Detection Of Long-term Changes Research Articles

Effects of beach width variability on recreational function: A case study of NE Brazil

Beaches have historically been recognized as one of the main spaces for leisure and recreation for residents and tourists worldwide. They are maintained by the combined action of waves, tides, currents, and winds that directly affect their composition, shape, and size. Subject to morphological variations on a short-term scale (hours and days) as a result of the action of storm waves or extraordinary tidal events, the geomorphic and sedimentary characteristics of the beach are important for determining recreational function. Considering that the beach profile changes seasonally owing to the conditions of sea agitation and sand supply, some uses may not adapt to these variations. Thus, there must be a minimum beach width for recreational functions to be developed. The method used to measure beach width is an important parameter to study erosion and accretion on beaches around the world. However, the detection of long-term changes in beach width is challenging because it is necessary to have observational data on the beach morphological change. In this study, we evaluated the relationship between the variability of the width of the emerged beach and its effects on recreational function in a stretch of Tabuba beach, Caucaia, Ceará.

Multi-sensor change detection for within-year capture and labelling of forest disturbance

Knowledge of forest change type and timing is required for forest management, reporting, and science. Time series of historic satellite data (e.g. Landsat) have resulted in an invaluable record of changes in forest conditions. Natural resource management and reporting typically operate at an annual time step, yet the recent addition of data streams from compatible satellites (e.g., Sentinel-2) offer the possibility of generating frequent, management-relevant forest status assessments and maps of change. Analytical approaches that rely on a time series of observations to identify change often struggle to provide reliable estimates of change events in terminal years of the time series until subsequent, additional observations are available. Methods to meaningfully integrate observations from compatible satellite platforms can provide short-term information to augment and refine estimates of change area and type in those terminal years of the time series. In this research we fuse Landsat-8 and Sentinel-2A and -2B data streams to capture, with reduced latency, stand replacing forest change (harvest and wildfire), tagged to a temporal window of occurrence over an ~10,000 km2 area of central British Columbia, Canada. We introduce a new algorithm, SLIMS (Shrinking Latency in Multiple Streams), to rapidly and reliably detect change, and then use an established Bayesian approach to meaningfully combine changes detected in the Landsat and Sentinel data streams. Our results indicate that the type and timing of stand-replacing disturbances can be identified in these forests with high accuracy. Overall, 13.9% of the study area was disturbed between the end of 2016 and the end of 2017, with the majority of disturbed area attributable to wildfire and a smaller amount attributed to forest harvesting, mostly in the winter 2016–2017 with some limited summer harvest also occurring. Overall accuracy of the change, assessed using independent validation data, was 95% ± 2.3%. The capacity of these change results to augment a trend-based assessment of change for 2017 was also demonstrated and provides a framework for how short- and long-term change detection approaches provide complementary information that can increase the timeliness and accuracy of change area estimates in the terminal years of a time series. These findings also demonstrate the capacity to regard Landsat and Sentinel-2 sensors as elements of a virtual constellation to obtain forest change information in a timely (i.e., end of growing season) and reliable fashion over large areas.

NestNet: a multiscale convolutional neural network for remote sensing image change detection

ABSTRACT With the rapid development of remote sensing technologies, the frequency of observations of the same location is increasing, and many satellites and sensors produced a large amount of time series images. These images make long-term change detection and dynamic characteristic estimation of ground features possible. However, conventional remote sensing image change detection methods mostly rely on manual visual interpretation and supervised or unsupervised computer-aided classification. Traditional methods always face many bottlenecks when processing big and fast-growing datasets, such as low computational efficiency, low level of automation, and different identification standards and accuracies caused by different operators. With the rapid accumulation of remote sensing data, it has become an important but more challenging task to conduct change detection in a more precise, automated and standardized way. The development of geointelligent computing technologies provides a means of solving these problems and improve the accuracy and efficiency of remote sensing image change detection. In this paper, we presented a novel deep learning model called nest network(NestNet) based on a convolutional neural network to improve the accuracy of the automatic change detection task by using remotely sensed time series images. NestNet extracts the respective features of bi-temporal images using an encoding parallel module and subsequently employs absolute different operations to process the features of two images. Compared with change detection method based on U-Shaped network plus plus (UNet++), the parallel module improves the efficiency of NestNet. Finally, a decoding module is used to generate a predicted change image. This paper compares NestNet to traditional methods and state-of-the-art deep learning models on two datasets. The experimental results demonstrate that the accuracy of NestNet is better than that of state-of-the-art methods. It can be concluded that the NestNet model is a potential approach for change detection using high resolution remote sensing images.

