Spectral Mixture Analysis Research Articles

Comparison of pixel unmixing models in the evaluation of post-fire forest resilience based on temporal series of satellite imagery at moderate and very high spatial resolution

In Mediterranean fire-prone ecosystems, shifts in fire regime as a consequence of global change could modify the resilience of vegetation communities. In this paper, we aim to compare the efficiency of high and moderate spatial resolution satellite imagery in the evaluation of resilience in a fire-prone landscape under different fire regime categories using two pixel unmixing techniques. A time series of Landsat (ETM+ and OLI; spatial resolution of 30 m) and WorldView-2 (spatial resolution of 2 m) imagery collected between 2011 (pre-fire conditions) and 2016 were used to estimate the temporal variation of fractional vegetation cover (FVC) as a quantitative measure of forest resilience. For this time series, FVC was computed under four fire-regime categories of recurrence and severity using two approaches: dimidiate pixel model and multiple endmember spectral mixture analysis (MESMA). The dimidiate pixel model was computed using NDVI as spectral response for the case of Landsat imagery and NDVI and red-edge NDVI (RENDVI) for WorldView-2. MESMA was applied to unmix WorldView-2 and Landsat imagery into four fraction images: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), soil and shade. The PV shade normalized fraction corresponds to the FVC. In summer of 2016 we established 85 30 × 30 m field plots and 360 2 × 2 m field plots to measure the percentage of total vegetation cover in order to validate the FVC estimates made from remote sensing data. The FVC time series showed the same general pattern with both spatial scales and modeling approaches, high fire recurrence categories registering the highest resilience. The accuracy of the dimidiate pixel model was significantly higher for WorldView-2 based estimates (RMSE: 5–10%) than for Landsat (RMSE: 10–15%). The dimidiate pixel model computed from NDVI for both Landsat and WorldView-2 underestimated FVC at high field-sampled vegetation cover, while MESMA estimations were accurate for the entire range of vegetation cover for both satellites. The fraction of photosynthetic vegetation calculated using WorldView-2 had a higher performance (RMSE: 4–6%) than that quantified from Landsat (RMSE: 6–8%). The linear relationships assumed for validation purposes were statistically significant for both sensors and modeling approaches. Our study demonstrates the highest performance of very high spatial resolution satellite imagery and MESMA models in the quantitative estimation of FVC as a measure of post-fire resilience.

Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm

Successful post-fire management depends on accurate burn severity maps that are increasingly derived from satellite data, replacing field-based estimates. Post-fire vegetation and soil changes, besides modifying the reflected and emitted radiation recorded by sensors onboard satellites, strongly alters water balance in the fire affected area. While fire-induced spectral changes can be well represented by fraction images from Multiple Endmember Spectral Mixture Analysis (MESMA), changes in water balance are mainly registered by evapotranspiration (ET). As both types of variables have a clear physical meaning, they can be easily understood in terms of burn severity, providing a clear advantage compared to widely-used spectral indices. In this research work, we evaluate the potential of Landsat-derived ET to estimate burn severity, together with MESMA derived Sentinel-2 fraction images and important environment variables (pre-fire vegetation, climate, topography). In this study, we use the random forest (RF) classifier, which provides information on variable importance allowing us to identify the combination of input variables that provided the most accurate estimate. Our study area is located in Central Portugal, where a mega-fire burned >450 km2 from 17 to 24 June 2017. We used the official burn severity map as ground reference. The RF algorithm identified ET as the most important variable in the burn severity model, followed by MESMA char fractions. When both ET and MESMA char fraction image were used as RF inputs, burn severity estimates reached higher accuracy than if only one of them was used, which suggests their potential synergetic interaction. In particular, when environmental variables were used in addition to ET and char fraction, the highest accuracy for burn severity was reached (κ = 0.79). Our main conclusion is that post-fire fine resolution ET is a useful and easily understandable indicator of burn severity in Mediterranean ecosystems, in particular when used in combination with a MESMA char fraction image. This novel approach to estimate burn severity may help to develop successful post-fire management strategies not only in Mediterranean ecosystems but also in other ecosystems, due to ease of generalization.

