Spectral Trajectories Research Articles

Spheres of maximum electromagnetic chirality

The search for objects that yield maximum electromagnetic chirality in their emitted wave field has gathered significant attention in recent years. However, achieving such maximum chirality is challenging, as it typically requires complex chiral metamaterials. Here, we demonstrate that chiral spheres can yield maximum chirality in their emitted wave field. Specifically, we analytically find the spectral trajectories at which chiral spheres become optically transparent to a given helicity of the incident field, while for its opposite helicity, they behave as dual objects, i.e., on scattering, they preserve helicity. Since chiral spheres behave as dual objects at the first Kerker condition of zero backscattering, we significantly simplify this condition in terms of a Riccati-Bessel function. Our analytical findings suggest the creation of spherical building blocks for designing metasurfaces or metamaterials with maximum electromagnetic chirality properties. Published by the American Physical Society 2024

Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli

Current tests of hearing fail to diagnose pathologies in ~10% of patients seeking help for hearing difficulties. Neural ensemble responses to perceptually relevant cues in the amplitude envelope, termed envelope following responses (EFR), hold promise as an objective diagnostic tool to probe these ‘hidden’ hearing difficulties. But clinical translation is impeded by current measurement approaches involving static amplitude modulated (AM) tones, which are time-consuming and lack optimal spectrotemporal resolution. Here we develop a framework to rapidly measure EFRs using dynamically varying AMs combined with spectrally specific analyses. These analyses offer 5x improvement in time and 30x improvement in spectrotemporal resolution, and more generally, are optimal for analyzing time-varying signals with known spectral trajectories of interest. We validate this approach across several mammalian species, including humans, and demonstrate robust responses that are highly correlated with traditional static EFRs. Our analytic technique facilitates rapid and objective neural assessment of temporal processing throughout the brain that can be applied to track auditory neurodegeneration using EFRs, as well as tracking recovery after therapeutic interventions.

Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests

AbstractWestern North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

A fast spectral recovery does not necessarily indicate post-fire forest recovery

BackgroundClimate change has increased wildfire activity in the western USA and limited the capacity for forests to recover post-fire, especially in areas burned at high severity. Land managers urgently need a better understanding of the spatiotemporal variability in natural post-fire forest recovery to plan and implement active recovery projects. In burned areas, post-fire “spectral recovery”, determined by examining the trajectory of multispectral indices (e.g., normalized burn ratio) over time, generally corresponds with recovery of multiple post-fire vegetation types, including trees and shrubs. Field data are essential for deciphering the vegetation types reflected by spectral recovery, yet few studies validate spectral recovery metrics with field data or incorporate spectral recovery into spatial models of post-fire vegetation recovery. We investigated relationships between spectral recovery and field measurements of post-fire recovery (16 to 27 years post-fire) from 99 plots in mixed conifer forests of the Blue Mountains, USA. Additionally, using generalized linear mixed effects models, we assessed the relative capacities of multispectral, climatic, and topographic data to predict field measurements of post-fire recovery.ResultsWe found that a fast spectral recovery did not necessarily coincide with field measurements of forest recovery (e.g., density of regenerating seedlings, saplings, and young trees and % juvenile conifer cover). Instead, fast spectral recovery often coincided with increases in % shrub cover. We primarily attributed this relationship to the response of snowbrush ceanothus, an evergreen shrub that vigorously resprouts post-fire. However, in non-trailing edge forests—where it was cooler and wetter and fast-growing conifers were more common—rapid spectral recovery coincided with both increases in % shrub cover and forest recovery. Otherwise, spectral recovery showed potential to identify transitions to grasslands, as grass-dominated sites showcased distinctly slow spectral trajectories. Lastly, field measurements of post-fire forest recovery were best predicted when including post-fire climate and multispectral data in predictive models.ConclusionsDespite a disconnect between a fast spectral recovery and forest recovery, our results suggest that including multispectral data improved models predicting the likelihood of post-fire forest recovery. Improving predictive models would aid land managers in identifying sites to implement active reforestation projects.Graphical Photo credit: J. Celebrezze

Spatio‐Temporal Dynamics of Aboveground Biomass in China's Oasis Grasslands Between 1989 and 2021

