Phenological Analysis Research Articles

Enhanced Blue Band Vegetation Index (The Re-Modified Anthocyanin Reflectance Index (RMARI)) for Accurate Farmland Shelterbelt Extraction

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research.

Tracking animal movements via collaborative acoustic telemetry networks: Multiscale habitat use, phenology, and management insights.

Estuaries support diverse fish and invertebrate communities, including resident species that rely on estuarine habitats year-round and transient migratory species. The unique movement patterns of these animals connect habitats within and far beyond the estuary and are integrally linked to fisheries management objectives. With a focus on Chesapeake Bay, this study leveraged data from collaborative acoustic telemetry networks in the northwest Atlantic to assess habitat use and phenology of movements for seven species of fish (cownose rays, dusky sharks, smooth dogfish, alewife, striped bass, common carp, and blue catfish) and one invertebrate (horseshoe crabs). A total of 288 acoustically tagged individuals were detected >3.2 million times (6,743 to 2,095,717 detections per species) on receivers across ~20.5 degrees of latitude spanning the North American Atlantic seaboard from Florida, USA, to New Brunswick, Canada. Common metrics of movement and phenology grouped these species as resident (common carp, blue catfish, horseshoe crabs), primarily resident in estuaries (juvenile striped bass), and coastal migrant (cownose rays, dusky sharks, smooth dogfish, alewife); maximum distance traveled varied by three orders of magnitude among these species. Further analysis of phenology for coastal migrants elucidated the timing and duration of these species' use of Chesapeake Bay. Collectively, movements linked habitats within Chesapeake Bay and connected the estuary to coastal ecosystems both to the north (e.g., alewife) and south (e.g., cownose rays), creating networks of fisheries management jurisdictions that varied in complexity and identified opportunities for enhancement to current management or co-management of some species. Our results elucidate the importance of estuaries to species with diverse movement behaviors, identify scales and pathways of habitat connectivity via animal movements, and highlight the utility of collaborative acoustic telemetry networks for quantifying movements relevant to both ecological research and fisheries management.

Biogeography, Conservation Status, and Traditional Uses of Zingiberaceae in Saraburi Province, Thailand, with Kaempferia chaveerachiae sp. nov.

This research investigates the Zingiberaceae family in Saraburi Province, Thailand, aiming to understand its biodiversity, distribution, ecology, conservation status, and traditional uses. Through systematic exploration, 103 species are identified, including a novel species, Kaempferia chaveerachiae Saensouk, P.Saensouk, and Boonma. Additionally, 23 species are newly reported for distribution in Saraburi Province. Methodologically, a district-wise analysis and the unweighted pair group method with arithmetic mean (UPGMA) cluster analysis were employed to discern species prevalence and geographical patterns. Findings reveal varied species distribution aligned with diverse ecological conditions, informing targeted conservation strategies. Fourteen endemic species and the District Endemism Index emphasize localized conservation significance. Assessment of conservation status highlights critical and vulnerable species, prompting focused conservation initiatives. Phenological analysis underscores adaptability through flowering peaks tied to environmental cues. Ethnobotanical insights highlight the multifunctionality of Zingiberaceae, emphasizing its cultural significance and traditional applications. This study offers valuable insights into Zingiberaceae diversity, advocating for tailored conservation strategies and sustainable practices in Saraburi Province to safeguard its unique flora, habitats, and indigenous knowledge.

Phenological Analysis of Grasses (Poaceae) in Comparison with Aerobiological Data in Moscow (Russia).

Grasses (Poaceae) produce large amounts of pollen and are among the main causes of pollinosis worldwide. Despite their morphological similarity, pollen grains of different grass species may have different allergenicities. Therefore, quantification of the roles of individual species in airborne pollen is an important task. There are very few studies on this topic, and none of them have been conducted in a temperate continental climate. Our study was carried out for three years (2020-2022) in the urban territory of Moscow (Russia) and aimed to understand what grass species contribute the most to the total pollen load of the atmosphere. The comparison of aerobiological and phenological data was based on calculating the phenological index, which is a combination of phenological parameters, pollen productivity of individual species, and their abundance. Our data showed that the decomposition of pollination curves based on the phenological index was sometimes very efficient but not always possible in temperate continental climates. The main reasons for disagreement between aerobiological and phenological data were weather conditions and lawn mowing. Not all grasses were equally important as sources of allergenic pollen. The greatest contribution to the pollen load at the beginning of the season in Moscow was made by Dactylis glomerata, and to a lesser extent by Phleum pratense and Festuca pratensis. These are the most common species, which are widespread throughout Europe. The contribution of minor components is insignificant.

