Explore the vast range of titles available.

Background Local ecological calendars are ecocultural frameworks that link temporal and spatial scales, contributing to resilience and adaptive management of natural resources and landscapes. They also facilitate management, access and withdrawal of provisioning ecosystem services. In this article, we describe how the ecological calendar of the Kedayan people of Brunei Darussalam links skyscape and biodiversity with sociocultural aspirations to foster adaptive management of landscape, and provide an understanding of the transmission of calendric knowledge in the community. Methods In 2018, we collaborated with sixteen purposively sampled knowledge keepers from the Kedayan community of Brunei Darussalam to document the Kedayan local ecological calendar, and develop a calendrical pictogram. Using a structured questionnaire, we then interviewed 107 randomly selected community members, to understand the contemporary relevance and popularity of the Kedayan calendar, and the transmission of calendric knowledge in the community. Results Our findings reveal that very few respondents ( n  = 27, 25.3%) are aware of the existence of Kedayan ecological calendar; majority ( n  = 80, 74.7%) were not aware of its existence. There is no statistically significant correlation between consulting healers, knowledge on appropriate time requisite to consult healers, and awareness and self-professed knowledge of Kedayan calendar. Only 14 (13.1%) of the respondents reported to have received some form of calendric knowledge, while the majority (86.9%; n  = 93) never received any calendric knowledge. Only a negligible 1.9% reported to have transmitted calendric knowledge to others indicating a breakdown in transmission of calendric knowledge. Conclusion The calendric pictogram would help the community in revitalizing their calendar. However, the community will have to invest on enhancing transmission of calendric knowledge.

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Babaçu (Attalea pindobassu) is a valuable palm native to the Caatinga, inhabiting the mountains of the Chapada Diamantina, northeastern Brazil. We explored the Local Ecological Knowledge (LEK) of the Coxo de Dentro extractivist community regarding babaçu’s flowering and fruiting and the alignment with the phenological pattern observed through monthly monitoring of this palm, from January to December 2021. We investigated the impact of climatic factors on the reproductive phenology of babaçu, considering LEK and monthly phenological monitoring. LEK was examined through phenological calendars developed within focus groups. The reproductive events of the babaçu were found to be continuous, with flowering responding positively to photoperiod and fruiting positively influenced by temperature, consistent with LEK of the palm’s phenology. There were no significant differences in the time intervals of reproductive events between field-monitored phenological observations and LEK. Babaçu extractivists from Coxo de Dentro possess in-depth knowledge about the reproductive phenology of this palm as well as the climatic factors affecting its phenological expression. We suggest that tapping LEK is a promising approach for rapid recognition of tropical plant phenological patterns. In the case of Attalea pindobassu, we recommend that fruit harvesting be concentrated during the fruiting peak periods of January and February.

Cropland abandonment is one of the most widespread types of land-use change in Southern China. Quickly and accurately monitoring spatial-temporal patterns of cropland abandonment is crucial for food security and a good ecological balance. There are still enormous challenges in the long-term monitoring of abandoned cropland in cloud and rain-prone and cropland-fragmented regions. In this study, we developed an approach to automatically obtain Landsat imagery for two key phenological periods, rather than as a time series, and mapped annual land cover from 1989 to 2021 based on the random forest classifier. We also proposed an algorithm for pixel-based, long-term annual land cover correction based on prior knowledge and natural laws, and generated cropland abandonment maps for Guangdong Province over the past 30 years. This work was implemented in Google Earth Engine. Accuracy assessment of the annual cropland abandonment maps for every five years during study period revealed an overall accuracy of 92–95%, producer (user) accuracy of 90–96% (73–87%), and Kappa coefficients of 0.81–0.88. In recent decades, the cropland abandonment area was relatively stable, at around 50 × 104 ha, while the abandonment rate gradually increased with a decrease in the cultivated area after 2000. The Landsat-based cropland abandonment monitoring method can be implemented in regions such as southern China, and will support food security and strategies for maintaining ecological balance.