AbstractGrassland provides multiple ecosystem services and plays a key role in preventing desert encroachment and maintaining oasis stability. In China, the area of cropland in oases has expanded significantly in recent decades, which results in a rapid increase in agricultural water demand and encroachment on grassland subsistence space. However, our knowledge about how the expansion of cropland affects oasis grasslands remains limited. We used machine learning, temporal segmentation of spectral trajectories, and maximum covariance analysis to generate an annual oasis grassland aboveground biomass (AGB) data set at 30‐m resolution from 1989 to 2021 based on multiple remotely sensed and ground observation data sets, and investigated the dynamics of grassland AGB under cropland expansion in oases. We found that overall oasis grassland AGB increased significantly (0.3 gm−2 yr−1, P < 0.01) during 1989–2021, but trends in AGB were not consistent across basins. In the Yellow River, Turpan Hami, Qaidam, and southern Altai Mountains River Basins, AGB was dominated by a significant increase. Conversely, in all basins in southern Xinjiang, AGB showed a decreasing trend due to the rapid cropland expansion. Spatially, cropland expansion and AGB dynamics were strongly coupled. In regions characterized by agricultural concentration, grassland AGB benefitted from resource spillover via edge effects. However, in downstream areas or those with a low proportion of cropland, where most grasslands are distributed, the relationship between the two shifted to a trade‐off. Our study provides a scientific basis for identifying priority areas for ecological restoration and for science‐based planning of the scale of cropland in oases.

Dynamic blending and assimilation in Catalan lingual fricative sequences. An ultrasound and acoustic study

Abstract Ultrasound and center of gravity frequency data for the sequences /ʃ#s/ and /s#ʃ/ produced by Central Catalan speakers reveal that the former sequence is implemented through continuous articulatory and spectral trajectories which, depending on speaker, may be: intermediate between /ʃ/ and /s/ all throughout, thus supporting a dynamic blending mechanism; /ʃ/-like at onset and intermediate between the two fricatives at offset, which is indicative of C1-to-C2 carryover coarticulation. The sequence /s#ʃ/, on the other hand, undergoes regressive assimilation into [ʃ(ʃ)] according to the acoustic signal but less clearly so in the light of the articulatory data. This discrepancy appears to be due to the fact that, while C1=/s/ assimilates indeed to C2=/ʃ/ at constriction location, coarticulation-induced changes in tongue body configuration behind the primary articulator may occur as long as they do not jeopardize the front-cavity dependent frequency characteristics of the [ʃ] frication noise. Differences in articulatory complexity between /ʃ#s/ and /s#ʃ/ appear to result from the production mechanisms involved, i.e., tongue dorsum raising behind the /s/ constriction for /s#ʃ/ and tongue body repositioning for /ʃ#s/. In agreement with this interpretation, /ʃ#s/ but not /s#ʃ/ turned out to be longer than /s#s/ and /ʃ#ʃ/.

Spatio-temporal spectral trajectory pattern to continuous maps of forest disturbance and recovery: case of tropical forests of Vatovavy Fitovinany, Madagascar

Spatio-temporal spectral trajectory pattern to continuous maps of forest disturbance and recovery: case of tropical forests of Vatovavy Fitovinany, Madagascar

Urban Renewal Mapping: A Case Study in Beijing from 2000 to 2020

Understanding the distribution and land history of old urban areas (OUAs) and renewed urban areas (RUAs) has become the key point of urban management. However, it is hard to acquire adequate information for lack of pertinent detection methods. Here, we established a complete mapping framework on Google Earth Engine (GEE) platform to identify OUAs and RUAs and detect the temporal information of urban renewal, which was implemented in Beijing during 2000–2020. We used Landsat imagery and LandTrendr algorithm to fit the spectral trajectories of 14 bands/indices with specific segment attributes as the feature inputs for Random Forest classification. We produced the maps of OUAs and RUAs with an overall accuracy of 95.36%. On this basis, we further utilized LandTrendr to detect the start year, end year, and duration of urban renewal with the accuracies within the ±5-year difference of 85.52%, 80.97%, and 74.53%, respectively. These maps all present informative spatiotemporal patterns. Furthermore, the urban renewal process is likely to be influenced by major national or international events. The study answers the issues about urban renewal from multiple angles and provides scientific support for future urban planning.