Development of integrated wetland change detection approach: In case of Erdos Larus Relictus National Nature Reserve, China

Wetlands are threatened by the global warming and the human exploitation pressure, and have been shrinking quickly in recent years. Timely and accurate wetland area change detection is the primary task for wetland conservation and restoration. The objective of this study is to develop an integrated change detection approach which integrates the advantages of spectral mixture analysis (SMA) and change vector analysis (CVA) for the change identification of wetland dynamics. In the proposed approach, water, vegetation and soil fractions of wetlands were derived by SMA; then, the detailed change information (including change magnitude and 12 change direction categories) were calculated through CVA. The proposed approach was applied for the wetlands change in Erdos Larus Relictus National Nature Reserve (ELRNNR), China, using time-series Landsat images during 1977–2017. We found that the wetland faced serious degradation, with water fraction changed to soil (5.79 km2), to vegetation (1.35 km2) and to both soil and vegetation (3.53 km2). From 1977 to 2000, a slight degradation occurred in the northeast edge of Bojiang Lake and a marginal degradation in Bojiang and Houjia Lakes inside the ELRNNR, with water fraction changed to soil and vegetation. During 2000–2010, severe degradation occurred in ELRNNR, and from 2010 to 2017, the wetland was more susceptible to the precipitation change and human activities. Analysis of the result indicated that the long-term drought and effects of mismanagement as well as misuse by human beings were the driving factors of wetland degradation. The proposed approach in this study achieves a higher accuracy than the classification approach to detect wetland change, with the ability to obtain more detailed change information.

Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method

Due to the fragmented compositional structure of urban scenes, many pixels are mixtures of multiple materials even in high spatial resolution airborne hyperspectral data. In the past ten years, sparse regression based spectral unmixing methods have achieved some noticeable results. Recently, Chen et al. proposed a jointly sparse spectral mixture analysis model for urban mapping. Their model has a high computational load, however, and wrongly detects a water component in residential areas due to the spectral confusion between water, shadow and other low-albedo land cover materials. In this paper, we propose to exclude water from the spectral mixture analysis in urban scenes. In order to decrease the computational load of Chen et al.’s approach, we propose a fast jointly sparse unmixing method. Our experiments demonstrate that the proposed method obtains a slightly degraded result but has a much lower computational load. It is fourteen times faster than their method, and only requires about one-ninth of the memory. A parallel implementation of the proposed method shows its potential to be applied in practical applications, especially in resource-constrained computational environments.

Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data

Earthquake-induced rubble in urbanized areas must be mapped and characterized. Location, volume, weight and constituents are key information in order to support emergency activities and optimize rubble management. A procedure to work out the geometric characteristics of the rubble heaps has already been reported in a previous work, whereas here an original methodology for retrieving the rubble’s constituents by means of active and passive remote sensing techniques, based on airborne (LiDAR and RGB aero-photogrammetric) and satellite (WorldView-3) Very High Resolution (VHR) sensors, is presented. Due to the high spectral heterogeneity of seismic rubble, Spectral Mixture Analysis, through the Sequential Maximum Angle Convex Cone algorithm, was adopted to derive the linear mixed model distribution of remotely sensed spectral responses of pure materials (endmembers). These endmembers were then mapped on the hyperspectral signatures of various materials acquired on site, testing different machine learning classifiers in order to assess their relative abundances. The best results were provided by the C-Support Vector Machine, which allowed us to work out the characterization of the main rubble constituents with an accuracy up to 88.8% for less mixed pixels and the Random Forest, which was the only one able to detect the likely presence of asbestos.

Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction

Crop biomass (Bio) is one of the most important parameters of a crop, and knowledge of it before harvest is essential to help farmers in their decision making. Both green and dry Bio can be estimated from vegetation spectral indices (VIs) because they have a close relationship with accumulated absorbed photosynthetically active radiation (APAR), which is proportional to total Bio. The aims of this study were to analyze the potential capacity of spectral vegetation indices in estimating corn green biomass based on their relationship with the photosynthetic vegetation sub-pixel fraction derived from spectral mixture analysis and to analyze the best interval of VI accumulation (days) for corn grain yield estimation. Field data of center pivots cultivated with corn during the irrigation seasons of 2015 and 2018 and Landsat 8 and Sentinel 2 images were used. The EVI produced the best results; Pearson's correlation coefficient, RMSE and Willmott’s index reached 0.99, 6.5%, and 0.948, respectively. Among the nine potential VIs analyzed, the EVI, SAVI and OSAVI were considered the first, second and third best performing for corn green Bio estimation, respectively, based on their comparison to the photosynthetic vegetation sub-pixel fraction (fPV), and the time intervals that extended until 120 days after sowing showed the best results for corn grain yield estimation.

