Leafless roughness of complex tree morphology using terrestrial LiDAR

Strategies for extracting roughness parameters from riparian forests need to address the issue that the trees are more than just stems and that in large rivers flow can rise into the canopy. Remote sensing information with 3-D capabilities such as lidar can be used to extract information on trees. However, first and last pulse airborne lidar data are insufficient to characterize the complex vertical structure of vegetation because by definition, there are few data at intermediate levels. Terrestrial laser scanning (TLS) is used in this study to define complex structures at a millimetric scanning resolution for the purpose of extracting canopy parameters relevant for the parameterization of the flow resistance equations. We will mainly be concerned with the projected area of leafless trees, estimating the total tree dimensions using several different methods. These include manipulating mass point cloud data obtained from TLS to create stage-dependent projected areas through complex meshing techniques and voxelization. Stage-dependent projected areas were defined for natural and planted poplar forests in the riparian zone of the Garonne and Allier rivers in southern and central France, respectively. Roughness values for planted poplar forests dominant in many western European river floodplains range from Manning's n = 0.037–0.094 and n = 0.140–0.330 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively. Roughness values for natural poplar forests ranged from n = 0.066–0.210 and n = 0.202–0.720 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively

Imaging spectrometry-derived estimates of regional ecosystem composition for the Sierra Nevada, California

The composition of the plant canopy is a key attribute of terrestrial ecosystems, influencing the fluxes of carbon, water, and energy between the land surface and the atmosphere. Terrestrial ecosystem and biosphere models, which are used to predict how ecosystems are expected to respond to changes in climate, atmospheric CO2, and land-use change, require accurate representations of plant canopy composition at large spatial scales. The ability to accurately specify plant canopy composition is important because it determines the physiological and ecological properties of plants (such as leaf photosynthetic capacity, patterns of plant carbon allocation and tissue turnover, and the resulting dynamics of plant demography) that govern the biophysical and biogeochemical functioning of ecosystems. Traditionally, plant canopy composition has been represented in a coarse-grained manner within terrestrial biosphere models, with ecosystems being comprised of a single plant functional type (PFT). However, models are increasingly seeking to represent fine-scale spatial variation in plant functional diversity. In this study, we show how imaging spectrometry measurements can provide spatially-comprehensive estimates of within-biome heterogeneity in PFT composition across a functionally diverse and topographically heterogeneous ~710 km2 area in the Southern Sierra Mountains of California. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data at 18 m resolution from the recent HyspIRI Preparatory Mission (Hyperspectral InfraRed Imager) were used to estimate the sub-pixel fractions of seven PFTs represented in the ED2 terrestrial biosphere model: Shrub, Oak, Western Hardwood, Western Pine, Cedar/Fir, and High-elevation Pine, plus a Grass/NPV (Non-Photosynthetic Vegetation) fraction using Multiple Endmember Spectral Mixture Analysis (MESMA). ED2 is an individual-based terrestrial biosphere model capable of representing fine-scale sub-pixel ecosystem heterogeneity. Our results show that this methodology captures important elevation-related shifts in canopy composition that occur within the study area that are not resolved by existing multi-spectral land-cover products. These estimates modestly improved when the putative PFT endmembers considered in the mixture analysis were constrained using available geospatial data about the presence and absence of the PFTs in particular areas: the average RMSEs (root-mean-square errors) with the geospatially-constrained versus conventional method were 11.3% and 11.9% respectively, with larger reductions in the bias (i.e. mean error) in the abundances of Oak, Cedar/Fir, and Western Hardwood PFTs (ranging from 2.0% to 7.8%). At the hectare scale around four flux towers in the Southern Sierra Mountains, the overall composition improved from an RMSE of 18.2% (5.0-24.2% for individual PFTs) to RMSE 9.5% (3.3-13.2% for individual PFTs). Downgrading AVIRIS to 30 m resolution resulted in a reduction in accuracy of the constrained method to an RMSE of 12.7% (0-23.7%) with < 1% change in bias for all tree and shrub PFTs. Our results demonstrate that imaging spectrometry measurements from planned satellite missions such as HyspIRI, EnMAP (Environmental Mapping and Analysis Program), and HISUI (Hyper-spectral Imager SUIte) can provide important and much-needed information about fine-scale heterogeneity in the composition of plant canopies for constraining and improving terrestrial ecosystem and biosphere model simulations of regional- and global-scale vegetation dynamics and function

