Linear Mixture Research Articles

Mapping Coastal Wetlands and Their Dynamics in the Yellow River Delta over Last Three Decades: Based on a Spectral Endmember Space

The accurate mapping and analysis of coastal wetlands and their dynamics are crucial for local coastal wetland protection, sustainable social development, and biodiversity preservation. However, detailed mapping and comprehensive analysis of coastal wetlands remain scarce. In this study, we utilized Landsat-TM/OLI remote sensing data and employed the linear spectral mixture analysis (LSMA) method to map changes in coastal wetlands and analyze their dynamics in the Yellow River Delta (YRD) from 1991 to 2020. Our mapping results demonstrate high accuracy and are consistent with previous studies, boasting an overall accuracy exceeding 96%. During the period of 1991–2020, the YRD estuary expanded by approximately 8744.58 ha towards the east and north. The vegetation of P. australis and S. salsa underwent transformation due to agricultural practices or degradation to bare flats. Moreover, these areas saw extensive colonization by the invasive species S. alterniflora. Over the three decades, S. alterniflora expanded approximately 5 km along the coast, significantly impacting the local coastal wetland biodiversity. Furthermore, a considerable number of natural wetlands transitioned into human-made wetlands from 1991 to 2014. In particular, bare flats underwent substantial changes, transforming into aquaculture sites and salt exploitation areas. These dynamics in coastal wetlands had significant repercussions on local ecosystems, including wetland fragmentation, biodiversity depletion, and water pollution. However, post-2014, numerous wetland protection strategies were implemented, resulting in the restoration of natural wetlands. Detailed wetland mapping and dynamic analysis furnish valuable insights for the management, protection, and sustainable utilization of diverse coastal wetlands.

Multivariate linear mixture models for the prediction of febrile seizure risk and recurrence: a prospective case–control study

Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case–control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71 females). Participants formed one case group (patients with FS) and two control groups (febrile patients without seizures and healthy controls). The impact of blood iron status, peak body temperature, and participants’ demographics on FS risk and recurrence was investigated with univariate and multivariate statistics. Serum iron concentration, iron saturation, and unsaturated iron-binding capacity differed between the three investigated groups (pFWE < 0.05). These serum analytes were key variables in the design of novel multivariate linear mixture models. The models classified FS risk with higher accuracy than univariate approaches. The designed bi-linear classifier achieved a sensitivity/specificity of 82%/89% and was closest to the gold-standard classifier. A multivariate model assessing FS recurrence provided a difference (pFWE < 0.05) with a separating sensitivity/specificity of 72%/69%. Iron deficiency, height percentile, and age were significant FS risk factors. In addition, height percentile and hemoglobin concentration were linked to FS recurrence. Novel multivariate models utilizing blood iron status and demographic variables predicted FS risk and recurrence among infants and young children with fever.

Investigation of an optimized hot press process design for manufacturing structural polyethersulfone foams

Semi-finished thermoplastic foam panels can be transformed into structural foams—a sandwich-like structure with high-density skins—through thermoforming. This transformation enables the production of mono material sandwich panels, which allow a significant increase in bending properties without increasing the part weight. This study describes the effect of process parameters on skin formation in thermoplastic structural foams manufactured in a two-step isochoric and isothermal hot press process. Process data recorded during foam manufacturing was analyzed and compared to foam behavior in quasi-static isothermal compression tests performed at different levels of temperature and crosshead speeds. The effect of skin-core structure on bending properties is compared to theoretical models from the established literature. It was found that a rule of linear mixture that considers the relative skin thickness allows adequate prediction of both bending modulus and strength. Preferable process design—especially combinations of tool temperature and maximum compaction speed—is described based on the comparison of measured and predicted performance.