The Pūre orchard is one of the oldest apple orchards in the Baltic, where thousands of varieties of fruit trees from throughout the world are grown and tested. Over time, a huge knowledge base has been accumulated, but most of the observational data are stored in archives in paper format. We have digitized a small part of the full flowering phenological data of apple trees (Malus domestica) over the period of 1959 to 2019 for 17 varieties of apple trees, a significant step for horticulture and agricultural economics in Latvia. Climate change has led to significant changes in the phenology of apple trees as all varieties, autumn, summer and winter, have begun to flower earlier: from 2002 to 2019, on average full flowering was recorded to have taken place around 21 May, whereas for the period 1959–1967 it occurred around 27–28 May. To develop better-quality phenological predictions and to take account of the fragmentary nature of phenological data, in our study we assessed the performance of three meteorological data sets – gridded observation data from E-OBS, ERA5-Land reanalysis data and direct observations from a distant meteorological station – in simple phenological degree-day models. In the first approximation, the gridded E-OBS data set performs best in our phenological model.

Indigenous Peoples in Northwestern North America have always worked with predictable cycles of day and night, tides, moon phases, seasons, and species growth and reproduction, including such phenological indicators as the blooming of flowers and the songs of birds. Negotiating variability has been constant in people's lives. Long‐term monitoring and detailed knowledge of other lifeforms and landscapes of people's home territories have assisted in responding and adapting to change. Aspects of cultural knowledge and practice that have helped Indigenous Peoples navigate nature's cycles at different scales of time and space include kin ties and social relationships, experiential learning, language, storytelling and timing of ceremonies such as “First Foods” celebrations. Working with ecological processes, Indigenous Peoples have been able to maintain optimal conditions for preferred species, reducing variability and uncertainty through taking care of productive habitats, leaving ecosystems intact, and allowing other species to change in their own cycles. Since the onset of colonization, however, Indigenous Peoples' lifeways have been changed drastically, culminating with the current impacts of global climate change and biodiversity loss. This paper, based on contributions of numerous Indigenous Knowledge holders from across Northwestern North America, outlines some of the key ways in which Indigenous Peoples have embraced predictability and change in their environments and lifeways, and addresses the particular threat of climate change: its recognition, ways of adapting to it, and, ultimately, how it might be reversed through developing more careful, respectful relationships with and responsibilities for the other‐than‐human world. Plain Language Summary Indigenous Peoples of Northwestern North America have, for millennia, lived within seasonal cycles, using the life cycles of plants, birds, and other local species as indicators for harvesting. Their own calendars also mark the times of year when they can normally access and process the foods, materials, and medicines they rely upon and interact with. Indigenous Peoples have long held respectful, interdependent relationships with the plants and animals of their homelands, and have developed many different ways of tending and caring for these species, as well as creating adaptive practices, enabling them to respond to unanticipated shocks and events such as floods or unexpected loss of fish. The arrival of European colonizers caused many changes to Indigenous Peoples' lifeways, resulting in overall resource depletion and, most recently, drastic declines in biodiversity tied with global climate change, industrialization, and colonization. However, Indigenous Peoples' knowledge, practices, and strategies remain critically important, and are absolutely vital in identifying, alleviating, and reversing the impacts of these combined threats. Equally crucial are ethical ways of working together for the benefit of all. Key Points Indigenous Knowledge has guided Peoples of Northwestern North America in optimizing their seasonal activities in synchrony with biological species The breadth and variety of Indigenous Knowledge prepared people for interannual variation, and helped them face the impacts with resilience Currently, with biodiversity loss and climate change threatening protective systems, Indigenous Knowledge is as critically important as ever