Prostate cancer: AR aberrations and resistance to abiraterone or enzalutamide

Resistance to abiraterone or enzalutamide is a major medical burden the duration of benefit is highly variable and cross-resistance often occurs when these two agents are given sequentially. Blood-based analysis of androgen receptor splice variants and AR copy number gain or mutations could enhance understanding of the mechanisms of resistance and improve management of patients with castration-resistant prostate cancer

Opening the black box of outer space: the case of Jason-3

If you look at a rendering of planet Earth from a bird's eye view, you will see satellites orbiting the planet like electrons, each one a testament to humanity's expansion beyond Earth's atmosphere. It begs the question: what is this new humanized landscape? The dominant voice that has attempted to answer this question is the realist one, which has led the charge of academic inquiry into outer space since the fateful launch of the Sputnik in 1957. Though enlightening in some respects, the realist perspective oftentimes obscures the heterogeneous complexity of the actors, actions, limits and possibilities that have constructed this very humanized outer space. This paper looks at the humanization of outer space through the lens of JASON-3, an internationally collaborative satellite designed primarily to measure the topography of the Earth's oceans. A vast number of actors collaborated to enact the network that created JASON-3, including bureaucratic agencies, academics, private contractors, political bodies, other satellites, the sun and even gravity. This paper will focus on these actors and the work that they did to form the network, showing a glimpse of the entangled connections that eventually produced JASON-3. Through telling this story, I argue: (1) outer space is more complex than state-level relations and (2) critical geography -- with its insight into relational spaces and deconstructing power structures -- has a unique place to fill in outer space literature

Livelihood impacts of forest carbon protection in the context of Redd+ in Cross River State, Southeast Nigeria

The rate of landcover change linked to deforestation and forest degradation in tropical environments has continued to surge despite a series of forest governance policy instruments over the years. These informed the launch of one of the most important international policies called Reducing Emission from Deforestation and Forest Degradation Plus (REDD+) to combat forest destruction. REDD+ assumes that communities will have increased assets to natural capital which will enhance their livelihood portfolio and mitigate the effects of climate variability and change across biomes. The aim of this study is to ascertain the livelihoods impacts of forest carbon protection within the context of REDD+ in Cross River State, Nigeria. Six forest communities were chosen across three agroecological zones of the State. Anchored on the Sustainable Livelihood Framework, a set of questionnaires were administered to randomly picked households. The results indicate that more than half of the respondents aligned with financial payment and more natural resources as the perceived benefits of carbon protection. More so, a multinomial logistic regression showed that income was the main factor that influenced respondent’s support for forest carbon protection. Analysis of income trends from the ‘big seven’ non-timber forest resources in the region showed increase in Gnetum africanum, Bushmeat, Irvingia gabonensis, Garcinia kola, while carpolobia spp., Randia and rattan cane revealed declining income since inception of REDD+. The recorded increase in household income was attributed to a ban in logging. It is recommended that the forest communities should be more heavily involved in the subsequent phases of the project implementation to avoid carbon leakages

Determining leaf area index and leafy tree roughness using terrestrial laser scanning