The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches

Drought-induced forest dieback can lead to a tipping point in community dominance, but the coupled response at the tree and stand-level response has not been properly addressed. New spatially and temporally integrated monitoring approaches that target different biological organization levels are needed. Here, we compared the temporal responses of dendrochronological and spectral indices from 1984 to 2020 at both tree and stand levels, respectively, of a drought-prone Mediterranean Pinus pinea forest currently suffering strong dieback. We test the influence of climate on temporal patterns of tree radial growth, greenness and wetness spectral indices; and we address the influence of major drought episodes on resilience metrics. Tree-ring data and spectral indices followed different spatio-temporal patterns over the study period (1984–2020). Combined information from tree growth and spectral trajectories suggests that a reduction in tree density during the mid-1990s could have promoted tree growth and reduced dieback risk. Additionally, over the last decade, extreme and recurrent droughts have resulted in crown defoliation greater than 40% in most plots since 2019. We found that tree growth and the greenness spectral index were positively related to annual precipitation, while the wetness index was positively related to mean annual temperature. The response to drought, however, was stronger for tree growth than for spectral indices. Our study demonstrates the value of long-term retrospective multiscale analyses including tree and stand-level scales to disentangle mechanisms triggering and driving forest dieback.

Characterization of Wildfires and Harvesting Forest Disturbances and Recovery Using Landsat Time Series: A Case Study in Mediterranean Forests in Central Italy

Large-scale forest monitoring benefits greatly from change detection analysis based on remote sensing data because it enables characterizing forest dynamics of disturbance and recovery by detecting both gradual and abrupt changes on Earth’s surface. In this study, two of the main disturbances occurring in Mediterranean forests, harvesting operations and forest fires, were analyzed through the analysis of Landsat Times Series images in a case study in Central Italy (Tuscany region). Disturbances were characterized based on their distinct temporal behaviors before and after the event: a period of 20 years (1999–2018) was used to extract and analyze at pixel level spectral trajectories for each disturbance and produce descriptive temporal trends of the phenomena. Recovery metrics were used to characterize both short- (5 years) and long-term aspects of recovery for harvested and burned areas. Spectral, recovery, and trend analysis metrics were then used with the Random Forest classifier to differentiate between the two disturbance classes and to investigate their potential as predictors. Among spectral bands, the Landsat SWIR 1 band proved the best to detect areas interested by harvesting, while forest fires were better detected by the SWIR 2 band; among spectral indices, the NBR scored as the best for both classes. On average, harvested areas recovered faster in both short- and long-term aspects and showed less variability in the magnitude of the disturbance event and recovery rate over time. This tendency is confirmed by the results of the classifier, which obtained an overall accuracy of 98.6%, and identified the mean of the post-disturbance values of the trend as the best predictor to differentiate between disturbances.

Continuous Change Detection and Classification—Spectral Trajectory Breakpoint Recognition for Forest Monitoring

Forest is one of the most important surface coverage types. Monitoring its dynamics is of great significance in global ecological environment monitoring and global carbon circulation research. Forest monitoring based on Landsat time-series stacks is a research hotspot, and continuous change detection is a novel approach to real-time change detection. Here, we present an approach, continuous change detection and classification-spectral trajectory breakpoint recognition, running on Google Earth Engine (GEE) for monitoring forest disturbance and forest long-term trends. We used this approach to monitor forest disturbance and the change in forest cover rate from 1987 to 2020 in Nanning City, China. The high-resolution Google Earth images are collected for the validation of forest disturbance. The classification accuracy of forest, non-forest, and water maps by using the optima classification features was 95.16%. For disturbance detection, the accuracy of our map was 86.4%, significantly higher than 60% of the global forest change product. Our approach can successfully generate high-accuracy classification maps at any time and detect the forest disturbance time on a monthly scale, accurately capturing the thinning cycle of plantations, which earlier studies failed to estimate. All the research work is integrated into GEE to promote the use of the approach on a global scale.

