Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

Environmental drivers of water use for Caatinga woody plant species: combining remote sensing phenology and sap flow measurements

Abstract: We investigated the water use of Caatinga vegetation, the largest seasonally dry forest in South America. We identified and analysed the environmental drivers of phenology in woody species and their relationship with transpiration. To monitor the phenological evolution we used remote sensing indices at different spatial and temporal scales: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and green chromatic coordinate (GCC). To represent the phenology we used the GCC extracted from in-situ automated digital camera images; indices calculated based on sensors included NDVI, SAVI and GCC from Sentinel-2A and B satellites images, and NDVI products MYD13Q1 and MOD13Q1 from moderate-resolution imaging spectroradiometer (MODIS). Environmental drivers included continuously monitored rainfall, air temperature, soil moisture, net radiation, and vapour pressure deficit. To monitor soil water status and vegetation water use we installed soil moisture sensors along three soil profiles and sap flow sensors for five plant species. Our study demonstrated that the near-surface GCC data played an important role in allowing individual monitoring of species whereas the species’ sap flow data correlated better with NDVI, SAVI and GCC than with species’ near-surface GCC. The wood density appeared to affect the transpiration cessation times in the dry season given species with the lowest wood density reach negligible values of transpiration earlier in the season than those with high woody density. Our results show that soil water availability is the main limiting factor for transpiration during more than 80 % of the year, and that both the phenological response and water use are directly related to water availability when relative saturation of the soil profile falls below 0.25

Linking plant phenology to conservation biology

Phenology has achieved a prominent position in current scenarios of global change research given its role inmonitoring and predicting the timing of recurrent life cycle events. However, the implications of phenology to environmental conservation and management remain poorly explored. Here,we present the first explicit appraisal of howphenology-amultidisciplinary science encompassing biometeorology, ecology, and evolutionary biology- can make a key contribution to contemporary conservation biology. We focus on shifts in plant phenology induced by global change, their impacts on species diversity and plant-animal interactions in the tropics, and how conservation efforts could be enhanced in relation to plant resource organization. We identify the effects of phenological changes and mismatches in the maintenance and conservation of mutualistic interactions, and examine how phenological research can contribute to evaluate, manage and mitigate the consequences of land-use change and other natural and anthropogenic disturbances, such as fire, exotic and invasive species. Wealso identify cutting-edge tools that can improve the spatial and temporal coverage of phenological monitoring, from satellites to drones and digital cameras. We highlight the role of historical information in recovering long-term phenological time series, and track climate-related shifts in tropical systems. Finally, we propose a set of measures to boost the contribution of phenology to conservation science.Weadvocate the inclusion of phenology into predictive models integrating evolutionary history to identify species groups that are either resilient or sensitive to future climate-change scenarios, and understand how phenological m ismatches can affect community dynamics, ecosystem services, and conservation over time

Multiscale phenology of seasonally dry tropical forests in an aridity gradient

The leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing of new leaves triggered by the first rains of the wet season. Such phenological transitions may not be accurately detected by remote sensing vegetation indices and derived transition dates (TDs) due to the coarse spatial and temporal resolutions of satellite data. The aim of this study was to compared TDs from PhenoCams and satellite remote sensing (RS) and used the TDs calculated from PhenoCams to select the best thresholds for RS time series and calculate TDs. For this purpose, we assembled cameras in seven sites along an aridity gradient in the Brazilian Caatinga, a region dominated by SDTFs. The leafing patterns were registered during one to three growing seasons from 2017 to 2020. We drew a region of interest (ROI) in the images to calculate the normalized green chromatic coordinate index. We compared the camera data with the NDVI time series (2000–2019) derived from near-infrared (NIR) and red bands from MODIS product data. Using calibrated PhenoCam thresholds reduced the mean absolute error by 5 days for SOS and 34 days for EOS, compared to common thresholds in land surface phenology studies. On average, growing season length (LOS) did not differ significantly among vegetation types, but the driest sites showed the highest interannual variation. This pattern was applied to leaf flushing (SOS) and leaf fall (EOS) as well. We found a positive relationship between the accumulated precipitation and the LOS and between the accumulated precipitation and maximum and minimum temperatures and the vegetation productivity (peak and accumulated NDVI). Our results demonstrated that (A) the fine temporal resolution of phenocamera phenology time series improved the definitions of TDs and thresholds for RS landscape phenology; (b) long-term RS greening responded to the variability in rainfall, adjusting their timing of green-up and green-down, and (C) the amount of rainfall, although not determinant for the length of the growing season, is related to the estimates of vegetation productivity