I funghi della foresta dei Bagni di Masino (Sondrio, Italia). Osservazioni micofloristiche, fenologiche ed ecologiche

The results of the three-year survey (2019−2021) of the macromycetes of the “Val Masino - Bagni” Regional Forest, located in the eastern Rhaetian Alps, Bernina Alps subsection, are presented. The geographical, geological, geomorphological, pedological and vegetational characteristics are briefly illustrated. The local weather-climatic conditions were processed on the basis of daily temperature, precipitation and relative humidity data measured in the three-year period 2019-2021 from the nearest ARPA station (Val Masino - San Martino). The survey area, between 1100 and about 1300 m a.s.l., was divided into 11 homogeneous stations for geographical and vegetational characteristics, of which one in beech forest (Habitat EUNIS T18 - Fagus forest on acid soils), four in spruce forests (Habitat EUNIS T31 - Temperate mountain Picea forest), one in pastured clearing (Habitat EUNIS R57 - Herbaceous forest clearing vegetation), four in secondary pastures (Habitat EUNIS R21 - Mesic permanent pasture of lowlands and mountains) and one in anthropized area (dirt road, flowerbeds, parking lots). The survey led to the identification of 354 ma-cromycetes, to which a further 43 must be added, found in previous years by the members of the Mycological Group Cantù and Como. In total 397 macromycetes are currently known for the investigated area, of which 94.4% belong to the phylum Basidiomycota (375 species/varieties, 155 genera) and 5.6% to the phylum Ascomycota (22 species, 17 genera). 57 species/varieties (54 Basidiomycota and 3 Ascomycota) are reported for the first time for the province of Sondrio: Aleurodiscus amorphus, Ama-nita brunneofuliginea, A. spadicea, Amylostereum chailletii, Buchwaldoboletus lignicola, Connopus acervatus, Corticium meridioroseum, Cortinarius cinnabarinus, C. livido-ochraceus, C. torvus, Cylindrobasi-dium evolvens, Cystoderma jasonis, Cystodermella terryi, Faerberia carbonaria, Ganoderma resinaceum, Gymnopus hariolorum, Helvella bicolor, Homophron cernuum, Hydnotrya cerebriformis, Hygrophorocybe nivea, Hygrophorus unicolor, Hypholoma epixanthum, Lachnellula subtilissima, Lactarius albocarneus, L. subdulcis, Laxitextum bicolor, Lentinus substrictus, Lycoperdon echinatum, Mycena fagetorum, M. pelianthina, M. renati, Neoantrodia serialis, Neofavolus suavissimus, Panaeolus olivaceus, Panellus violaceofulvus, Phlebiopsis gigantea, Phylloporus pelletieri, Picipes melanopus, Pluteus salicinus, Polyporus tuberaster, Porostereum spadiceum, Postia tephroleuca, Psathyrella fagetophila, P. spintrigeroides, P. tephrophylla, Pycnoporellus fulgens, Ramaria stricta, Rhizocybe pruinosa, Rigidoporus sanguinolentus, Russula cavipes, R. farinipes, R. olivacea, Steccherinum fimbriatum, Stereum gausapatum, Tricholoma album, T. ustale, Xerocomellus pruinatus. A comparison with the Red List of threatened macromycetes in Switzerland shows that 31 species considered threatened were found in the survey area, equal to 7.8% of the total, of which one (Mycena fagetorum) belonging to the CR category (Critically Endangered), 3 to category EN (Endangered), 18 to category VU (Vulnerable) and 9 to category NT (Nearly Threatened). The phenological analysis, conducted excluding the 50 li-gnicolous taxa (Polyporaceae s.l. and Corticiaceae s.l.) whose fruiting bodies can persist in the environment from a few months to a few years and therefore alter the results, reveals that the month with the greatest mycological diversity is September with 237 taxa out of 347, equal to 68.3%, followed by October (145, 41.8%), July (99, 28.4%) and August (94, 26.9%). According to the season of discovery, the exclusively autumnal macromycetes (September, October and November) are 170, the exclusively estival ones (June, July and August) are 44 and those exclusively vernal (March, April and May) are 11. The evaluation of the ecosystemic health status of the forest habitats of the investigated area (stations 1−5), based on the spectra of trophic groups (biotrophic, ectomycorrhizal, mycoparasite, parasite, saprotrophic, sapro-ectomycorrhizal and sapro-parasite), shows that only station 4 (outside the borders of the Regional Forest) presents a percentage value of ectomycorrhizal species indicating a phase of acute disturbance (36.9%), while all the others show a good state of health with values between 49.6% (station 1) and 54% (station 5). In the investigated area, some species indicating degradation processes already underway (Megacollybia platyphylla, Clitocybe phaeophthalma and Apioperdon pyriforme) and future (Mycena amicta, M. galericulata, M. pelianthina and M. pura) were found. In addition, the comparison of the percentage of ectomycorrhizal macromycetes surveyed within the two fenced areas located within the station 2 (73.8%) with that of the station 2 as a whole (51.2%) shows how the anthropic disturbance, resulting from the high tourist attendance (many thousands of people every year, mostly concentrated in the months from May to September), has consequences not only on the vegetation, but also on the growth and distribution of the mycoflora. Finally, lignicolous, beech forest and grassland habitats ma-cromycetes are taken into consideration, with some observations on taxa considered of particular interest or rarity. In particular, the mycoflora of the beech forest is analyzed in detail, given that this habitat constitutes the most valuable element of the investigated area. Of the 354 taxa surveyed in the three-year period 2019−2021, those that, on the basis of the literature consulted, can be considered more or less closely associated with beech are 40, of which 13 exclusive and 27 preferential.

