Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model

Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery (60 cm) from the USDA-NAIP program. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP images collected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km$^2$ sites across various forest types and landscape variations in California. Our predictions of tree heights exhibited a mean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present in California. In 2020, trees taller than 5 m covered ~ 19.3% of California. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models. The approach we used allowed for the reconstruction of the three-dimensional structure of individual trees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation and mapping capability, even in the presence of image distortion. These findings demonstrate the potential of large-scale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery.Comment: 29 pages, 9 figures, submitted to Remote Sensing in Ecology and Conservation (RSEC

Life cycle of bamboo in the southwestern Amazon and its relation to fire events

Bamboo-dominated forests comprise 1 % of the world's forests and 3 % of the Amazon forests. The Guadua spp. bamboos that dominate the southwest Amazon are semelparous; thus flowering and fruiting occur once in a lifetime before death. These events occur in massive spatially organized patches every 28 years and produce huge quantities of necromass. The bamboo-fire hypothesis argues that increased dry fuel after die-off enhances fire probability, creating opportunities that favor bamboo growth. In this study, our aim is to map the bamboo-dominated forests and test the bamboo-fire hypothesis using satellite imagery. Specifically, we developed and validated a method to map the bamboo die-off and its spatial distribution using satellite-derived reflectance time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) and explored the bamboo-fire hypothesis by evaluating the relationship between bamboo die-off and fires detected by the MODIS thermal anomalies product in the southwest Amazon. Our findings show that the near-infrared (NIR) is the most sensitive spectral interval to characterize bamboo growth and cohort age. Automatic detection of historical bamboo die-off achieved an accuracy above 79 %. We mapped and estimated 15.5 million ha of bamboo-dominated forests in the region. The bamboo-fire hypothesis was not supported because only a small fraction of bamboo areas burned during the analysis timescale, and, in general, bamboo did not show higher fire probability after the die-off. Nonetheless, fire occurrence was 45 % higher in dead than live bamboo in drought years, associated with ignition sources from land use, suggesting a bamboo-human-fire association. Although our findings show that the observed fire was not sufficient to drive bamboo dominance, the increased fire occurrence in dead bamboo in drought years may contribute to the maintenance of bamboo and potential expansion into adjacent bamboo-free forests. Fire can even bring deadly consequences to these adjacent forests under climate change effects. © 2018 Author(s)

Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR

Logging, including selective and illegal activities, is widespread, affecting the carbon cycle and the biodiversity of tropical forests. However, automated approaches using very high resolution (VHR) satellite data (≤1 m spatial resolution) to accurately track these small-scale human disturbances over large and remote areas are not readily available. The main constraint for performing this type of analysis is the lack of spatially accurate tree-scale validation data. In this study, we assessed the potential of VHR satellite imagery to detect canopy tree loss related to selective logging in closed-canopy tropical forests. To do this, we compared the tree loss detection capability of WorldView-2 and GeoEye-1 satellites with airborne LiDAR, which acquired pre- and post-logging data at the Jamari National Forest in the Brazilian Amazon. We found that logging drove changes in canopy height ranging from −5.6 to −42.2 m, with a mean reduction of −23.5 m. A simple LiDAR height difference threshold of −10 m was enough to map 97% of the logged trees. Compared to LiDAR, tree losses can be detected using VHR satellite imagery and a random forest (RF) model with an average precision of 64%, while mapping 60% of the total tree loss. Tree losses associated with large gap openings or tall trees were more successfully detected. In general, the most important remote sensing metrics for the RF model were standard deviation statistics, especially those extracted from the reflectance of the visible bands (R, G, B), and the shadow fraction. While most small canopy gaps closed within ~2 years, larger gaps could still be observed over a longer time. Nevertheless, the use of annual imagery is advised to reach acceptable detectability. Our study shows that VHR satellite imagery has the potential for monitoring the logging in tropical forests and detecting hotspots of natural disturbance with a low cost at the regional scale

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is < 2000ĝ€-mmĝ€-yrĝ'1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000ĝ€-mmĝ€-yrĝ'1. Author(s) 2016.Fil: Wagner, Fabien H.. Instituto Nacional de Pesquisas Espaciais; BrasilFil: Hérault, Bruno. Ecologie Des Forets de Guyane; BrasilFil: Bonal, Damien. Institut National de la Recherche Agronomique; FranciaFil: Stahl, Clment. Universiteit Antwerp; BélgicaFil: Anderson, Liana O.. National Center For Monitoring And Early Warning Of Natural Disasters; BrasilFil: Baker, Timothy R.. University Of Leeds; Reino UnidoFil: Sebastian Becker, Gabriel. Universidad de Hohenheim; AlemaniaFil: Beeckman, Hans. Royal Museum For Central Africa; BélgicaFil: Boanerges Souza, Danilo. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Cesar Botosso, Paulo. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Bowman, David M. J. S.. University of Tasmania; AustraliaFil: Bräuning, Achim. Universitat Erlangen-Nuremberg; AlemaniaFil: Brede, Benjamin. Wageningen University And Research Centre; Países BajosFil: Irving Brown, Foster. Universidade Federal Do Acre; BrasilFil: Julio Camarero, Jesus. Instituto Boliviano de Investigacion Forestal Bolivia; BoliviaFil: Camargo, Plnio Barbosa. Universidade de Sao Paulo; BrasilFil: Cardoso, Fernanda C.G.. Universidade Federal do Paraná; BrasilFil: Carvalho, Fabrcio Alvim. Universidade Federal de Juiz de Fora; BrasilFil: Castro, Wendeson. Universidade Federal Do Acre; BrasilFil: Koloski Chagas, Rubens. Universidade de Sao Paulo; BrasilFil: Chave, Jrome. Centre National de la Recherche Scientifique; FranciaFil: Chidumayo, Emmanuel N.. University Of Zambia; ZambiaFil: Clark, Deborah A.. University Of Missouri-st. Louis; Estados UnidosFil: Regina Capellotto Costa, Flavia. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Couralet, Camille. Royal Museum For Central Africa; BélgicaFil: Henrique Da Silva Mauricio, Paulo. Universidade Federal Do Acre; BrasilFil: Dalitz, Helmut. Universidad de Hohenheim; AlemaniaFil: Resende De Castro, Vinicius. Universidade Federal de Vicosa; BrasilFil: Milani, Jaanan Eloisa De Freitas. Universidade Federal do Paraná; BrasilFil: Roig Junent, Fidel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla; Argentin