Subpixel Mapping of Surface Water in the Tibetan Plateau with MODIS Data

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R2) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%).

Surface anthropogenic heat islands in six megacities: An assessment based on a triple-source surface energy balance model

Anthropogenic heat flux (AHF) is a main contributor to the formation of surface urban heat islands (SUHI). Megacities in particular are facing severe problems due to excessive population growth, urban area expansion, human activity, increased energy consumption, and increased anthropogenic heat. In this study, a physical modeling approach based on a triple-source surface energy balance (triple-SEB) model was developed to uncover the effect of AHF on land surface temperature (LST) and surface anthropogenic heat island (SAHI) intensity. For this purpose, satellite imagery along with climatic and meteorological data from 1985 to 2019 were studied for six selected megacities: Los Angeles, Atlanta, Athens, Istanbul, Tehran, and Beijing. First, LST and the fraction of different surface covers were calculated by using a single-channel algorithm and a normalized spectral mixture analysis model, respectively. In the second step, impervious surface cover (ISC) and the urban main boundary area (UMBA) of each city were extracted based on the biophysical composite index and city clustering algorithm, respectively. In the third step, anthropogenic LST (ALST) was modeled using a triple-SEB model. In the fourth step, the ALST and UMBA were used together to model SAHI intensity at different dates. Finally, the relationship between the estimated ALST and ISC, as well as between SAHI and ISC, was examined. Results show that the average value of estimated ALST for the megacities increased from 2.02, 0.55, 0.61, 0.64, 0.58, and 0.72 to 2.99, 1.73, 1.66, 1.19, 2.32, and 2.76 °C, respectively. The coefficient of determination between the mean value of ISC and the estimated ALST for all megacities yielded 0.8, which was higher than that between ISC and satellite-derived LST. Moreover, the SAHI intensity for these megacities was found to have increased to 0.73, 0.92, 0.95, 0.98, 0.95 and 1.32 °C, respectively, which can be predicted by ISC with a coefficient of determination of 0.78, 0.79, 0.79, 0.73, 0.71 and 0.52, respectively. This suggests that the triple-SEB model proposed by this study allowed for independent modeling of AHF's influence on SUHI and a better determination of the effect of ISC on LST and SUHI intensity. This approach facilitated comparative analysis of LST and SAHI for a city at different times as well as SAHIs in different cities with different geographic and climate settings.

A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing

Grassland degradation is an important research topic on a global scale, since it can severely restrict the development of animal husbandry and threaten ecological security. The proper monitoring of regional grassland degradation is the basis for strengthening grassland protection and restoration, as well as improving grassland ecology. In this study, the standards for monitoring grassland degradation at the regional level were established based on the field data measured in the study area and the data of a grazing-controlled experimental plot. We extracted the spectral characteristic parameters and carried out the spectral dimensionality reduction and accuracy evaluation using principal component analysis (PCA) and the multilayer perceptron neural network (MLPNN). Based on the EO-1 Hyperion images, multiple endmember spectral mixture analysis (MESMA) and the fully constrained least squares method pixel un-mixing (FCLS) were used to identify typical vegetation species and assess the degree of grassland degradation at the regional level per the established grassland degradation monitoring standards. This new method of monitoring grassland degradation from the perspective of the vegetation species composition not only makes grassland degradation monitoring more accurate, but also provides a reference for relevant studies.

Merging spatial and spectral datasets to place olivine in stratigraphic context at Arruntia crater, a rare window into Vesta's northern hemispheric crust