A global analysis of ice phenology for 3702 lakes and 1028 reservoirs across the Northern Hemisphere using Sentinel-2 imagery

As existing global lake ice studies have predominantly focused on medium to large lakes, and reservoir ice studies have been limited to regional scales, very few studies of ice phenology have combined both lakes and reservoirs of different sizes. This study aims to characterize the freeze-up and break-up dates of 3702 lakes and 1028 reservoirs from 1 to 31,000 km2 across the Northern Hemisphere, and to analyze spatial patterns and relationships between ice phenological dates and driving factors. The freeze-up and break-up dates of these water bodies were retrieved from Sentinel-2 imagery using an ice detection algorithm through the Google Earth Engine platform from 2019 to 2023. The algorithm was verified by comparing phenology dates with an independent database based on observations from passive microwave sensors, with a mean absolute error of 18 days for both freeze-up and break-up dates. This newly established ice phenology database along with various geographic, morphometric, and climatic characteristics of the water bodies, was used to develop a random forest model for predicting ice phenology dates. While the predictive model performance is at a fair level (mean absolute error of 12 days for both freeze-up and break-up), challenges were encountered in certain high-elevation areas where cloudy conditions as well as black ice resulted in delayed freeze-up dates. Among the variables included in the random forest model, latitude and accumulation of freezing degree days were identified as the main drivers of ice phenology dates. Despite the challenges of applying a single, straightforward method on a global scale, this study has allowed the creation of a vast and comprehensive database of lake and reservoir freeze-up and break-up dates that can be used by the community to further analyze ice patterns.

Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

A satellite-field phenological bridging framework for characterizing community-level spring forest phenology using multi-scale satellite imagery