Water Availability Is the Main Climate Driver of Neotropical Tree Growth

• Climate models for the coming century predict rainfall reduction in the Amazonian region, including change in water availability for tropical rainforests. Here, we test the extent to which climate variables related to water regime, temperature and irradiance shape the growth trajectories of neotropical trees. • We developed a diameter growth model explicitly designed to work with asynchronous climate and growth data. Growth trajectories of 205 individual trees from 54 neotropical species censused every 2 months over a 4-year period were used to rank 9 climate variables and find the best predictive model. • About 9% of the individual variation in tree growth was imputable to the seasonal variation of climate. Relative extractable water was the main predictor and alone explained more than 60% of the climate effect on tree growth, i.e. 5.4% of the individual variation in tree growth. Furthermore, the global annual tree growth was more dependent on the diameter increment at the onset of the rain season than on the duration of dry season. • The best predictive model included 3 climate variables: relative extractable water, minimum temperature and irradiance. The root mean squared error of prediction (0.035 mm.d–1) was slightly above the mean value of the growth (0.026 mm.d–1). • Amongst climate variables, we highlight the predominant role of water availability in determining seasonal variation in tree growth of neotropical forest trees and the need to include these relationships in forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest

Role of Pleiotropy in the Evolution of a Cryptic Developmental Variation in Caenorhabditis elegans

Using vulval phenotypes in Caenorhabditis elegans, the authors show that cryptic genetic variation can evolve through selection for pleiotropic effects that alter fitness, and identify a cryptic variant that has conferred enhanced fitness on domesticated worms under laboratory conditions

Increased GABAA Receptor ε-Subunit Expression on Ventral Respiratory Column Neurons Protects Breathing during Pregnancy

GABAergic signaling is essential for proper respiratory function. Potentiation of this signaling with allosteric modulators such as anesthetics, barbiturates, and neurosteroids can lead to respiratory arrest. Paradoxically, pregnant animals continue to breathe normally despite nearly 100-fold increases in circulating neurosteroids. ε subunit-containing GABAARs are insensitive to positive allosteric modulation, thus we hypothesized that pregnant rats increase ε subunit-containing GABAAR expression on brainstem neurons of the ventral respiratory column (VRC). In vivo, pregnancy rendered respiratory motor output insensitive to otherwise lethal doses of pentobarbital, a barbiturate previously used to categorize the ε subunit. Using electrode array recordings in vitro, we demonstrated that putative respiratory neurons of the preBötzinger Complex (preBötC) were also rendered insensitive to the effects of pentobarbital during pregnancy, but unit activity in the VRC was rapidly inhibited by the GABAAR agonist, muscimol. VRC unit activity from virgin and post-partum females was potently inhibited by both pentobarbital and muscimol. Brainstem ε subunit mRNA and protein levels were increased in pregnant rats, and GABAAR ε subunit expression co-localized with a marker of rhythm generating neurons (neurokinin 1 receptors) in the preBötC. These data support the hypothesis that pregnancy renders respiratory motor output and respiratory neuron activity insensitive to barbiturates, most likely via increased ε subunit-containing GABAAR expression on respiratory rhythm-generating neurons. Increased ε subunit expression may be critical to preserve respiratory function (and life) despite increased neurosteroid levels during pregnancy

The response of tropical rainforests to drought : lessons from recent research and future prospects

Key message: we review the recent findings on the influence of drought on tree mortality, growth or ecosystem functioning in tropical rainforests. Drought plays a major role in shaping tropical rainforests and the response mechanisms are highly diverse and complex. The numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical rainforests on the three continents. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance. - Context: tropical rainforest ecosystems are characterized by high annual rainfall. Nevertheless, rainfall regularly fluctuates during the year and seasonal soil droughts do occur. Over the past decades, a number of extreme droughts have hit tropical rainforests, not only in Amazonia but also in Asia and Africa. The influence of drought events on tree mortality and growth or on ecosystem functioning (carbon and water fluxes) in tropical rainforest ecosystems has been studied intensively, but the response mechanisms are complex.- Aims: herein, we review the recent findings related to the response of tropical forest ecosystems to seasonal and extreme droughts and the current knowledge about the future of these ecosystems. - Results: this review emphasizes the progress made over recent years and the importance of the studies conducted under extreme drought conditions or in through-fall exclusion experiments in understanding the response of these ecosystems. It also points to the great diversity and complexity of the response of tropical rainforest ecosystems to drought. - Conclusion: the numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical forest regions. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance

Abstracts from the 20th International Symposium on Signal Transduction at the Blood-Brain Barriers

https://deepblue.lib.umich.edu/bitstream/2027.42/138963/1/12987_2017_Article_71.pd