Mapping Grassland Based on Bio-Climate Probability and Intra-Annual Time-Series Abundance Data of Vegetation Habitats

Accurate inventories of grasslands are important for studies of greenhouse gas (GHG) dynamics, as grasslands store about one-third of the global terrestrial carbon stocks. This paper develops a framework for large-area grassland mapping based on the probability of grassland occurrence and the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this study, grassland occurrence probability maps were produced based on data on bio-climate factors obtained from MODIS/Terra Land Surface Temperature (MOD11A2), MODIS/Terra Vegetation Indices (MOD13A3), and Tropical Rainfall Measuring Mission (TRMM 3B43) using the random forests (RF) method. Time series of 8-day fractional vegetation-related endmembers (green vegetation, non-photosynthetic vegetation, sand land, saline land, and dark surfaces) were generated using linear spectral mixture analysis (LSMA) based on MODIS/Terra Surface Reflectance data (MOD09A1). Time-series endmember fraction maps and grassland occurrence probabilities were employed to map grassland distribution using an RF model. This approach improved the accuracy by 5% compared to using endmember fractions alone. Additionally, based on the grassland occurrence probability maps, we identified extensive ecologically sensitive regions, encompassing 1.54 (104 km2) of desert-to-steppe (D-S) and 2.34 (104 km2) of steppe-to-meadow (S-M) transition regions. Among these, the D-S area is located near the threshold of 310 mm/yr in precipitation, an annual temperature of 10.16 °C, and a surface comprehensive drought index (TVPDI) of 0.59. The S-M area is situated close to the line of 437 mm/yr in precipitation, an annual temperature of 5.49 °C, and a TVPDI of 0.83.

Estimating Agricultural Cropping Intensity Using a New Temporal Mixture Analysis Method from Time Series MODIS

Agricultural cropping intensity plays an important role in evaluating the food security and the sustainable development of agriculture. The existing indicators measuring cropping intensity include cropping frequency and multiple cropping index. As a nominal measurement, cropping frequency classifies crop patterns into single-cropping and/or double-cropping and leads to information loss. Multiple cropping index is calculated on the basis of statistical data, ignoring the spatial heterogeneity within the administrative region. Neither of these indicators can meet the requirements of precision agriculture, and new methods for fine cropping intensity mapping are still lacking. Time series remote sensing data provide vegetation phenology information and reveal temporal development of vegetation, which can be used to facilitate the fine cropping intensity mapping. In this study, a new temporal mixture analysis method is introduced to estimate the abundance level cropping intensity from time series remote sensing data. By analyzing phenological characteristics of major land-cover types in time series vegetatiosacan indices, a novel feature space was constructed by using the selected PCA components, and three unique endmembers (double-cropping, natural vegetations and water bodies) were found. Then, a linear spectral mixture analysis model was applied to decompose mixed pixels by replacing spectral data with multi-temporal data. The spatio-temporal continuous, fine resolution, abundance level cropping intensity maps were produced for the North China Plain and the middle and lower reaches of the Yangtze River Valley. The experiments indicate a good result at both county and pixel level validation. The method of manually delineating endmembers can well balance the accuracy and efficiency. We also found the size of the study area has little effect on the unmixing accuracy. The results demonstrated that the proposed method can model cropping intensity finely at large scale and long temporal span, at the same time with high efficiency and ease of implementation.

Odd log-logistic generalised Lindley distribution with properties and applications

In this paper, a new three-parameter lifetime model, called the odd log-logistic generalised Lindley distribution, is introduced. Some structural properties of the new distribution including ordinary and incomplete moments, quantile and generating functions and order statistics are obtained. The new density function can be expressed as a linear mixture of exponetiated Lindley densities. Different methods are discussed to estimate the model parameters and a simulation study is carried out to show the performance of the new distribution. The importance and flexibility of the new model are also illustrated empirically by means of two real data sets. Finally, Bayesian analysis and Gibbs sampling are performed based on the two real data sets.

Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval

AbstractThis study is part of the requirements consolidation study for the European Copernicus Hyperspectral Imaging Mission for Environment (CHIME). It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS‐NG and PRecursore IperSpettrale della Missione Applicativa (PRISMA), for detecting topsoil texture properties. The spatial variability is retrieved using the linear spectral mixture analysis, an image‐based algorithm that breaks down the hyperspectral data set into fractional abundance of spectral classes within each pixel. The fractional abundance of image‐based endmembers is broken into categories to find intervals having a spatial relation with texture components in terms of fine (clay and silt) or coarse (sand) abundance. The fraction maps obtained show similar spatial patterns to the USDA soil texture classification, obtained with a geostatistical approach. Specifically, AVIRIS CHIME‐like FAM1 &gt; 0.45 presented an agreement of 86% with clay and/or silt higher than 45% which, according to the United Stated Agriculture Department (USDA) intervals, correspond to loam‐clay loam classes. Similar results are obtained with PRISMA with FAM2 0.20–0.35, overlapping 63% of the kriging‐based USDA clay‐loam class. The fractional abundance categories showing the highest overlap percentages are correlated with the short‐wave infrared spectral range, showing average coefficients of 0.7 where wavelengths are over 1,500 nm. From 1700 nm, CHIME‐like shows values of 0.8. In conclusion, this exploratory research and results leverage the opportunity of extending the processing chain to a larger number of case studies to better understand the physical relation between the spectral reflectance captured by new spaceborne hyperspectral sensors and the spatial patterns of soil texture classes.

Efficiency and Safety of Temperatures Management in Aortic Arch Surgery: A System Review and Meta-Analysis

Objective. The study evaluates the safety and efficacy of hypothermic cardiac arrest (HCA) at various temperatures in aortic arch surgeries. Methods. We conducted a literature search in PubMed, Google Scholar, and Embase databases. For single proportion assessments, we employed fixed-effect and random-effect models in the general linear mixture model and the inverse variance model for other computations. We analyzed factors such as age, sex, operation time, and postoperative complications, with subgroup and metaregression analyses. We used funnel plots to depict potential publication bias. Results. Our research incorporated 43 papers with 34,797 cases. HCA temperatures were divided into five groups (A: 30–32°C, B: 28–30°C, C: 26–28°C, D: 24–26°C, and E: &lt;24°C). There is no statistically significant difference in myocardial ischemia time ( P = 0.90) and isolated cerebral perfusion (ICP) time ( P = 0.95). Groups A and C have the best performance in avoiding postoperative complications including transient nerve injury (TNI), permanent nerve injury (PNI), renal failure (RF), and mortality occurrence rate. Group A has the lowest occurrence rate in PNI (3%) and mortality (3%). Group C has the lowest RF incidence (5%). Conclusion. Maintaining temperatures of 30–32°C in en bloc anastomosis or 26–28°C during arch replacement with separate grafts can significantly reduce complications including PNI, RF, and in-hospital mortality.

Long‐term desertification process monitoring and driving factors analysis in rare earth mining area

With the exploitation of rare earth elements (REE) in southern China, desertification has become a serious ecological problem in this region. To accurately understand the desertification process in mining areas and formulate targeted ecological restoration programs, taking the Lingbei mining area in Ganzhou City, Jiangxi Province as the study area, we explored desertification information extraction methods, desertification dynamics monitoring, and desertification drivers in REE mining areas using multisource data from 1986 to 2021. The results show that from the method of extracting desertification information, Random Forest is the prominent (83.33%), followed by Albedo–NDVI (Normalized Difference Vegetation Index) space (74.44%), and thirdly Linear Spectral Mixture Model (65.56%). From 1986 to 2021, the desertification land area in Lingbei mining areas first increased and then decreased, showing a reverse trend, but it has not recovered to the pre‐mining level. The government's ecological reclamation measures are the key factors for the restoration of desertification land. There are still 17.81 km2 areas that have not been restored, of which 2.16 km2 are at the level of severe desertification. Moderate desertification and light desertification have not been completely curbed, which needs continuous attention. Vegetation coverage has the highest explanatory power to the distribution of land desertification in mining areas, followed by land use classification, indicating that human activities have a great influence on the spatial differentiation of land desertification in mining areas, and it is necessary to pay attention to regional balanced and sustainable development in the future.

Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

ABSTRACT Crown fire damage is a mixture of three principal fire-related components: charred material, scorched foliage, and unaltered green canopy. This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts (Portugal and Italy) by applying linear spectral mixture analysis (LSMA) on Sentinel-2 imagery. The tree crowns fire damage was subsequently mapped, integrating fractional abundance information in a random forest (RF) algorithm, comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal. Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification (LMSA-derived RMSE < 0.1), the F-scores always were ≥ 90% whether generic endmembers or image endmembers derived information was employed. The environmental heterogeneity of the two study areas affected the fire severity gradients, with a prevalence of the charred (PT) (45–46%) and green class (IT) (44–53%). Post-fire temporal monitoring was initialized by applying the proposed strategies, and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event, with a reduced charcoal predominance and an increasing proportion of green components.

Effect of Spatial and Spectral Scaling on Joint Characterization of the Spectral Mixture Residual: Comparative Analysis of AVIRIS and WorldView-3 SWIR for Geologic Mapping in Anza-Borrego Desert State Park.

A geologic map is both a visual depiction of the lithologies and structures occurring at the Earth's surface and a representation of a conceptual model for the geologic history in a region. The work needed to capture such multifaced information in an accurate geologic map is time consuming. Remote sensing can complement traditional primary field observations, geochemistry, chronometry, and subsurface geophysical data in providing useful information to assist with the geologic mapping process. Two novel sources of remote sensing data are particularly relevant for geologic mapping applications: decameter-resolution imaging spectroscopy (spectroscopic imaging) and meter-resolution multispectral shortwave infrared (SWIR) imaging. Decameter spectroscopic imagery can capture important mineral absorptions but is frequently unable to spatially resolve important geologic features. Meter-resolution multispectral SWIR images are better able to resolve fine spatial features but offer reduced spectral information. Such disparate but complementary datasets can be challenging to integrate into the geologic mapping process. Here, we conduct a comparative analysis of spatial and spectral scaling for two such datasets: one Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS-classic) flightline, and one WorldView-3 (WV3) scene, for a geologically complex landscape in Anza-Borrego Desert State Park, California. To do so, we use a two-stage framework that synthesizes recent advances in the spectral mixture residual and joint characterization. The mixture residual uses the wavelength-explicit misfit of a linear spectral mixture model to capture low variance spectral signals. Joint characterization utilizes nonlinear dimensionality reduction (manifold learning) to visualize spectral feature space topology and identify clusters of statistically similar spectra. For this study area, the spectral mixture residual clearly reveals greater spectral dimensionality in AVIRIS than WorldView (99% of variance in 39 versus 5 residual dimensions). Additionally, joint characterization shows more complex spectral feature space topology for AVIRIS than WorldView, revealing information useful to the geologic mapping process in the form of mineralogical variability both within and among mapped geologic units. These results illustrate the potential of recent and planned imaging spectroscopy missions to complement high-resolution multispectral imagery-along with field and lab observations-in planning, collecting, and interpreting the results from geologic field work.

Quantitative Simulations of Siloxane Adsorption in Metal-Organic Frameworks.

We present a transferable force field (FF) for simulating the bulk properties of linear and cyclic siloxanes and the adsorption of these species in metal-organic frameworks (MOFs). Unlike previous FFs for siloxanes, our FF accurately reproduces the vapor-liquid equilibria of each species in the bulk phase. The quality of our FF combined with the Universal Force Field using standard Lorentz-Berthelot combining rules for MOF atoms was assessed in a wide range of MOFs without open metal sites, showing good agreement with dispersion-corrected density functional theory calculations. Predictions with this FF show good agreement with the limited experimental data for siloxane adsorption in MOFs that is available. As an example of using the FF to predict adsorption properties in MOFs, we present simulations examining entropy effects in binary linear and cyclic siloxane mixture coadsorption in the large-pore MOF with structure code FOTNIN.