A major goal of the Dawn mission to Vesta was to test and refine current models of the asteroid's formation by characterizing the distribution of mineral components on its surface. Detection of the mineral olivine by the Visible and Infrared Mapping spectrometer (VIR) and the Framing Camera (FC) onboard Dawn was a key milestone in this effort, and was expected to help resolve a debate regarding the dominant mode of Vesta's petrologic evolution (i.e., magma ocean vs. serial magmatism). However, the subtleties of the olivine spectral signature combined with the small size of individual olivine-rich exposures (tens of meters) prohibits detailed mapping of this petrologically significant mineral component when the data from either instrument are evaluated independently. As a result, the particular role of these olivine exposures in Vesta's geologic history remains largely unresolved.Here, we fully characterize the type locality of Vesta's olivine-rich materials, the northern hemisphere craters Bellicia and Arruntia, using a novel data fusion approach for linking the VIR and FC datasets. Specifically, we have leveraged a spectral mixture analysis framework to create a new dataset that maps, or “extrapolates”, full resolution spectra, derived from VIR data, onto the higher spatial resolution of FC. When used as an exploratory tool in conjunction with the original FC and VIR data, the new “extrapolated” dataset reveals important new details about the distribution of olivine around Bellicia and Arruntia craters.There are clear compositional distinctions between the exposures at the two craters, with a much stronger olivine component in the walls of Bellicia than Arruntia. The proximal Arruntia ejecta appear more consistent with high proportions of an evolved high‑calcium pyroxene (as in eucrites) than with an enhanced olivine component. Examples of diogenite-rich howardite also occur in the region. Further, we leverage Arruntia's role as the freshest large crater in the northern hemisphere, and thus a rare window into the Vesta's northern hemispheric crust, to suggest an overall stratigraphy for the region: a thin veneer of howardite overlies a sequence of diogenite-rich (near surface), olivine-rich (intermediate), and eucrite-rich materials (at depth). The manner of exposure of these various components in a marked stratification point to a plutonic model for emplacing these mineralogically unique components in the near surface of Vesta.

Drought response of urban trees and turfgrass using airborne imaging spectroscopy

Vegetation growth and physiological functioning, including carbon uptake and transpiration, can be strongly affected by drought. During water-limited conditions, urban vegetation may be limited in its ability to provide ecosystem services, such as shading and evaporative cooling. While there has been extensive research on drought response in natural vegetation and agricultural systems using remote sensing, drought impacts on urban vegetation have remained understudied. With >50% of people worldwide living in cities and the increased potential for future droughts, it is important to understand how urban vegetation will respond and to evaluate potential impacts. Here, we used repeat flights of NASA airborne imaging spectroscopy (AVIRIS and AVIRIS-NG) to quantify the responses of common urban tree species and turfgrass in downtown Santa Barbara, California, USA to the 2012–2016 California drought. The effects of the drought on vegetation were quantified using several spectral indicators: fractional cover from multiple endmember spectral mixture analysis (MESMA), vegetation indices, and continuum removed absorption features. We used existing tree crown object polygons that were classified to species and had associated estimates of pre-drought leaf area index and biomass. We also included polygons of turfgrass parks and recreational fields. All tree species and turfgrass had four or more spectral indicators with significantly lower mean values (p < 0.05) during the drought in 2014, and evidence of recovery was observed for some species in 2017, which was after the drought had ended. In general, needleleaf and native trees were more affected than broadleaf and non-native trees, respectively, but with considerable interspecific variability. Based on the spectral indicators, turfgrass cover senesced in the middle of the drought but recovered soon after the drought ended, whereas the majority of tree species had not fully recovered. Mean green vegetation cover declined in most tree canopies during the drought, primarily exposing paved surfaces and/or non-photosynthetic vegetation, depending on the site type and tree patch size. Drought impacts as expressed in spectral indicators were generally more severe in tree patches with high pre-drought leaf area index, suggesting that denser, leafier canopies were more difficult for trees to maintain during the drought. Most spectral indicators were correlated with one another (R > 0.4), suggesting co-occuring adjustments of greenness, water content, and plant pigments.

Unsupervised Bayesian Subpixel Mapping of Hyperspectral Imagery Based on Band-Weighted Discrete Spectral Mixture Model and Markov Random Field

Although accurate training and initialization information is difficult to acquire, unsupervised hyperspectral subpixel mapping (SPM) without relying on this predefined information is an insufficiently addressed research issue. This letter presents a novel Bayesian approach for unsupervised SPM of hyperspectral imagery (HSI) based on the Markov random field (MRF) and a band-weighted discrete spectral mixture model (BDSMM), with the following key characteristics. First, this is an unsupervised approach that allows adjustment of abundance and endmember information adaptively for less relying on algorithm initialization. Second, this approach consists of the BDSMM for accommodating the noise heterogeneity and the hidden label field of subpixels in HSI. The BDSMM also integrates SPM into the spectral mixture analysis and allows enhanced SPM by fully exploring the endmember-abundance patterns in HSI. Third, the MRF and BDSMM are integrated into a Bayesian framework to use both the spatial and spectral information efficiently, and an expectation-maximization (EM) approach is designed to solve the model by iteratively estimating the endmembers and the label field. Experiments on both simulated and real HSI demonstrate that the proposed algorithm can yield better performance than traditional methods.