Recovery of tree community composition across different types of anthropogenic disturbances and characterization of their effect using Landsat time series in Bornean tropical montane forest

Anthropogenic pressure in tropical montane forests is rapidly increasing, becoming a major threat to these complex ecosystems. Studies have shown that the wide variety of human activities in tropical uplands results in different ecological responses of secondary forests, but basic information on the disturbance impacts and underlying recovery processes is lacking. Here, we compared structural characteristics and tree community composition of old growth forest and secondary forests in a montane region of Sabah, Malaysia, which experienced five different anthropogenic disturbances. We also investigated the use of metrics from spectral trajectories of a Landsat time series (LTS) change detection algorithm (LandTrendr) to identify characteristics of disturbance events and their linkage to the recovery of tree community composition, with field validation. Five LTS metrics—time since the greatest disturbance (TSD), magnitude of disturbance (MD), distance to undisturbed forests (d_UND), recovery indicator (RI), and years to recovery (Y2R) were derived and were related to field-based tree community composition. Our analysis revealed a gradient of recovery patterns in community composition and structural attributes among forest disturbance types, suggesting the importance of community composition as an indicator of forest recovery. Among derived LTS metrics, TSD, MD, d_UND, and Y2R 100% were significantly related with the similarity in community composition. Our results suggest that spectral trajectories from LTS can serve as a useful predictor of community composition change in recovering stands. This approach provides an efficient means for developing systematic conservation strategies for high-elevation regions in the tropics, where human-modified landscapes are expanding.

Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.

Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2

In the past decade, massive outbreaks of bark beetles (Ips spp.) have caused large-scale decline of coniferous-dominated, prevailingly managed forests of Central Europe. Timely detection of newly infested trees is important for minimizing economic losses and effectively planning forest management activities to stop or at least slow outbreaks. With the advancement of Copernicus services, a pair of Sentinel-2 satellites provides a unique remote sensing data source of multi-temporal observations in high spatial resolution on the scale of individual forest stands (although not allowing for individual tree detection). This study investigates the potential for using seasonal trajectories of Sentinel-2 bands and selected vegetation indices in early detection of bark beetle infestation (so–called green-attack stage detection) in Norway spruce monoculture forests in the Czech Republic. Spectral trajectories of nine bands and six vegetation indices were constructed for the 2018 vegetation season using 14 satellite observations from April to November to distinguish four infestation classes. We used a random forest algorithm to classify healthy (i.e., stands not infested) and infested trees with various trajectories of decay. The seasonal trajectories of vegetation indices separated the infestation classes better than did the individual bands. Among the most promising vegetation indices we identified the tasselled cap wetness (TCW) component and normalized difference index constructed from near and shortwave infrared bands. Analysing the inter-annual change of the indices was more promising for early detection than is single-date classification. It achieved 96% classification accuracy on day of year 291 for the tested data set.The algorithm for early detection of tree infestation based on the assessment of seasonal changes in TCW was applied on the time series of Sentinel-2 observations from 2019 and its outputs were verified using field observations of forest conditions conducted on 80 spruce forest plots (located in spruce monoculture stands). The overall accuracy of 78% was achieved for the separation of healthy and green-attack classes. Our study highlights the great potential of multi-temporal remote sensing and the use of shortwave infrared wavelengths for early detection of spruce forest decline caused by bark beetle infestation.

Change in forest condition: Characterizing non-stand replacing disturbances using time series satellite imagery

Objective information is required to monitor and characterize disturbances and disturbance regimes as related to changing climate and anthropogenic pressures. Forest disturbances occur over a range of spatial and temporal scales, with varying extent, severity, and persistence. To date, most of our understanding of detecting forest disturbances using remotely sensed imagery has been based on detecting abrupt and rapid, stand replacing disturbances, such as those related to wildfire and forest harvesting. Conversely, more continuous, subtle, and gradual non-stand replacing (NSR) disturbances, such as those related to drought stress or insect infestation have been subject to less focus. This can be attributed to the variability of NSR in space and time, as well as detection difficulties due to their often-subtle alterations to forest canopies and structure. Time series remote sensing techniques offer new opportunities for the capture and characterization of NSR disturbances. In this research, we investigate NSR disturbances as detected using a Landsat time series-based disturbance detection algorithm, Composite2Change (C2C) applied to the 650 Mha forested ecosystems of Canada. We first examined three key characteristics of the spectral trajectory of NSR disturbances: pre-disturbance conditions, disturbance severity and disturbance persistence. From these characteristics, we defined seven typical NSR patterns (exemplars) for Canada’s forested ecosystems. Using annual information on predicted stand-level cover and biomass, we found that all NSR exemplars resulted in a loss of canopy cover, ranging from an average loss of between 5 and 31%. For instance, the most common exemplar was linked to a 15% reduction in canopy cover and a 13 Mg/ha reduction in biomass. While stands lost biomass, over the duration of the NSR, most stands increased slightly in stand height likely due to growth of the remaining trees or release from competition from the remaining stems. Comparing exemplars across Canada with independent data on NSR disturbance events confirmed that NSR exemplars that had larger severities, and shorter disturbance persistence times, were more commonly associated with insect infestations (e.g. mountain pine beetle (Dendroctonus ponderosae) and spruce budworm (Choristoneura fumiferana)), whereas exemplars with a lower severity, and longer persistence were more commonly associated with drought events. Developing a nomenclature around remote sensing based characteristics of NSR disturbances provides an approach to both map NSR disturbances and better understand their impact on forest attributes, as well as provide tools to assess if these disturbanes are changing over space and time.

Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series

Climate change and severe extreme events, i.e., changes in precipitation and higher drought frequency, have a large impact on forests. In Poland, particularly Norway spruce and Scots pine forest stands are exposed to disturbances and have, thus experienced changes in recent years. Considering that Scots pine stands cover approximately 58% of forests in Poland, mapping these areas with an early and timely detection of forest cover changes is important, e.g., for forest management decisions. A cost-efficient way of monitoring forest changes is the use of remote sensing data from the Sentinel-2 satellites. They monitor the Earth’s surface with a high temporal (2–3 days), spatial (10–20 m), and spectral resolution, and thus, enable effective monitoring of vegetation. In this study, we used the dense time series of Sentinel-2 data from the years 2015–2019, (49 images in total), to detect changes in coniferous forest stands dominated by Scots pine. The simple approach was developed to analyze the spectral trajectories of all pixels, which were previously assigned to the probable forest change mask between 2015 and 2019. The spectral trajectories were calculated using the selected Sentinel-2 bands (visible red, red-edge 1–3, near-infrared 1, and short-wave infrared 1–2) and selected vegetation indices (Normalized Difference Moisture Index, Tasseled Cap Wetness, Moisture Stress Index, and Normalized Burn Ratio). Based on these, we calculated the breakpoints to determine when the forest change occurred. Then, a map of forest changes was created, based on the breakpoint dates. An accuracy assessment was performed for each detected date class using 861 points for 46 classes (45 dates and one class representing no changes detected). The results of our study showed that the short-wave infrared 1 band was the most useful for discriminating Scots pine forest stand changes, with the best overall accuracy of 75%. The evaluated vegetation indices underperformed single bands in detecting forest change dates. The presented approach is straightforward and might be useful in operational forest monitoring.

Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series

This work analyses the rate of recovery of the spectral signal from clearcut areas of coppice Mediterranean forests using Landsat Time Series (LTS). The analysis revealed a more rapid rate of spectral signal recovery than what was found in previous investigations in boreal and temperate forests. The rate of post-disturbance vegetation recovery is an important component of forest dynamics. In this study, we analyze the recovery of the spectral signal from forest clearcut areas in Mediterranean conditions when the coppice system of forest management is applied. We used LTS surface reflectance data (1999–2015).We generated an annual reference database of clearcuts using visual interpretation and local forest inventory data, and then derived the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) spectral trajectories for these clearcuts. From these spectral trajectories, we calculated the Years to Recovery or Y2R, the number of years it takes for a pixel to return to within a specified threshold (i.e., 70%, 80%, 90%, 100%) of its pre-disturbance value. Spectral recovery rates were then corroborated using measures of canopy height derived from airborne laser scanning (ALS) data. The coppice system is associated with rapid recovery rates when compared to rates of recovery from seeds or seedlings in temperate and boreal forest conditions. We found that the Y2R derived from the spectral trajectories of post-clearcut NBR and NDVI provided similar characterizations of rapid recovery for the coppice system of forest management applied in our study area. The ALS measures of canopy height indicated that the Y2R metric accurately captured the rapid regeneration of coppice systems. The rapid rate of spectral recovery associated with the coppice system is 2–4 years, which contrasts with values reported in boreal and temperate forest environments, where spectral recovery was attained in approximately 10 years. NBR is an effective index for assessing rapid recovery in this forest system.