Vegetation roughness, and more specifically forest roughness, is a necessary component in better defining flood dynamics both in the sense of changes in river catchment characteristics and the dynamics of forest changes and management. Extracting roughness parameters from riparian forests can be a complicated process involving different components for different required scales and flow depths. For flow depths that enter a forest canopy, roughness at both the woody branch and foliage level is necessary. This study attempts to extract roughness for this leafy component using a relatively new remote sensing technique in the form of terrestrial laser scanning. Terrestrial laser scanning is used in this study due to its ability to obtain millions of points within relatively small forest stands. This form of lidar can be used to determine the gaps present in foliaged canopies in order to determine the leaf area index. The leaf area index can then be directly input into resistance equations to determine the flow resistance at different flow depths. Leaf area indices created using ground scanning are compared in this study to indices calculated using simple regression equations. The dominant riparian forests investigated in this study are planted and natural poplar forests over a lowland section of the Garonne River in Southern France. Final foliage roughness values were added to woody branch roughness from a previous study, resulting in total planted riparian forest roughness values of around Manning's n = 0.170–0.195 and around n = 0.245–330 for in-canopy flow of 6 and 8 m, respectively, and around n = 0.590 and around n = 0.750 for a natural forest stand at the same flow depths

A Comparative Study between Two Regression Methods on LiDAR Data: A Case Study

Airborne LiDAR (Light Detection and Ranging) has become an excellent tool for accurately assessing vegetation characteristics in forest environments. Previous studies showed empirical relationships between LiDAR and field-measured biophysical variables. Multiple linear regression (MLR) with stepwise feature selection is the most common method for building estimation models. Although this technique has provided very interesting results, many other data mining techniques may be applied. The overall goal of this study is to compare different methodologies for assessing biomass fractions at stand level using airborne Li- DAR data in forest settings. In order to choose the best methodology, a comparison between two different feature selection techniques (stepwise selection vs. genetic-based selection) is presented. In addition, classical MLR is also compared with regression trees (M5P). The results when each methodology is applied to estimate stand biomass fractions from an area of northern Spain show that genetically-selected M5P obtains the best results

Quantification of above-ground biomass over the cross-river state, Nigeria, using sentinel-2 data

Higher-resolution wall-to-wall carbon monitoring in tropical Africa across a range of woodland types is necessary in reducing uncertainty in the global carbon budget and improving accounting for Reducing Emissions from Deforestation and forest Degradation Plus (REDD+). This study uses Sentinel-2 multispectral imagery combined with climatic and edaphic variables to estimate the regional distribution of aboveground biomass (AGB) for the year 2020 over the Cross River State, a tropical forest region in Nigeria, using random forest (RF) machine learning. Forest inventory plots were collected over the whole state for training and testing of the RF algorithm, and spread over undisturbed and disturbed tropical forests, and woodlands in croplands and plantations. The maximum AGB plot was estimated to be 588 t/ha with an average of 121.98 t/ha across the entire Cross River State. AGB estimated using random forest yielded an R2 of 0.88, RMSE of 40.9 t/ha, a relRMSE of 30%, bias of +7.5 t/ha and a total woody regional AGB of 0.246 Pg for the Cross River State. These results compare favorably to previous tropical AGB products; with total AGB of 0.290, 0.253, 0.330 and 0.124 Pg, relRMSE of 49.69, 57.09, 24.06 and 56.24% and −41, −48, −17 and −50 t/ha bias over the Cross River State for the Saatchi, Baccini, Avitabile and ESA CCI maps, respectively. These are all compared to the current REDD+ estimate of total AGB over the Cross River State of 0.268 Pg. This study shows that obtaining independent reference plot datasets, from a variety of woodland cover types, can reduce uncertainties in local to regional AGB estimation compared with those products which have limited tropical African and Nigerian woodland reference plots. Though REDD+ biomass in the region is relatively larger than the estimates of this study, REDD+ provided only regional biomass rather than pixel-based biomass and used estimated tree height rather than the actual tree height measurement in the field. These may cast doubt on the accuracy of the estimated biomass by REDD+. These give the biomass map of this current study a comparative advantage over others. The 20 m wall-to-wall biomass map of this study could be used as a baseline for REDD+ monitoring, evaluation, and reporting for equitable distribution of payment for carbon protection benefits and its management