Forest phenology, as a sensitive indicator of a forest’s response to climate change and variability, has long been monitored using remote sensing, yet has seldom been interpreted or validated with spatially compatible, community-level field phenological observations. In temperate deciduous forests, multiple spring phenological phases are critical for modeling carbon storage and biogeochemical cycles. The simultaneous detection of all these critical phenological phases at the community level remains a long-standing challenge. To tackle these challenges, the objective of this study is to develop a novel satellite-field phenological bridging framework for characterizing all key spring phenological phases of a fragmented deciduous forest using multi-scale satellite time series. The framework consists of four key components: deep learning-based spatiotemporal image fusion, satellite-based forest phenology modeling, satellite-based forest phenological metric extraction, and field-based forest community phenological characterization. With the devised framework, we extract a total of 24 satellite phenological metrics of Trelease Woods, a forest fragment near Urbana, IL, USA. From weekly field phenological observations of 148 canopy trees of 15 common species of the forest community, we devise three summative field phenological indices to characterize community-level phenological states in spring. Under the satellite-field bridging framework, events during each key spring forest phenological phase (i.e., bud swell, budburst/leafing out, leaf expansion, and leaf maturity) are successfully detected using the fusion imagery (MAE from 1.1 to 2.9 days and bias from -2.4 to 1.5 days). However, the satellite detection of the earliest field events may be influenced by understory plants, soil background, and snow. The subsequent multi-scale, satellite phenological analysis underscores the importance of taking into account spatial scale and representation from both satellite and field phenological perspectives in building corresponding bridging relationships. Among the extracted pheno-metrics, bridging the threshold-based metrics to field phenological indices results in the highest accuracy (MAE less than 3 days and bias less than 2 days). The strong agreement among the field indices demonstrates the effectiveness of our field phenological surveying approach in generating community-wide forest phenological representation. Our study innovatively scales up the field phenological observations from the individual trees to the species to the community level, and the devised framework enables accurate retrieval of all key phenological events of community-wide, spring canopy development of the forest fragment.

Vegetation profile and ecological traits in irrigated plains of semi-arid regions: A case study of Okara, Punjab Pakistan

Comprehensive botanical assessments are pivotal for understanding the intricate world of plant biodiversity. These assessments serve multifaceted purposes, from cataloging significant plant species within a region to aiding in conservation, monitoring protected areas, and providing valuable data for various scientific disciplines. The primary objective was to assess the vegetation profile and ecological traits in this region, conduct a comprehensive floristic inventory, categorize species, and investigate adaptability to semi-arid conditions within the irrigated plains of Okara, Punjab, Pakistan. Comprehensive botanical assessments were conducted to document the floristic composition and ecological characteristics of the region. A total of 135 plant species, belonging to 118 genera and 46 families, were identified. Notably, Poaceae and Fabaceae were the most prominent families. The study revealed diverse growth habits, with herbaceous plants being the most abundant. Life form analysis indicated Therophytes as the dominant category, and Microphylls were the most prevalent leaf type. Flowering phenology analysis highlighted two major flowering periods, primarily influenced by plant habit and size. Habitat-wise distribution demonstrated variations in species preferences, with unique associations observed in forest, vegetable, maize, paddy, and mustard fields. Invasive species accounted for 16.93% of the flora, with several herbs, shrubs, and trees identified as invasive. These findings contribute valuable insights into the botanical diversity and ecological traits of the Okara region, with implications for biodiversity conservation and land management strategies.

Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest.

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937-1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate-phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals.

Relationship Analysis of Phenology, Stress Tolerance, and Mean Productivity in Wilt and Cold Stressed Chickpea (Cicer arietinum L.) Following Synthetic PGRs Application

To understand the phenotypic response to mitigate stress tolerance for achieving maximum mean productivity, a comparative study of four synthetic PGRs—Abscisic Acid (ABA), Naphthyl Acetic Acid (NAA), Salicylic Acid (SA), and Fusaric Acid (FA)—was conducted as a pot experiment against artificially inoculated Fusarium oxysporum (a wilt pathogen) and cold exposure in four chickpea varieties. Additionally, the relationship between traits and PGRS application was looked at to evaluate their role in the stress tolerance mechanism. The results showed that ABA at 5 and 2 ppm was effective in delaying flowering, therefore extending the vegetative development phase in plants. In this way, flowering promotes stress tolerance while evading the damaging impacts of wilting and cold. This resulted in a lower percentage of wilt and a reduced incidence of cold compared to all other treatments, which raised mean productivity. The use of ABA at 5 and 2 ppm has been shown to positively correlate with both the prolongation of vegetative development and the delay of flowering. But fusaric acid (FA), a fungal toxin, is what caused early flowering, which allowed the flower to coincide with the development of wilt and cold. The occurrence of wilt at seedling and cold during flowering accelerated the incidence of wilt and cold, which led to a reduced mean productivity after giving fusaric acid (FA) @ 10 and 20 ppm. According to the investigation, the application of fusaric acid (FA) at 10 and 20 ppm was found to be positively and highly correlated with an increase in the incidence of wilt and cold and consequently negatively correlated with the mechanism possessed by ABA at 5 and 2 ppm.