Long-Term Changes in the Water Mass Properties in the Balearic Channels Over the Period 1996–2019

The analysis of a 24-year time series of Conductivity-Temperature-Depth (CTD) casts collected in the Balearic Channels (1996–2019) has allowed detecting and quantifying long-term changes in water mass properties in the Western Mediterranean. For the complete period, the intermediate waters have experienced warming and salting at rates of 1.4°C/100yr and 0.3–0.6/100yr for the Western Intermediate Water, and 1°C/100yr and 0.3–0.4/100yr for the Levantine Intermediate Water. The density of these two water masses has not changed. The deep waters, defined as those denser than 29.1 kg/m3, showed positive trends in temperature, salinity, and density (0.8°C/100yr, 0.2/100yr, and 0.02 kg.m–3/100yr, respectively). The high temporal variability of the upper layer makes the detection of long-term changes more difficult. Nevertheless, combining CTD data with temperature data from the oceanographic station at L’Estartit and simulated data from the NCEP/NCAR reanalysis, it can be established that the Atlantic Water increased its temperature at a rate of 2.1–2.8°C/100yr and likely its salinity at a rate of 0.6/100yr. The water column absorbed heat at a rate equivalent to 1–1.2 W/m2. All these trends are much higher than those reported in previous works (more than double in some cases). The warming of the water column produced an increase in the thermosteric component of sea level. However, this increase was compensated by the decrease in the halosteric component. Besides these changes, other alterations related to the Western Mediterranean Transition have been observed over shorter periods. The temperature and salinity of the intermediate waters increased before the winter of 2004/2005 and then the temperature and salinity of the deep waters increased dramatically in 2005. The density of the deep water reached values unprecedented before 2005. Deep and intermediate waters were uplifted by the presence of such dense deep waters. The arrival of warmer and saltier intermediate waters from the Eastern Mediterranean is also observed, mainly after 2010.

Calanchi badlands reconstructions and long-term change detection analysis from historical aerial and UAS image processing

In Italy, highly dissected badlands may be found widespread (over the entire Apennine region) and are locally described as “calanchi” (singular: “calanco”). Accelerated geomorphic processes led to the rapid evolution of these erosional landunits with intense drawbacks on agriculture, infrastructure and society. Historical aerial photographs (HAP) of such badland areas exist worldwide. To reconstruct the past surface and related topographic changes offers high potential for the assessment of long-term topographic changes of many interesting landscapes. Due to technical and conceptual demands of traditional photogrammetry those photographs have been used comparable little in long-term geomorphology. Processing them with user-friendly SfM-MVS photogrammetry would provide a new perspective on the assessment of long-term erosion rates on the catchment and slope scale and the related badland evolution. Therefore, this multi-temporal approach focusses on the appliance of SfM-MVS photogrammetry in order to reconstruct the past landscape and to calculate the related long-term surface changes of a badland located in the upper Val d’Orcia (Tuscany, Italy). The analysis is based on a set of high-resolution UAV images (2016) and two sets of historical aerial images of varying quality and scale (1976 and 1994). Digital elevation models (DEMs), shaded relief raster, several morphometric parameters and DEMs of difference (DoD) (resolution: 1 m) were used to obtain overall terrain changes and to calculate sediment balances and annual surface change rates on catchment and slope scale. Change detection analysis revealed a mean surface lowering of 0.01 m*a–1 (1976–2016) on the catchment scale and 0.03 m*a–1 (1976–1994) to 0.06 m*a–1 (1994–2016) on the slope scale. Our results further show a reduced activity of fluvial erosion processes from the mid 90s’ on while mass movements dominating the topographic changes in the second half of the 40 years observation period. Erosion processes are enhanced near the tear-off edges of the lateral valleys. Deposition mainly occurs in the main valley floor which can be classified as a temporary sediment sink/store. The geomorphic development of the catchment indicates a self-stabilizing behaviour towards an equilibrium state. A reduction of the mean slope gradient in the main valley from 25° in 1976 to 14° in 2016 confirms the self-stabilizing theory. The study also revealed the potential and limitations of historical aerial images for long-term geomorphic change detection and showed that SfM-MVS algorithms operate less reliable when applied to only a few images (n=3) of a singular flightline. Nevertheless, the good agreement of our results with the long term rates obtained by other authors with different methods/techniques and different time scales proof the great potential of historical aerial photographs for 3D surface reconstructions (SfM-MVS) of semi-arid badlands when ceratin quality parameters, mainly high image overlaps and entropy, are fulfilled.

Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin, Ethiopia

Integration of remote sensing data sets from multiple satellites is tested to simulate water storage variation of Lake Ziway, Ethiopia for the period 2009-2018. Sixty Landsat ETM+/OLI images served to trace temporal variation of lake surface area using a water extraction index. Time series of lake levels were acquired from two altimetry databases that were validated by in-situ lake level measurements. Coinciding pairs of optical satellite based lake surface area and radar altimetry based lake levels were related through regression and served for simulating lake storage variation. Indices for extracting lake surface area from images showed 91–99 % overall accuracy. Lake water levels from the altimetry products well agreed to in-situ lake level measurements with R2 = 0.92 and root mean square error of 11.9 cm. Based on this study we conclude that integrating satellite imagery and radar altimetry is a viable approach for frequent and accurate monitoring of lake water volume variation and for long-term change detection. Findings indicate water level reduction (4 cm/annum), surface area shrinkage (0.08km2/annum) and water storage loss (20.4Mm3/annum) of Lake Ziway (2009–2018).

Benthic studies in LTER sites: the use of taxonomy surrogates in the detection of long-term changes in lagoonal benthic assemblages

In benthic studies, the identification of organisms at the species level is known to be the best source for ecological and biological information even if time-consuming and expensive. However, taxonomic sufficiency (TS) has been proposed as a short-cut method for quantifying changes in biological assemblages in environmental monitoring. In this paper, we set out to determine whether and how the taxonomic complexity of a benthic assemblage influences the results of TS at two different long-term ecological research (LTER) sites in the Po delta region (north-eastern Italy). Specifically, we investigated whether TS can be used to detect natural and human-driven patterns of variation in benthic assemblages from lagoonal soft bottoms. The first benthic dataset was collected from 1996 to 2015 in a “choked” lagoon, the Valli di Comacchio, a lagoon characterised by long water residence times and heavy eutrophication, while the second was collected from 2004 to 2010 in a “leaky” lagoon, the Sacca di Goro, a coastal area with human pressure limited to aquaculture. Univariate and multivariate statistical analyses were used to assess differences in the taxonomic structure of benthic assemblages and to test TS on the two different datasets. TS seemed to work from species to family level at both sites, despite a higher natural variability of environmental conditions combined with multiple anthropogenic stressors. Therefore, TS at the family level may represent effective taxonomic surrogates across a range of environmental contexts in lagoon environments. Since the structure of the community and the magnitude of changes could influence the efficiency of taxonomic surrogates and data transformations in long-term monitoring, we also suggest periodic analyses at finer taxonomic levels in order to check the efficiency of the application of taxonomic substitutes in routine monitoring programmes in lagoon systems.