Mapping Land Cover Change over a 25-Year Period (1993–2018) in Sri Lanka Using Landsat Time-Series

Land use and land cover change (LULCC) are dynamic over time and space due to human and biophysical factors. Accurate and up-to-date LULCC information is a mandatory part of environmental change analysis and natural resource management. In Sri Lanka, there is a significant temporal gap in the existing LULCC information due to the civil war that took place from 1983 to 2009. In order to fill this gap, this study presents a whole-country LULCC map for Sri Lanka over a 25-year period using Landsat time-series imagery from 1993 to 2018. The LandTrendr change detection algorithm, utilising the normalised burn ratio (NBR) and normalised difference vegetation index (NDVI), was used to develop spectral trajectories over this time period. A land cover change and disturbance map was created with random forest, using 2117 manually interpreted reference pixels, of which 75% were used for training and 25% for validation. The model achieved an overall accuracy of 94.14%. The study found that 890,003.52 hectares (ha) (13.5%) of the land has changed, while 72,266.31 ha (1%) was disturbed (but not permanently changed) over the last 25 years. LULCC was found to concentrate on two distinct periods (2000 to 2004 and 2010 to 2018) when social and economic stability allowed greater land clearing and investment opportunities. In addition, LULCC was found to impact forest reserves and protected areas. This new set of Sri Lanka-wide land cover information describing change and disturbance may provide a reference point for policy makers and other stakeholders to aid in decision making and for planning purposes.

Pretrained convolutional neural network for classifying rice-cropping systems based on spatial and spectral trajectories of Sentinel-2 time series

The rice-cropping system in southern China experienced a major change in the past few decades. Rice-cropping systems not only affect the comprehensive utilization intensity of agricultural resources, food security, and the ecological environment but also reveal the conditions of the agricultural policy, regional economy, and rural labor force. Consequently, accurate detection of rice-cropping systems is an essential issue. We present a method for classifying rice-cropping systems using a pretrained convolutional neural network (CNN) that involves extracting deep features of spatial and spectral trajectories. The study area is a major rice-growing region located in Zhuzhou City, Hunan Province, China. Multitemporal Sentinel-2 satellite images were collected to obtain time series curves of texture (spatial trajectories) and the first derivative vegetation index (spectral trajectories) as inputs of the hierarchical classification model. The classification results of land cover and rice-cropping systems were acquired using the pretrained CNN, and the method achieved overall accuracy of 94.78% and 94.87%, respectively. This method was then compared with the support vector machine (SVM). The accuracy of rice-cropping systems using the pretrained CNN was 7.11% higher than that of the SVM. The adaptability of the model was also investigated using the time series curve in another year with relatively insufficient data. The model obtained satisfactory performance with the overall accuracy of land cover types and rice-cropping systems of 93.52% and 93.23%, respectively. We suggest that the pretrained CNN can improve the accuracy of rice-cropping system mapping by extracting deep features of spatial and spectral trajectories during the rice growth cycle and such method may be applied in other fine crop classifications.

A constrained tonal semi-supervised non-negative matrix factorization to classify presence/absence of wheezing in respiratory sounds

From a clinical point of view, the detection of wheezing presence in respiratory sounds is a challenging task for early identification of pulmonary diseases since wheezing is the main manifestation associated to airway obstruction. In this article, we propose a novel method to detect the presence or absence of wheeze sounds in breath recordings in order to increase the reliability of the subjective diagnosis provided by the physician in the auscultation process. Specifically, it is assumed an unhealthy subject when wheeze sounds can be detected during breathing. The proposed method consists of three stages. The first stage attempts to estimate the spectral interval, band of interest (BOI), that shows the highest probability to find wheeze sounds. In the second stage, a constrained tonal semi-supervised non-negative matrix factorization (NMF) approach is applied to obtain spectral patterns that models the periodic or tonal nature typically shown by wheeze sounds. The third stage analyzes the estimated wheezing spectrogram based on the smoothness of the spectral trajectories from the most significant energy previously factorized in the BOI. Our system has been evaluated and compared to other state-of-the-art methods, yielding competitive results in the wheezing presence detection in respiratory sounds.