Satellite-based phenology analysis of the Hyrcanian forest in the Alborz mountains and Caspian Sea shores, Iran

ABSTRACT The Caspian Sea and the Hyrcanian forest rely on each other to maintain balanced hydrological and climatological conditions and preserve the local wildlife and natural resources. Climate variation impacts this balance and the ecological status of the forest. In this study, thermal and phenological seasonality in three Hyrcanian protected areas is evaluated over a period of 33 years (1987–2019). The results suggest extensions of 3–18 days per decade in growing seasons. This could be mitigated by applying policies such as the expansion of protected areas, stringent endangered species conservation, updated silviculture practices, social awareness and cross-sectoral management.

Fine classification and phenological analysis of rice paddy based on multi-temporal general compact polarimetric SAR data.

Fine classification and phenological information of rice paddy are of great significance for precision agricultural management. General compact polarimetric (CP) synthetic aperture radar (SAR) offers the advantage of providing rich polarimetric information, making it an important means of monitoring rice growth. Therefore, in response to the current challenges of difficulty in rice type classification and the small differences in phenological polarimetric characteristics, a novel strategy for fine classification and phenological analysis of rice paddy is proposed. This strategy thoroughly explores the polarimetric information of general CP SAR data and the target scattering characterization capabilities under different imaging modes. Firstly, the general CP SAR data is formalized using the standard CP descriptors, followed by the extraction of general CP features through the ΔαB /αB target decomposition method. optimal CP features are generated to achieve fine classification of rice paddy. Finally, 6 phenological stages of rice are analyzed based on the general CP features. The experiment results of rice classification show that the classification accuracy based on this strategy exceeds 90%, with a Kappa coefficient above 0.88. The highest classification accuracies were observed for transplanting hybrid rice paddy (T-H) and direct-sown japonica rice paddy (D-J), at 80.9% and 89.9%, respectively. The phenological evolution rule of the two rice types indicate that from early June (seedling stage) to late July (elongation stage), the CP feature variation trends of T-H and D-J are generally consistent. However, from October (mature stage) to November (harvest stage), the variation trends of the CP features for T-H and D-J are significantly different. The study found that from the booting-heading stage to the harvest stage, the linear π/4 mode outperforms circular and elliptical polarimetric modes in distinguishing different types of rice. Throughout the entire phenological period of rice growth, CP SAR of linear π/4 mode is surpasses that of other linear modes in discriminating different type of rice. The proposed strategy enables high-precision fine classification rice paddy, and the extracted general CP αB parameter effectively reflects the phenological change trends in rice growth.

Mechanism analysis of vegetation phenology in an urban agglomeration in an arid zone driven by seasonal land surface temperatures

In arid areas, vegetation is the habitat that sustains the life and survival of species, and it also plays a vital role in soil erosion prevention. This paper investigates the spatial aggregation patterns and trends of vegetation phenology (VP) and land surface temperature (LST) from 2002 to 2021. Additionally, the spatial Durbin model was used to analyze seasonal elements such as air temperature, evaporation, LST, precipitation, soil moisture, aerosol optical depth, and the impact of DEM on VP. Finally, explore the relationship between seasonal LST and urbanization and VP. The results show that in arid areas, the vegetation start of season time (SOS) showed an advancing trend, the vegetation end of season time (EOS) and length of season time (LOS) showed an extending trend, and the vegetation time of middle of season (MOS) tended to be stable. VP changes in oasis areas fluctuated significantly, while VP varies in non-oasis regions were stable. Under the scenario of spatial heterogeneity, LST is the most influential factor in VP. Spring LST has the greatest impact on SOS, winter LST on EOS and LOS. Urbanization advances SOS and extends EOS but has less impact on LOS. This study's results are essential for understanding the complex mechanism of VP changes in oases in arid regions and analyzing the distribution characteristics of VP under urbanization levels.