Extraction and Spatiotemporal Evolution Analysis of Impervious Surface and Surface Runoff in Main Urban Region of Hefei City, China

The biophysical composition index (BCI)-based linear spectral mixture model (LSMM) is used in this study to extract the impervious surface (IS), vegetation, and soil coverage of the main urban region (MUR) of Hefei City over the 2001–2021 period. In addition, the Soil Conservation Service-Curve Number (SCS-CN) model is first applied to simulate the surface runoff (SR) in the MUR of Hefei City over the past 21 years, then assessed for simulation accuracy using typical waterlogging points in the study area. On this basis, the spatiotemporal evolution of IS and SR and their relationships in the MUR of Hefei City are investigated and discussed in this study. The obtained results showed that (1) the root-mean-square error (RMSE), mean absolute error (MAE), and systematic error (SE) values of the BCI index-based LSMM are smaller than those of the LSMM, demonstrating a higher extraction accuracy of urban IS extraction of the BCI index-based LSMM. (2) The IS area of the MUR of Hefei City exhibits an increasing trend from 107.555 km2 in 2001 to 387.660 km2 in 2021. In addition, the change rate and change intensity values indicate an increasing–decreasing–increasing trend. The highest change rate and change intensity values are 24.839 km2/year and 23.094%, respectively, and were observed in the 2001–2005 period. (3) The simulated SR (165–195 mm) in the MUR of Hefei City demonstrates an increasing trend in the 2001–2021 period at a rainfall intensity value of 200 mm/d. In addition, the simulated SR amount in the central area exhibits slight changes, while that in the surrounding areas shows substantial variations. (4) The distribution of IS and SR in the MUR of Hefei City reveals strong directional variations, which are all affected by geographical conditions. The IS coverage and SR show high positive correlation coefficients in different years. (5) The present study provides primary data for effective urban planning, water resources management and regulation, and disaster prevention and mitigation in Hefei City, as well as a scientific reference for future studies on urban IS, SR, and their quantitative relationships in other regions.

Polar Low Motion and Track Characteristics over the North Atlantic

Abstract Polar lows (PLs) are intense mesoscale cyclones over high-latitude oceans. The PL motion and track characteristics over the North Atlantic in the cold season are examined based on ∼1700 PLs during 1979–2016. Our analysis shows that PL motion is mainly controlled by the environmental flow in the lower troposphere. In particular, the steering flow defined over 950–550 hPa within a 450-km radius best matches the PL motion. Meanwhile, 700 hPa is an optimal single level to assess the steering flow. Cluster analysis based on a linear regression mixture model is utilized to describe the PL tracks. Four distinct clusters are identified, and they are characterized by northeastward motion (NE), eastward motion (E), southward motion (S), and slow motion without a dominating direction (SM), respectively. Although PLs in the four clusters have similar lifespans, SM-type PLs have much shorter tracks than the other clusters, due to their slow translation speeds. Further analysis shows that there are no distinctive geographic differences in genesis locations for the four clusters. The track differences can be largely explained by the associated synoptic-scale environmental circulations. Additionally, the S-type PLs tend to develop in a reverse shear environment, the NE-type and E-type PLs are associated with forward shear and left shear environments, respectively, while the SM-type PLs do not show a preference in the environmental shear. The link between the PL tracks and the North Atlantic weather regimes is also investigated. The NAO+ regime is associated with the most frequent PL occurrence over the North Atlantic, while the Scandinavian blocking regime is associated with lowest PL frequency. Significance Statement Polar lows (PLs) are intense mesoscale cyclones and pose hazards to high-latitude coastal areas. A better understanding of polar low motion can help improve polar low forecasts and reduce the hazardous impacts of the storms. Based on a PL track dataset over the North Atlantic basin during 1979–2016, it is found that PL motion is mainly controlled by the environmental flow in the lower troposphere and that the steering flow defined over 950–550 hPa within a 450-km radius best matches the PL motion. In addition, four distinct types of tracks are identified using cluster analysis, and they are characterized by northeastward motion, eastward motion, southward motion, and slow motion without a dominating direction, respectively. The different background flows can largely explain the propagation directions of different track types. Furthermore, the links between the PL motion and the North Atlantic weather regimes are also investigated.