Monitoring LULC changes and its impact on the LST and NDVI in District 1 of Shiraz City

Today, immediate and long-term change detection and monitoring using remote sensing (RS) data and geographical information system (GIS) is of paramount importance in generating information about the latest land use/land cover (LULC), land surface temperature (LST), and normalized difference vegetation index (NDVI) in accordance with spatial and temporal changes. Therefore, to obtain these components, a multi-temporal dataset was used consisting of two sets of Landsat Thematic Mapper (TM) images from 1986 to 2011 period across District 1 of Shiraz. Additionally, to investigate the relationship between LST and NDVI over seasons, four Landsat images were used. LULC, LST, and NDVI components were retrieved using Landsat image in ERDAS IMAGINE 9.2 image processing software. Results showed that during the study period, the city had experienced a massive urban (residential) growth. Moreover, change detection suggested that residential areas had increased by 13.17 km2 and vegetation zones (garden) and barren lands had decreased by 4.6 and 8.63 km2, respectively, during 1985–2011 period. The study of the relationship between vegetation index (land cover) and vegetation (land use) in District 1 showed that with reduced vegetation zone (land use), the quality of vegetation (land cover) had deteriorated. These findings indicate that reduced quality of vegetation cover and consequently its Reduction can have a positive effect on the temperature patterns. In general, the negative correlation between vegetation and LST caused by lower vegetation quality was less significant in 2011 compared to 1986, while there the correlation between vegetation and LST in summer was higher than other seasons.

Improving accuracy of long-term land-use change in coal mining areas using wavelets and Support Vector Machines

ABSTRACTOld satellite sensors lack several quality features such as high spatial and spectral resolution. For accurate long-term change detection, improvement of the quality of old satellite images is required. In the present study, we used two wavelet-based image enhancement techniques [(discrete wavelet transform (DWT) and dual tree-complex wavelet transform (DT-CWT)] for improving the quality of Landsat 2 data of 1975 and Landsat 8 data of 2015 to study the impact of coal mining on land use change over a period of four decades. The enhanced images were subjected to land-use classification using Support Vector Machines. Land-use classification accuracy was measured using confusion matrix-based accuracy assessment. Accuracy assessment revealed that the overall classification accuracy of DWT enhanced images was 82.10% for the year 1975 and 88.46% for the year 2015. The overall classification accuracy of DT-CWT enhanced images was 85.71% for the year 1975 and 88.54% for the year 2015. The results of change detection revealed that the total areal coverage of dense vegetation increased by 65%, indicating that the rate of land degradation had slowed down due to the legislative and policy changes to promote sustainable development in coal mining after 1986.

AN APPROACH TO EXTRACT MOVING OBJECTS FROM MLS DATA USING A VOLUMETRIC BACKGROUND REPRESENTATION

Abstract. Data recorded by mobile LiDAR systems (MLS) can be used for the generation and refinement of city models or for the automatic detection of long-term changes in the public road space. Since for this task only static structures are of interest, all mobile objects need to be removed. This work presents a straightforward but powerful approach to remove the subclass of moving objects. A probabilistic volumetric representation is utilized to separate MLS measurements recorded by a Velodyne HDL-64E into mobile objects and static background. The method was subjected to a quantitative and a qualitative examination using multiple datasets recorded by a mobile mapping platform. The results show that depending on the chosen octree resolution 87-95% of the measurements are labeled correctly.

Evaluation of long-term trends in extreme precipitation: Implications of in-filled historical data use for analysis

This study focuses on the assessment of biases from infilling missing precipitation data on the detection of long-term change using parametric and non-parametric statistical techniques. Long-term historical precipitation data available for almost 100years at 53 rain gages in south Florida, USA, with gages having varying lengths of missing data are used for the study. Precipitation data with gaps and time series with spatial interpolated data are analyzed. Chronologically complete datasets are often used in climate variability studies by analyzing data in multiple temporal windows. The temporal windows selected in this work coincide with Atlantic multi-decadal oscillation (AMO) cool and warm phases that strongly influence precipitation extremes and characteristics in the study region. Selection of these windows has helped in evaluating the extremes derived based on infilled and unfilled data. The frequency of occurrence of precipitation extremes over a pre-specified threshold is also analyzed. Results indicate that infilled precipitation data introduce large biases in the statistical trends and over and under-estimate low and high extremes respectively. Evaluation of three extreme precipitation indices (i.e. Rx1day, R25mm and R50mm) indicates that bias increases with increase in amount of missing data. Nonparametric hypothesis tests indicate that statistical distributions of data of infilled and unfilled data are different when the data infilled is more than 5% of the entire data. Infilled data also introduced high variability in precipitation extremes in AMO cool and warm phases along with the changes in the frequency of occurrence of extreme events over a threshold.