Unveiling lake ice phenology in Central Asia under climate change with MODIS data and a two-step classification approach

Lakes in Central Asia, characterized largely by a semiarid climate and delicate ecosystems, are quite sensitive and vulnerable to climate change but remain less exploited, due mostly to a lack of in-situ measurements. Lake ice phenology is an essential climate variable identified by the Global Climate Observation System. To overcome the overestimation of clouds by the MODIS cloud mask, we developed and distributed a two-step Random Forest algorithm based on MODIS data on the Google Earth Engine. This algorithm first distinguishes cloudy pixels from non-cloudy pixels, and then ice pixels are discriminated from non-cloudy pixels using additional thermal infrared information. We validated the algorithm by implementing it over several boreal lakes with in-situ records in North America and compared our results with existing lake ice phenology datasets. We subsequently retrieved lake ice phenology for 20 large lakes (> 100 km2) in Central Asia from 2001 to 2021 and examined trends in lake ice phenology over this period. On average, lakes in Central Asia underwent later freezing at 0.13 d/yr, earlier thawing at 0.32 d/yr, and shorter ice duration at 0.45 d/yr, with over half of the lakes witnessing significant trends in at least one ice phenology indicator. To evaluate the impact of meteorological factors on lake ice phenology, we built a linear mixed model that predicts ice phenology using meteorological variables from ERA5. Daily mean air temperature dominates ice phenology, with mean air temperature during October to June in the following year explaining approximately 35% of the variance of the duration between freezing and thawing for each lake. Our study offers a comprehensive analysis of lake ice phenology in Central Asia under climate change.

Conducting video therapy with adults with an intellectual disability: A qualitative study of qualified and trainee NHS psychologists’ experiences

Online therapy is increasingly popular and necessary. There is little research regarding experiences of psychologists delivering online therapy to people with an intellectual disability. This study used interpretive phenological analysis to explore the experiences of five psychologists delivering online therapy to individuals with an intellectual disability. Four superordinate themes were identified regarding environment, person-centred working, rapport, and clinician ability and experience. Psychologists spoke of prior negative beliefs, challenges and discomforts to video therapy. However, motivation to continue offering therapy to clients overweighed this, with benefits of using video therapy including increased accessibility. A hybrid model including face-to-face and online therapy appears preferable. Training is recommended for supporting clients, and an assessment tool for suitability of online therapy. Further research exploring online therapy experiences of people with intellectual disabilities is required.

From frequency to intensity – A new index for annual large-scale cropping intensity mapping

Continuous population growth, global warming, extreme weather and local wars pose considerable challenges to agricultural production. Excessive agricultural intensification may lead to serious environmental problems such as water resources depletion and non-point source pollution. Sustainable agricultural intensification is a potential solution, and cropping intensity and its spatio-temporal patterns are of great significance to the planet hearth, human well-being and sustainable development of agriculture. However, traditional methods for cropping intensity mapping lack the deep analysis of crop phenology and failure to consider the spatial detail, spatial coverage and time continuity simultaneously. Existing cropping intensity indicators, such as cropping frequency or multiple crop index, have their limitations in China because of its diverse topographies and fragmented landscapes. In actual fact, few indicators for fine cropping intensity have been developed that address this problem. Time series vegetation index data contain meaningful vegetation phenology information. Information mining on these data can provide new insights for cropping intensity mapping. In this study, the concept of systematic mapping was put forward to measure cropping intensity with a fresh perspective. A new phenology-based cropping intensity index (PCII) was proposed to reach the goal of “from frequency to intensity” in cropping intensity mapping. Through thoroughly exploring the temporal spectral characteristics of croplands and considering the crop pattern as a whole, PCII set up the mapping between cropping intensity and land-cover types. The method can accurately map cropping intensity even though the study spanned a vast area, and can handle the effects of geographical differentiation caused by vegetation phenology differences. It can also precisely measure cropping intensity at the abundance level and reflect the heterogeneity within a pixel. It’s a novel method for annual fine cropping intensity mapping at large-scale and long-term.