Documenting effects of climate change is an important step towards designing mitigation and adaptation responses. Impacts of climate change on terrestrial biodiversity and ecosystems have been well-documented in the Northern Hemisphere, but long-term data to detect change in the Southern Hemisphere are limited, and some types of change are generally difficult to measure. Here we present a novel approach using local ecological knowledge to facilitate a continent-scale view of climate change impacts on terrestrial biodiversity and ecosystems that people have perceived in Australia. We sought local knowledge using a national web-based survey, targeting respondents with close links to the environment (e.g. farmers, ecologists), and using a custom-built mapping tool to ask respondents to describe and attribute recent changes they had observed within an area they knew well. Results drawn from 326 respondents showed that people are already perceiving simple and complex climate change impacts on hundreds of species and ecosystems across Australia, significantly extending the detail previously reported for the continent. While most perceived trends and attributions remain unsubstantiated, >35 reported anecdotes concurred with examples in the literature, and >20 were reported more than once. More generally, anecdotes were compatible with expectations from global climate change impact frameworks, including examples across the spectrum from organisms (e.g. increased mortality in >75 species), populations (e.g. changes in recruitment or abundance in >100 species, phenological change in >50 species), and species (e.g. >80 species newly arriving or disappearing), to communities and landscapes (e.g. >50 examples of altered ecological interactions). The overarching pattern indicated by the anecdotes suggests that people are more often noticing climate change losers (typically native species) than winners in their local areas, but with observations of potential 'adaptation in action' via compositional and phenological change and through arrivals and range shifts (particularly for native birds and exotic plants). A high proportion of climate change-related anecdotes also involved cumulative or interactive effects of land use. We conclude that targeted elicitation of local ecological knowledge about climate change impacts can provide a valuable complement to data-derived knowledge, substantially extending the volume of explicit examples and offering a foundation for further investigation.

Earth observation data processing requires interpretable deep learning (DL) models that learn physically significant and meaningful features. The current study proposes approaches to make the network to learn meaningful features. In addition, a set of interpretability- and explanation-based evaluation strategies are proposed to evaluate the DL models. Adversarial variational encoding along with constraints to regulate latent representations and embed label information are employed to learn interpretable manifold. The proposed architecture, called interpretable adversarial encoding network (IAENet), significantly improves the results compared to other main existing DL models. The proposed IAENet learns the features which are essential in distinguishing the different classes thereby improving the interpretability of the model. The explanations for the different models are generated through analysis of the concepts learned by each model using activation maximization. Besides, the relevance assigned by the model to input features is also estimated using the layer-wise relevance propagation approach. Experiments on the phenological curve-based crop classification illustrate that IAENet learn relevant features (giving importance to the non-rainy season) to distinguish different irrigation schemes. The performance can be attributed to the learned interpretable manifold, and the refinement of architectural units and convolutions considering the point-nature and irregular sampling of the input data. Experiments on learning crop-specific features from multispectral images for crop-type classification indicate that IAENet learns red and green edge features crucial in distinguishing the studied crops. The improvement in interpretability of the DL models is found to reduce the sensitivity toward network parameters. The proposed evaluation measures facilitate ascertaining the physical significance of the learned manifold.

Shifts in the timing of phenological events in plants and animals are cited as one of the most robust bioindicators of climate change. Much effort has thus been placed on the collection of phenological datasets, the quantification of the rates of phenological shifts and the association of these shifts with recorded meteorological data. These outputs are of value both in tracking the severity of climate change and in facilitating more robust management approaches in forestry and agriculture to changing climatic conditions. However, such an approach requires meteorological and phenological records spanning multiple decades. For communities in the Delta State of Nigeria, small-scale farming communities do not have access to meteorological records, and the dissemination of government issued daily to seasonal forecasts has only taken place in recent years. Their ability to survive inter-annual to inter-decadal climatic variability and longer-term climatic change has thus relied on well-entrenched indigenous knowledge systems (IKS). An analysis of the environmental cues that are used to infer the timing and amount of rainfall by farmers from three communities in the Delta State reveals a reliance on phenological events, including the croaking of frogs, the appearance of red millipedes and the emergence of fresh rubber tree and cassava leaves. These represent the first recorded awareness of phenology within the Delta State of Nigeria, and a potentially valuable source of phenological data. However, the reliance of these indicators is of concern given the rapid phenological shifts occurring in response to climate change.