Spatial characteristics of urban evapotranspiration effects on the thermal environment

Abstract Evapotranspiration (ET) is an important process of the regional hydrothermal cycle. However, it is unclear how the mitigation of urban ET in urban thermal environments occurs in a spatial context. Landsat 8 satellite images from 2014 to 2018 of Xuzhou and corresponding meteorological observations were selected, and the improved mono-window algorithm (IMW) and urban RS-PM model were applied to invert land surface temperature (LST) and ET, respectively. In addition, spatial analysis methods (a profile analysis, standard deviation ellipse (SDE) analysis, and bivariate Moran's I) were employed to quantify and simulate the spatial characteristics of the ET effect on LST. The results indicated the following: (1) There was a significant linear negative correlation between ET and LST, which confirms that ET has a negative effect on LST; (2) the SDE overlap ratios between patches with higher ET and lower LST imply that higher ET patches have a significant impact on the spatial distribution of LST; and (3) bivariate Moran's I between ET and LST and their linear mixture spectral analysis (LISA) maps reveal a significant negative spatial correlation between ET and LST. In addition, the landscape pattern of higher ET parches is also an important factor affecting the environmental temperature.

Heterogeneity in resilience patterns and its prediction of 1-year quality of life outcomes among patients with newly diagnosed cancer: An exploratory piecewise growth mixture model analysis

PurposeThis study was designed to explore the impact of a new cancer diagnosis on resilience of patients and whether the resilience patterns could predict Quality of Life (QoL) in the first year. MethodsAn exploratory linear piecewise growth mixture modeling (PGMM) with one hypothetical dot (3 months since diagnosis, T1) was employed to identify different resilience patterns and growth in 289 patients with different cancer diagnoses at five assessment occasions (T0-T4). Logistic regression analysis was performed to select potential predictors and receiver operating characteristic (ROC) curve analysis was utilized to test PGMM’s discriminative ability against 1-year QoL. ResultsFive discrete resilience trajectories with two growing trends were identified, including “Transcendence” (7.3%), “Resilient” (47.4%), “Recovery” (18.7%), “Damaged” (14.9%) and “Maladaption” (11.8%). Advanced stage, colorectal cancer, and receiving surgery therapy were significant predictors of negative resilience trajectories (“Damaged” or “Maladaption”). Discriminative ability was good for PGMM (AUC = 0.81, 95%CI, 0.76–0.85, P < 0.0001). ConclusionHeterogeneity is identified in resilience growth before and after 3 months since diagnosis. 26.7% newly diagnosed patients need additional attention especially for those with advanced colorectal cancer and receiving surgery therapy.

Thermodynamics of 2-alkanol + polar organic solvent mixtures. I. Systems with ketones, ethers or organic carbonates

The mixtures 2-propanol or 2-butanol + n-alkanone, or + acetophenone, or + linear monoether, or + cyclic ether, or + linear organic carbonate, or + propylene carbonate have been investigated using thermodynamic data, and in terms of the Flory theory, and the Kirkwood-Buff integrals. The data considered are: excess molar enthalpies (HmE), volumes, entropies, and the temperature dependence ofHmE. The enthalpy of the 2-alkanol-solvent interactions have been determined, and the different contributions to HmE discussed. It is shown that HmE values of the 2-alkanol (fixed) + n-alkanone, or + linear carbonate mixtures change in the same manner that for n-alkanone, or linear carbonate + n-alkane (fixed) systems. In contrast, HmE values of 2-alkanol (fixed) + linear monoether or + n-alkane mixtures change similarly. This set of results suggests that solvent–solvent interactions are determinant in systems with n-alkanone or linear carbonate, while interactions between alcohol molecules are determinant in mixtures with linear monoethers. According to the Flory model, orientational effects in systems with a given 2-alkanol become weaker in the sequence: linear monoether > linear organic carbonate > n-alkanone, and are stronger in solutions with a cyclic monoether than in those with cyclic diethers, and in systems with acetophenone or propylene carbonate than in the mixtures with the corresponding linear solvents. Results obtained from the Kirkwood-Buff integrals are consistent with these findings. The application of Flory model reveals that orientational effects are similar in systems with 1- or 2-alkanols, with the exception of solutions with linear monoethers, where such effects are stronger in mixtures containing 1-alkanols. For the examined systems, the formalism of the Kirkwood-Buff integrals makes no meaningful distinction between solutions with 1-alkanols or 2-alkanols.