Monitoring desertification by remote sensing using the Tasselled Cap transform for long-term change detection

Sensitive areas like oases are threatened by climatic variations and human activities that can catalyze desertification processes. Remote sensing the Earth surface from satellites is a good tool to monitor such types of change through several techniques. In this paper a remote sensing method that has been widely used for vegetated areas is adapted to study dry regions. The method consists of a combination of the Change Vector Analysis and the Tasselled Cap (TC) transform. To adapt it to dryland conditions a new set of parameters for the TC transform is hereby calculated for the Landsat 8 OLI system. The new TC parameters are tested in the analysis of the surface change in Azraq Oasis, Jordan, over a time span of 30 years (1984–2013) for Landsat satellites images. Azraq is considered a good testing site since in the early 1990s it has been subject to a complete drying up of the superficial springs, mainly due to over-exploitation of the groundwater basin. Results show that the chosen technique is able to detect the expected change on the surface, consistent with photo-interpretation and historical information available.

LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS

Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000 km<sup>2</sup> covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5 m-pixel colour aerial photography to collect > 15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80 % and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.

LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS

Abstract. Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000 km2 covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5 m-pixel colour aerial photography to collect > 15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80 % and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.

Long-term patterns of air temperatures, daily temperature range, precipitation, grass-reference evapotranspiration and aridity index in the USA great plains: Part II. Temporal trends

Summary Detection of long-term changes in climate variables over large spatial scales is a very important prerequisite to the development of effective mitigation and adaptation measures for the future potential climate change and for developing strategies for future hydrologic balance analyses under changing climate. Moreover, there is a need for effective approaches of providing information about these changes to decision makers, water managers and stakeholders to aid in efficient implementation of the developed strategies. This study involves computation, mapping and analyses of long-term (1968–2013) county-specific trends in annual, growing-season (1st May–30th September) and monthly air temperatures [(maximum (Tmax), minimum (Tmin) and average (Tavg)], daily temperature range (DTR), precipitation, grass reference evapotranspiration (ETo) and aridity index (AI) over the USA Great Plains region using datasets from over 800 weather station sites. Positive trends in annual Tavg, Tmax and Tmin, DTR, precipitation, ETo and AI were observed in 71%, 89%, 85%, 31%, 61%, 38% and 66% of the counties in the region, respectively, whereas these proportions were 48%, 89%, 62%, 20%, 57%, 28%, and 63%, respectively, for the growing-season averages of the same variables. On a regional average basis, the positive trends in growing-season Tavg, Tmax and Tmin, DTR, precipitation, ETo and AI were 0.18 °C decade−1, 0.19 °C decade−1, 0.17 °C decade−1, 0.09 °C decade−1, 1.12 mm yr−1, 0.4 mm yr−1 and 0.02 decade−1, respectively, and the negative trends were 0.21 °C decade−1, 0.06 °C decade−1, 0.09 °C decade−1, 0.22 °C decade−1, 1.16 mm yr−1, 0.76 mm yr−1 and 0.02 decade−1, respectively. The temporal trends were highly variable in space and were appropriately represented using monthly, annual and growing-season maps developed using Geographic Information System (GIS) techniques. The long-term and spatial and temporal information and data for a large region provided in this study can be used to analyze county-level trends in important climatic/hydrologic variables in context of climate change, water resources, agricultural and natural resources response to climate change.