Comparing Phenology of a Temperate Deciduous Forest Captured by Solar-Induced Fluorescence and Vegetation Indices

A shifting phenology in deciduous broadleaf forests (DBFs) can indicate forest health, resilience, and changes in the face of a rapidly changing climate. The availability of satellite-based solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) promises to add to the understanding of the regional-level DBF phenology that has been developed, for instance, using proxies of gross primary productivity (GPP) from the Moderate Imaging Spectroradiometer (MODIS). It is unclear how OCO-2 and MODIS metrics compare in terms of capturing intra-annual variations and benchmarking DBF seasonality, thus necessitating a comparison. In this study, spatiotemporally matched OCO-2 SIF metrics (at footprint level) and corresponding MODIS GPP, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) products within a temperate DBF were used to compare the phenology captured by the productivity metrics. Additionally, an estimate of the SIF yield (SIFy), derived from OCO-2 SIF measurements, and a MODIS fraction of photosynthetically active radiation (fPAR) were tested. An examination of the trends and correlations showed relatively few qualitative differences among productivity metrics and environmental variables, but it highlighted a lack of seasonal signal in the calculation of SIFy. However, a seasonality analysis quantitatively showed similar seasonal timings and levels of seasonal production in and out of the growing season between SIF and GPP. In contrast, NDVI seasonality was least comparable to that of SIF and GPP, with senescence occurring approximately one month apart. Taken together, we conclude that satellite-based SIF and GPP (and EVI to a smaller degree) provide the most similar measurements of forest function, while NDVI is not sensitive to the same changes. In this regard, phenological metrics calculated with satellite-based SIF, along with those calculated with GPP and EVI from MODIS, can enhance our current understanding of deciduous forest structures and functions and provide additional information over NDVI. We recommend that future studies consider metrics other than NDVI for phenology analyses.

Estimation of the Indonesian drought based on phenology vegetation analysis of maize

Climate change has a widespread influence on food essentials. Climate change is a pressing issue in Indonesia, a rapidly developing country. As a result, to forecast dryness during maize production in Indonesia’s Central East Java districts, it was proposed that a study be done on vegetation phenology. The study uses the vegetation to calculate the NDWI (Water Index) and Temperature (LST), calculate drought severity based on precipitation data. NDWI and LST were obtained from Landsat 8 OLI with a spatial resolution of 30 m and were used to identify water shortage and temperature within the study area. The inquiry was finished in 2018 and showed that the vegetation phenology was based on the growing season. The growing season of maize report from the NDVI (Vegetation Index) trajectories that April 2018 was the maize planting season, and the harvest was completed in later August. Additionally, LST analysis found that the temperature was higher in mid and southern Central East Java, Indonesia. To validate the data, rainfall information was used to compute the drought severity using SPI method to identify drought-prone areas. Drought severity validation data were validated for vegetation phenology analysis in 2018.

Spatial Analysis of Intra-Annual Reed Ecosystem Dynamics at Lake Neusiedl Using RGB Drone Imagery and Deep Learning

The reed belt of Lake Neusiedl, covering half the size of the lake, is subject to massive changes due to the strong decline of the water level over the last several years, especially in 2021. In this study, we investigated the spatial and temporal variations within a long-term ecosystem research (LTER) site in a reed ecosystem at Lake Neusiedl in Austria under intense drought conditions. Spatio-temporal data sets from May to November 2021 were produced to analyze and detect changes in the wetland ecosystem over a single vegetation period. High-resolution orthomosaics processed from RGB imagery taken with an unmanned aerial vehicle (UAV) served as the basis for land cover classification and phenological analysis. An image annotation workflow was developed, and deep learning techniques using semantic image segmentation were applied to map land cover changes. The trained models delivered highly favorable results in terms of the assessed performance metrics. When considering the region between their minima and maxima, the water surface area decreased by 26.9%, the sediment area increased by 23.1%, and the vegetation area increased successively by 10.1% over the investigation period. Phenocam data for lateral phenological monitoring of the vegetation development of Phragmites australis was directly compared with phenological analysis from aerial imagery. This study reveals the enormous dynamics of the reed ecosystem of Lake Neusiedl, and additionally confirms the importance of remote sensing via drone and the strengths of deep learning for wetland classification.