Stationary subspace analysis based on second-order statistics

In stationary subspace analysis (SSA) one assumes that the observable p-variate time series is a linear mixture of a k-variate nonstationary time series and a (p−k)-variate stationary time series. The aim is then to estimate the unmixing matrix which transforms the observed multivariate time series onto stationary and nonstationary components. In the classical approach multivariate data are projected onto stationary and nonstationary subspaces by minimizing a Kullback–Leibler divergence between Gaussian distributions, and the method only detects nonstationarities in the first two moments. In this paper we consider SSA in a more general multivariate time series setting and propose SSA methods which are able to detect nonstationarities in mean, variance and autocorrelation, or in all of them. Simulation studies illustrate the performances of proposed methods, and it is shown that especially the method that detects all three types of nonstationarities performs well in various time series settings. The paper is concluded with an illustrative example.

Conserving urban tropical biodiversity by connecting networks of green patches

AbstractTropical Southeast Asia is a hotspot for global biodiversity, and also a hotspot for rapidly expanding urbanisation. There is a need to identify, protect, restore and connect remaining green spaces in the urban matrix before this opportunity is lost to urban development. The objective of this study is to characterise ecological connectivity for mammals and identify important patches and linkages for connecting urban green spaces for Greater Kuala Lumpur (KL), Malaysia. We first map land cover across the region using a linear mixture model with preprocessed multidate cloud‐free mosaics derived from Sentinel 2 remote sensing data in Google Earth Engine. We then model connectivity using the land cover maps, expert‐based parameterisation of Euclidian distance and graph‐based connectivity models for a range of dispersal guilds representing small and medium terrestrial and arboreal mammals. Our analysis showed large differences in the effects of fragmentation within Greater KL on the different dispersal groups, with some groups perceiving the landscape as disconnected. However, our analysis identified a network of green patches and pathways which potentially could support connectivity in the urban landscape. Our results demonstrate the potential for Southeast Asian megacities to support biodiversity in the urban context, and the potential for a different kind of urban development, which supports biodiversity in its urban fabric.

Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies

ABSTRACT Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-resolution satellite images, but the monitoring accuracy is considerably affected by the mixed-pixel problem. Although various spectral unmixing methods have been applied to map sub-pixel surface water fractions for large water bodies, such as lakes and reservoirs, it is challenging to map SWBs that are small in size relative to the image pixel and have dissimilar spectral properties. In this study, a novel regression-based surface water fraction mapping method (RSWFM) using a random forest and a synthetic spectral library is proposed for mapping 10 m spatial resolution surface water fractions from Sentinel-2 imagery. The RSWFM inputs a few endmembers of water, vegetation, impervious surfaces, and soil to simulate a spectral library, and considers spectral variations in endmembers for different SWBs. Additionally, RSWFM applies noise-based data augmentation on pure endmembers to overcome the limitation often arising from the use of a small set of pure spectra in training the regression model. RSWFM was assessed in ten study sites and compared with the fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and the nonlinear random forest (RF) regression without data-augmentation. The results showed that RSWFM decreases the water fraction mapping errors by ~ 30%, ~15%, and ~ 11% in root mean square error compared with the linear FCLS, MESMA unmixings, and the nonlinear RF regression without data-augmentation respectively. RSWFM has an accuracy of approximately 0.85 in R2 in estimating the area of SWBs smaller than 1 ha.