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection

Coal fires that are induced by natural spontaneous combustion or result from human activities occurring on the surface and in underground coal seams destroy coal resources and cause serious environmental degradation. Thermal infrared image data, which directly measure surface temperature, can be an important tool to map coal fires over large areas. As the first of two parts introducing our coal fire detection method, this paper proposes a self-adaptive threshold-based approach for coal fire detection using ASTER thermal infrared data: the self-adaptive gradient-based thresholding method (SAGBT). This method is based on an assumption that the attenuation of temperature along the coal fire’s boundaries generates considerable numbers of spots with extremely high gradient values. The SAGBT method applied mathematical morphology thinning to skeletonize the potential high gradient buffers into the extremely high gradient lines, which provides a self-adaptive mechanism to generate thresholds according to the thermal spatial patterns of the images. The final threshold was defined as an average temperature value reading from the high temperature buffers (segmented by 1.0 σ from the mean) and along a sequence of extremely high gradient lines (thinned from the potential high gradient buffers and segmented within the lower bounds, ranging from 0.5 σ to 1.5 σ and with an upper bound of 3.2 σ, where σ is the standard deviation), marking the coal fire areas. The SAGBT method used the basic outer boundary of the coal-bearing strata to simply exclude false alarms. The intermediate thresholds reduced the coupling with the temperature and converged by changing the potential high gradient buffers. This simple approach can be economical and accurate in identifying coal fire areas. In addition, it allows for the identification of thresholds using multiple ASTER TIR scenes in a consistent and uniform manner, and supports long-term coal fire change analyses using historical images in local areas. This paper focuses on the introduction of the methodology. Furthermore, an improvement to SAGBT is proposed. In a subsequent paper, subtitled “Part 2, Validation and Sensitivity Analysis,” we address satellite-field simultaneous observations and report comparisons between the retrieved thermal anomalies and field measurements in different aspects to prove that the coal fires are separable by the SAGBT method. These comparisons allowed us to estimate the accuracy and biases of the SAGBT method. As an application of the SAGBT, a relationship between coal fires’ decadal variation and coal production was also examined. Our work documented a total area increase in the beginning of 2003, which correlates with increased mining activities and the rapid increase of energy consumption in China during the decade (2001–2011). Additionally, a decrease in the total coal fire area is consistent with the nationally sponsored fire suppression efforts during 2007–2008. It demonstrated the applicability of SAGBT method for long-term change detection with multi-temporal images.

The Phenomenon of Nonzero Norm of Long-Term Changes in the Total Water Supply in River Basins

In hydrology, the norm of changes in water supply is considered to be equal to zero, under long-term averaging. This assumption allows for calculating and mapping the long-term evaporation rates linked by the water balance with maps of precipitation and river runoff. The phenomenon of nonzero norm of change of the total water supply is provided using the effect of detection in stochastic systems. Was discovered a previously unknown phenomenon of detection of long-term changes in water supply in soil of river basins, namely, that under non-zero interaction intensity of inflow (outflow) of moisture into a watershed and saturation (emptying) of soil, a non-zero value of the average annual values of water supply changes occurs, due to the systematic shift of the average value with respect to the mode of probability density distribution of water supplies. One of the obvious applications of the discovered phenomenon is the adjustment of maps of the average long term evaporation in the areas, where the norm of water supply change is significantly different from zero.

Assessing Long-Term Changes in the Beach Width of Reef Islands Based on Temporally Fragmented Remote Sensing Data

Atoll islands are subject to a variety of processes that influence their geomorphological development. Analysis of historical shoreline changes using remotely sensed images has become an efficient approach to both quantify past changes and estimate future island response. However, the detection of long-term changes in beach width is challenging mainly for two reasons: first, data availability is limited for many remote Pacific islands. Second, beach environments are highly dynamic and strongly influenced by seasonal or episodic shoreline oscillations. Consequently, remote-sensing studies on beach morphodynamics of atoll islands deal with dynamic features covered by a low sampling frequency. Here we present a study of beach dynamics for nine islands on Takú Atoll, Papua New Guinea, over a seven-decade period. A considerable chronological gap between aerial photographs and satellite images was addressed by applying a new method that reweighted positions of the beach limit by identifying “outlier” shoreline positions. On top of natural beach variability observed along the reweighted beach sections, we found that one third of the analyzed islands show a statistically significant decrease in reweighted beach width since 1943. The total loss of beach area for all islands corresponds to 44% of the initial beach area. Variable shoreline trajectories suggest that changes in beach width on Takú Atoll are dependent on local control (that is, human activity and longshore sediment transport). Our results show that remote imagery with a low sampling frequency may be sufficient to characterize prominent morphological changes in planform beach configuration of reef islands.