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.

Monitoring Three-Decade Expansion of China’s Major Cities Based on Satellite Remote Sensing Images

As the largest developing country, China has experienced dramatic urban expansion since the “reform and opening-up” policy started at the end of the 1970s. In this paper, we monitor three decades of urban expansion in China’s 36 major cities, based on the spectral mixture analysis of remotely sensed satellite images. The results demonstrated that these major cities have expanded by 5.85 times from 1986 to 2015, with 15.51 km2 average expansion area per city per year. We found the urban expansion trajectories showed three different modes, i.e., exponential, linear and s-shaped, which were closely related to the city development level. In the old city zones, however, there was an interesting common tendency of the impervious surface area (ISA) first increasing and then decreasing, which could be largely attributed to the phenomenon of urban village reconstruction in China’s cities. Based on the Granger Causality Test (GCT), the interaction between urban ISA and Gross Domestic Product (GDP) per capita (GDPPC) suggested that the former was the driver of the latter. Meanwhile, taking the Yangtze River as the division between north and south China, there exists a north–south territorial differentiation for the interaction between ISA and total population at the year-end (TP).

Assessment of forest cover and carbon stock changes in sub-tropical pine forest of Azad Jammu & Kashmir (AJK), Pakistan using multi-temporal Landsat satellite data and field inventory.

This study aimed at estimating temporal (1989–2018) change in forest cover, carbon stock and trend in corresponding CO2 emissions/sequestration of a sub-tropical pine forest (STPF) in AJK, Pakistan. Our field inventory estimation shows an average above ground biomass (AAGB) accumulation of 0.145 Kt/ha with average carbon stock (ACS) value of 0.072 Kt/ha. Landsat TM, ETM+ and OLI images of 1989, 1993, 1999, 2005, 2010, 2015 and 2018 were used to extract vegetation fractions through Linear Spectral Mixture Analysis (LSMA) and forest area was calculated for respective years. Based on the forest area and estimated ACS value, the biomass carbon stock with corresponding CO2 emissions/sequestration was worked out for each time and change in forest carbon stock was determined for different time periods from 1989 to 2018. Our analysis shows net increase of 561 ha in forest cover and 40.39 Kt of ACS along with increase in corresponding CO2 sequestrations of 147.83 Kt over the study period. The results based on combination of remote sensing and field inventory provide valuable information and scientific basis to plan and ensure sustainable forest management (SFM) through reforestation, protection and conservation to enhance and maintain adequate forest cover and reduce CO2 emissions.

A rule-based spectral unmixing algorithm for extracting annual time series of sub-pixel impervious surface fraction

ABSTRACTThe spatial distribution and the temporal dynamics of impervious surface (IS) are important for understanding urbanization and its impact on surface energy balance, ecohydrological processes, and urban heat island. Despite the development of various methods, quantification of IS on multi-temporal scale is still challenging in complex urban areas due to the mixed pixel problem and the spectral ambiguity between classes. This study aimed at developing an algorithm, named Rule-Based Spectral Unmixing Algorithm (RBSUA), to derive sub-pixel IS fraction at annual scale using a time series of satellite images. A rule-based composite scheme is developed to integrate temporal contextual information into the popular Multiple Endmember Spectral Mixture Analysis (MESMA) to improve the classification of spectrally ambiguous classes. The developed algorithm also encompasses a temporal filtering to derive consistent IS fractions. Evaluation of the algorithm in a complex urban area – the National Capital Region (NCR), India – shows an improved performance in separating spectrally ambiguous classes, particularly IS and soil, and obtaining consistent series of IS fraction. The developed algorithm yielded the accuracy of annual IS fraction between 88% and 91% which is considerably higher than those from the original MESMA (48% to 83%). The application of the RBSUA in the NCR, India, shows that the IS has increased in the study area from the initial value of 377 ± 78 km2 in 1992 to 708 ± 64 km2 in 2017. The results suggested that the developed algorithm was effective in deriving IS at the sub-pixel level and detecting changes at the annual frequency.

