function. Thereafter, the LCC and the coefficients of the polynomial function were finally estimated by simultaneously combining the mean subsurface remote sensing reflectance, the historic LCC records, and the constraints among LCC in adjacent years. Experiments on a series of Landsat images of Luhuitou fringing reef (1973 to 2018) demonstrated that the proposed method is effective and feasible, where the introduction of the satellite images can greatly improve the accuracy. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Errors (MRE) of the LCC were able to reach 5.4%, 4.0%, and 15.9% respectively. This is regarded as the first test on LCC estimation by combining such a long-term LCC records with a series of satellite images.     

The effects of ecologically determined spatial complexity on the classification accuracy of simulated coral reef images

Numerous studies have been conducted to compare the classification accuracy of coral reef maps produced from satellite and aerial imagery with different sensor characteristics such as spatial or spectral resolution, or under different environmental conditions. However, in additional to these physical environment and sensor design factors, the ecologically determined spatial complexity of the reef itself presents significant challenges for remote sensing objectives. While previous studies have considered the spatial resolution of the sensors, none have directly drawn the link from sensor spatial resolution to the scale and patterns in the heterogeneity of reef benthos. In this paper, we will study how the accuracy of a commonly used maximum likelihood classification (MLC) algorithm is affected by spatial elements typical of a Caribbean atoll system present in high spectral and spatial resolution imagery.The results indicate that the degree to which ecologically determined spatial factors influence accuracy is dependent on both the amount of coral cover on the reef and the spatial resolution of the images being classified, and may be a contributing factor to the differences in the accuracies obtained for mapping reefs in different geographical locations. Differences in accuracy are also obtained due to the methods of pixel selection for training the maximum likelihood classification algorithm. With respect to estimation of live coral cover, a method which randomly selects training samples from all samples in each class provides better estimates for lower resolution images while a method biased to select the pixels with the highest substrate purity gave better estimations for higher resolution images.     

利用TM6数据反演陆地表面温度新算法研究

陆地表面温度(LST)反演一直是热红外遥感研究中的一大难题。虽然TM 6数据具有较高的空间分辨率(120 m),但由于只有一个热通道,要得到地表真实温度,原来需要利用辐射传输方程的方法,实时资料的缺乏限制了该方法的应用。因而由TM 6数据得到的通常都是星上亮度温度,而星上亮度温度与实际地表温度差距较大,因此,其反演的温度精度不高。而单窗算法和普适性单通道算法的提出为从TM 6数据较高精度地反演陆地表面温度提供了可能。分析和研究了这两个新的单通道温度反演算法,并针对北京市的实际情况,利用2005年5月6日的TM数据对北京市的陆地表面温度进行了反演,并用实地测量数据进行了比较验证。结果表明这两种温度反演算法都取得了较高的精度,它们的rm sd值分别为1.38°和2.18°。     

Simultaneous estimation of benthic fractional cover and shallow water bathymetry in coral reef areas from high-resolution satellite images

This article describes the development of a technique to estimate shallow water benthic cover and depth simultaneously from high-resolution satellite images of reef areas, specifically from the high-resolution sensor onboard IKONOS. The technique to derive the estimates of five bottom benthic cover types (sand, coral, seagrass, macroalgae and pavement) and depth from the four-band images uses a coupling of radiative transfer (RT) theory and spectral unmixing implemented in an iterative manner. To resolve the cover types for the unmixing, the method employed a combinatorial approach to select benthic cover composition. The estimation technique was applied to two reef areas around the coast of the Ishigaki in southern Ryukyus, namely, the Fukido River mouth area and the Shiraho Reef. The IKONOS images of Fukido River mouth area and Shiraho Reef were acquired in 2003 and 2002, respectively. The accuracy of the fractional cover and the depth estimates from the satellite images are then presented and compared with sea truth data and depth measurements. The results indicate good correspondence between estimated and measured depths, while the estimates for the benthic cover were at reasonable levels of accuracy.     

Evaluating MODIS data for mapping wildlife habitat distribution

Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect habitat data in the field. Many habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolution data are too coarse for habitat mapping. In this study we compare two models using data from different satellite sensor systems for mapping the spatial distribution of giant panda habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derived from a digital elevation model obtained from topographic maps of the study area). The second model is based on the Ecological Niche Factor Analysis (ENFA) of a time series of weekly composites of WDRVI (Wide Dynamic Range Vegetation Index) images derived from MODIS (Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. Results showed that the model using the seasonal variability of MODIS-WDRVI had a similar prediction success to that using Landsat TM and digital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provides an appropriate environmental predictor for giant panda habitat mapping. Therefore, the information contained in remotely sensed data acquired with low spatial resolution but high frequency of acquisitions has considerable potential for mapping the habitat distribution of wildlife species.     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

Change detection using remote sensing in a reef environment of the UAE during the extreme event of El Niño 2015–2016

ABSTRACT

Coral reefs of the United Arab Emirates (UAE) are living in the world’s hottest sea. Recently, corals harbouring Symbiodinium thermophilum, a thermotolerant microalgae, were found to be prevalent among UAE reefs and were reported to endure extreme sea-surface temperatures. Late 2015–early 2016 was marked with the strongest El Niño on record worldwide, which caused massive coral bleaching (loss of symbiotic microalgae from reef-building corals). In September 2015, the waters flanking UAE coasts were identified to be among the areas facing a thermal stress reaching its highest level liable to cause massive coral bleaching. However, the effect of this thermal stress on UAE corals remained largely unknown. Here, multi-temporal DubaiSat-2 satellite images were used to show that changes in the reef environment of Dalma Island, UAE, between 2014 and 2016, occurred in macroalgae-dominant habitats, whereas live corals remained unaltered. Furthermore, extending the study to a larger area helped in discovering a continuum of live and pristine corals, which was not reported or studied before. While sea-surface temperature anomalies of 1°C were reported to significantly damage coral reefs around the world, the live coral habitat was observed to exhibit no-change despite four consecutive months of +2°C to 3°C anomalies reported during the study period. These findings point to the tolerance of UAE live corals faced with extreme climate conditions.     

Mapping the distribution of coral reefs and associated sublittoral habitats in Pacific Panama: a comparison of optical satellite sensors and classification methodologies

This research compared the ability of Landsat ETM+, Quickbird and three image classification methods for discriminating amongst coral reefs and associated habitats in Pacific Panama. Landsat ETM+ and Quickbird were able to discriminate coarse and intermediate habitat classes, but this was sensitive to classification method. Quickbird was significantly more accurate than Landsat (14% to 17%). Contextual editing was found to improve the user's accuracy of important habitats. The integration of object‐oriented classification with non‐spectral information in eCognition produced the most accurate results. This method allowed sufficiently accurate maps to be produced from Landsat, which was not possible using the maximum likelihood classifier. Object‐oriented classification was up to 24% more accurate than the maximum likelihood classifier for Landsat and up to 17% more accurate for Quickbird. The research indicates that classification methodology should be an important consideration in coral reef remote sensing. An object‐oriented approach to image classification shows potential for improving coral reef resource inventory.