Next Generation Sequencing Reveals the Hidden Diversity of Zooplankton Assemblages

BackgroundZooplankton play an important role in our oceans, in biogeochemical cycling and providing a food source for commercially important fish larvae. However, difficulties in correctly identifying zooplankton hinder our understanding of their roles in marine ecosystem functioning, and can prevent detection of long term changes in their community structure. The advent of massively parallel next generation sequencing technology allows DNA sequence data to be recovered directly from whole community samples. Here we assess the ability of such sequencing to quantify richness and diversity of a mixed zooplankton assemblage from a productive time series site in the Western English Channel.Methodology/Principle FindingsPlankton net hauls (200 µm) were taken at the Western Channel Observatory station L4 in September 2010 and January 2011. These samples were analysed by microscopy and metagenetic analysis of the 18S nuclear small subunit ribosomal RNA gene using the 454 pyrosequencing platform. Following quality control a total of 419,041 sequences were obtained for all samples. The sequences clustered into 205 operational taxonomic units using a 97% similarity cut-off. Allocation of taxonomy by comparison with the National Centre for Biotechnology Information database identified 135 OTUs to species level, 11 to genus level and 1 to order, <2.5% of sequences were classified as unknowns. By comparison a skilled microscopic analyst was able to routinely enumerate only 58 taxonomic groups.ConclusionsMetagenetics reveals a previously hidden taxonomic richness, especially for Copepoda and hard-to-identify meroplankton such as Bivalvia, Gastropoda and Polychaeta. It also reveals rare species and parasites. We conclude that Next Generation Sequencing of 18S amplicons is a powerful tool for elucidating the true diversity and species richness of zooplankton communities. While this approach allows for broad diversity assessments of plankton it may become increasingly attractive in future if sequence reference libraries of accurately identified individuals are better populated.

Detection of long-term change in hydroelectric reservoir inflows: Bridging theory and practise

We studied long-term monotonic trend in inflow volume to 20 reservoirs, plus a measure of system-wide hydroelectric power potentially available for generation, in British Columbia, Canada. The primary analysis involved application of a linear method with explicit signal-to-noise ratio (S:N) estimation to monthly and annual mean inflows over the period of record common to all basins, providing a broad and convenient overview of changes across the hydroelectric system. Additional analyses then investigated the sensitivity of the results to methodological choices, focussing on annual inflow volume for eight indicator basins and system power. In these auxiliary analyses, alternative data selection and processing schemes were explored: full period of record for each indicator basin; residual after Pacific Decadal Oscillation signal removal; and data from streamgauges on catchments within or near each indicator basin. Alternative analysis techniques were also applied: nonparametric Spearman rank correlation with adjustment for serial dependence; change point detection using Mann–Whitney sliding window analysis; and visual assessment of data low-pass filtered using polynomial fits of various orders. Finally, we studied changes in year-to-year water supply variability, employing a split-sample approach implemented using a standard F-test; a nonparametric Ansari–Bradley test; and a novel Monte Carlo-based test on the Shannon entropy, a measure of hydroclimatic information content. The results provided no substantial evidence for decreased annual water supply and pointed to some increases; seasonal inflow pattern shifts were also detected for many basins, in particular a wintertime flow increase. The split-sample tests yielded no evidence for shifts in the volatility and predictability of annual inflow. One of the more intriguing and perhaps broadly important findings was that methodological choices ultimately made little difference to final outcomes.