Impact of water characteristics on the discrimination of benthic cover in and around coral reefs from imaging spectrometer data

Coral reefs are the foundation of productive ecosystems in the global, tropical oceans and are under threat from a variety of local to global scale stressors. Satellite imagery provides a tool to identify and understand the processes that control coral reef degradation, however due to the dynamic nature of seawater constituents, current spaceborne multispectral sensors cannot reliably discriminate between the many coral reef benthic classes necessary to detect change. Hyperspectral imagers may provide sufficient spectral resolution to estimate water column properties and differentiate benthic classes, however, the effects of depth, seawater constituents, and classification algorithm on the accuracy of benthic classifications have not been systematically assessed. Here, we simulate the ability of a spaceborne hyperspectral imager to accurately map fractional cover of coral reef benthic classes under a variety of conditions. Benthic reflectance is simulated by combining pure reflectance spectra of coral, algae, and sand and projecting these mixed spectra through a fully crossed set of water columns. We then use a semi-analytical optimization procedure to estimate the water column properties and multiple endmember spectral mixture analysis to estimate the fractional cover of the benthic classes using many independent endmember spectra. We compare our estimated benthic class fractions to the original, actual fractions used to produce the mixed coral reef spectra to quantify several measures of error. We found that multiple endmember spectral mixture analysis decreases fractional retrieval error, which is also reduced when the first derivative of the mixed and endmember spectra is used prior to unmixing. The estimation of fractional benthic class cover is most accurate for depths ≤3 m for most water conditions. Depths ≥5 m should be classified only if chlorophyll and sediment concentration are <0.1 mg m−3 and <0.1 g m−3, respectively. Our results indicate that the fractional cover of coral and algae should be at least 25% for accurate benthic class estimates (mean relative error < 50%), however there will be many ways to leverage the repeat measurements of a hyperspectral satellite sensor, such as a stable depth retrievals and benthic cover estimates, to produce more accurate and useful fractional cover data. We show how this simulation analysis can be used to generate maps of predicted benthic cover fractional retrieval uncertainty across a coral reef system using aerial hyperspectral imagery acquired over Hawaii, USA, although reef-specific, within pixel variations in depth and benthic class complexity should be considered.

Applying hyperspectral remote sensing methods to ship detection based on airborne and ground experiments

ABSTRACT Recent advances in hyperspectral remote sensing using hundreds of spectral channels make it possible to derive oceanic variables and to understand the spatial distribution of diverse oceanic phenomena. In this study, hyperspectral measurements were obtained using airborne and ground experiments and hyperspectral data processing techniques were applied for the detection of vessels. The observed hyperspectral data were analysed by using the spectral characteristics of endmembers and a representative spectral mixture analysis technique. The pixels in the image were classified into endmembers by using spectral matching algorithms and the marine library spectra from preliminary experiments. The estimated lengths and widths of the detected ships based on a series of hyperspectral data processing methods show root mean square error (RMSE) of 2.28% and 8.72% with respect to actual ship sizes, respectively. The investigation of the errors revealed that undulating surface waves, surface water penetration of the deck, and the submerged portion of the ship below the water surface potentially contributes to unclear ship boundaries. In this study, an optimal method for the objective determination of the threshold of abundance fractions based on pixels with multiple endmember contributions is proposed. The validation results based on the proposed hyperspectral method are in good agreement with the actual sizes of the ships.

Assessing environmental impacts of urban growth using remote sensing

ABSTRACTThis paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.

Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

Pressure on the biodiversity of ecosystems along many rivers is growing continuously due to the increasing number of hydropower facilities regulating downstream flow and sediment regimes. Despite a thorough understanding of the short-term processes and interactions at this hydro-biosphere interface, long-term analyses of the impacts on floodplain dynamics are lacking. We used interannual Landsat 4, 5, 7, and 8 time series to analyze the effects of hydrological events on floodplain vegetation in four mountainous floodplains in the Swiss Alps. Using a spectral mixture analysis approach, we demonstrate that the floodplain vegetation dynamics of mountainous rivers can be recovered at a spatial resolution of 30 m. Our results suggest that interactions between floods and floodplain vegetation are complex and not exclusively related to flood magnitude. Of the four reaches analyzed, only data gathered along the submountainous reach with a quasi-natural flow regime show a clear link between remotely sensed vegetation indices and floods. In addition, our 29-year time series shows a continuous upward trend in vegetation indices along the floodplains, strongest in the reaches affected by hydropower facilities. The approach presented in this study can be easily replicated in other mountain ranges by providing available flow data to verify the impact of hydropower on floodplain vegetation dynamics.