The diversity and evolution of pollination systems in large plant clades: Apocynaceae as a case study

Background and Aims Large clades of angiosperms are often characterized by diverse interactions with pollinators, but how these pollination systems are structured phylogenetically and biogeographically is still uncertain for most families. Apocynaceae is a clade of >5300 species with a worldwide distribution. A database representing >10 % of species in the family was used to explore the diversity of pollinators and evolutionary shifts in pollination systems across major clades and regions. Methods The database was compiled from published and unpublished reports. Plants were categorized into broad pollination systems and then subdivided to include bimodal systems. These were mapped against the five major divisions of the family, and against the smaller clades. Finally, pollination systems were mapped onto a phylogenetic reconstruction that included those species for which sequence data are available, and transition rates between pollination systems were calculated. Key Results Most Apocynaceae are insect pollinated with few records of bird pollination. Almost three-quarters of species are pollinated by a single higher taxon (e.g. flies or moths); 7 % have bimodal pollination systems, whilst the remaining approx. 20 % are insect generalists. The less phenotypically specialized flowers of the Rauvolfioids are pollinated by a more restricted set of pollinators than are more complex flowers within the Apocynoids + Periplocoideae + Secamonoideae + Asclepiadoideae (APSA) clade. Certain combinations of bimodal pollination systems are more common than others. Some pollination systems are missing from particular regions, whilst others are over-represented. Conclusions Within Apocynaceae, interactions with pollinators are highly structured both phylogenetically and biogeographically. Variation in transition rates between pollination systems suggest constraints on their evolution, whereas regional differences point to environmental effects such as filtering of certain pollinators from habitats. This is the most extensive analysis of its type so far attempted and gives important insights into the diversity and evolution of pollination systems in large clades

A dataset of Neotropical liana research focusing on the strategies of control for forest restoration and management practices

Abstract Key message Forest fragmentation leads to a micro-environmental condition that favors the proliferation of liana, which infest trees, compete with them, and reduce their performance. To report the state of the art of the main actions to manage this structural component of tropical forests, we surveyed the control strategies in the literature in the last 71 years, highlighting research goals, tree-climber interactions, management, restoration, and conservation. Dataset access is at https://doi.org/10.5281/zenodo.6678112 . Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/712ff481-dfa2-4ddb-b4fa-fcbd7c517842 Context Lianas (woody vines) are considered structural parasites of tropical trees because they start their development as terrestrial seedlings but need to reach a tree canopy for higher light availability. The tree-liana coexistence usually can damage tree species, thus removing lianas has been suggested as an alternative to reinforce forest regeneration. Aims The dataset compilation begun during the first author doctoral work and a first dataset on neotropical lianas was published ( https://doi.org/10.5281/zenodo.4050477 ) in 2020. The present dataset ( https://doi.org/10.5281/zenodo.6678112 ) presents an update of the 2020 dataset with additional amend (published articles from 2018 to 2021) and enhanced metadata descriptions. Our aim is providing an updated database extracted from scientific literature compiling information related to the effect of lianas on tree and forest structure and diversity, and to contribute to improve decision making on forest restoration and management. Methods We made a systematic literature review on lianas in the Neotropical region (native or restored) from 1950 to 2021. First, we selected studies on liana management and described each paper according to the following topics: vegetation status, positive (P), and negative (N) effects of lianas on each species, the species in focus, and the suggested management strategy. Results Almost 83% of the studies pointed out tree-climber interactions as negative to trees. Cutting was the management strategy adopted in 92% of the studies. Controlled burning, enrichment, and selective cutting were adopted in only one paper. Rainy and seasonal forests were the vegetation types with more studied sites (20 and 17 respectively). Only one study suggested enhancing forest diversity through direct seeding of lianas. Four studies evaluated the impact of lianas on forest diversity and forest fauna. Conclusion The data collected showed the different impacts of liana management on the diversity and structure of tropical forests. It can endorse environmental control and management practices and evaluate the consequences of these techniques in recovering forests or improving timber production