Miracle-dz/UniFAR-FTUs huggingface dataset:
Tropical Peatland Vegetation Structure and Biomass: Optimal Exploitation of Airborne Laser Scanning

Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and Ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R2 = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed.
Spatio-Temporal Variability of the Habitat Suitability Index for Chub Mackerel (Scomber Japonicus) in the East/Japan Sea and the South Sea of South Korea

The climate-induced decrease in fish catches in South Korea has been a big concern over the last decades. The increase in sea surface temperature (SST) due to climate change has led to not only a decline in fishery landings but also a shift in the fishing grounds of several fish species. The habitat suitability index (HSI), a reliable indicator of the capacity of a habitant to support selected species, has been widely used to detect and forecast fishing ground formation. In this study, the catch data of the chub mackerel and satellite-derived environmental factors were used to calculate the HSI for the chub mackerel in the South Sea, South Korea. More than 80% of the total catch was found in areas with an SST of 14.72–25.72 °C, chlorophyll-a of 0.30–0.92 mg m−3, and primary production of 523.7–806.46 mg C m−2 d−1. Based on these results, the estimated climatological monthly HSI from 2002 to 2016 clearly showed that the wintering ground of the chub mackerel generally formed in the South Sea of South Korea, coinciding with the catch distribution during the same period. This outcome implies that our estimated HSI can yield a reliable prediction of the fishing ground for the chub mackerel in the East/Japan Sea and South Sea of South Korea.
Estimation of Water Level Changes of Large-Scale Amazon Wetlands Using ALOS2 ScanSAR Differential Interferometry

Differential synthetic aperture radar (SAR) interferometry (DInSAR) has been successfully used to estimate water level changes (∂h/∂t) over wetlands and floodplains. Specifically, amongst ALOS PALSAR datasets, the fine-beam stripmap mode has been mostly implemented to estimate ∂h/∂t due to its availability of multitemporal images. However, the fine-beam observation mode provides limited swath coverage to study large floodplains and wetlands, such as the Amazon floodplains. Therefore, for the first time, this paper demonstrates that ALOS2 ScanSAR data can be used to estimate the large-scale ∂h/∂t in Amazon floodplains. The basic procedures and challenges of DInSAR processing with ALOS2 ScanSAR data are addressed and final ∂h/∂t maps are generated based on the Satellite with ARgos and ALtiKa (SARAL) altimetry’s reference data. This study reveals that the local ∂h/∂t patterns of Amazon floodplains are spatially complex with highly interconnected floodplain channels, but the large-scale (with 350 km swath) ∂h/∂t patterns are simply characterized by river water flow directions.
Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat

Detection of senescence’s dynamics in crop breeding is time consuming and needs considerable details regarding its rate of progression and intensity. Normalized difference red-edge index (NDREI) along with four other spectral vegetative indices (SVIs) derived from unmanned aerial vehicle (UAV) based spatial imagery, were evaluated for rapid and accurate prediction of senescence. For this, 32 selected winter wheat genotypes were planted under full and limited irrigation treatments. Significant variations for all five SVIs: green normalize difference vegetation index (GNDVI), simple ratio (SR), green chlorophyll index (GCI), red-edge chlorophyll index (RECI), and normalized difference red-edge index (NDREI) among genotypes and between treatments, were observed from heading to late grain filling stages. The SVIs showed strong relationship (R2 = 0.69 to 0.78) with handheld measurements of chlorophyll and leaf area index (LAI), while negatively correlated (R2 = 0.75 to 0.77) with canopy temperature (CT) across the treatments. NDREI as a new SVI showed higher correlations with ground data under both treatments, similarly as exhibited by other four SVIs. There were medium to strong correlations (r = 0.23–0.63) among SVIs, thousand grain weight (TGW) and grain yield (GY) under both treatments. Senescence rate was calculated by decreasing values of SVIs from their peak values at heading stage, while variance for senescence rate among genotypes and between treatments could be explained by SVIs variations. Under limited irrigation, 10% to 15% higher senescence rate was detected as compared with full irrigation. Principle component analysis corroborated the negative association of high senescence rate with TGW and GY. Some genotypes, such as Beijing 0045, Nongda 5181, and Zhongmai 175, were selected with low senescence rate, stable TGW and GY in both full and limited irrigation treatments, nearly in accordance with the actual performance of these cultivars in field. Thus, SVIs derived from UAV appeared as a promising tool for rapid and precise estimation of senescence rate at maturation stages.
Multitemporal Cloud Masking in the Google Earth Engine

The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these requirements. The proposed methodology is tested for the Landsat-8 mission over a large collection of manually labeled cloud masks from the Biome dataset. The quantitative results show state-of-the-art performance compared with mono-temporal standard approaches, such as FMask and ACCA algorithms, yielding improvements between 4–5% in classification accuracy and 3–10% in commission errors. The algorithm implementation within the Google Earth Engine and the generated cloud masks for all test images are released for interested readers.
Assessment of Forest Above-Ground Biomass Estimation from PolInSAR in the Presence of Temporal Decorrelation

In forestry studies, remote sensing has been widely used to monitor deforestation and estimate biomass, and it has contributed to forest carbon stock management. A major problem when estimating biomass from optical and SAR remote sensing images is the saturation effect. As a solution, PolInSAR offers a high coverage height map that can be transformed into a biomass map. Temporal decorrelation may affect the accuracy of PolInSAR and may also have an effect on the accuracy of the biomass estimates. In this study, we compared three different height estimation models: the Random-Volume-over-Ground (RVoG), Random-Motion-over-Ground (RMoG), and Random-Motion-over-Ground-Legendre (RMoG L ) models. The RVoG model does not take into account the temporal decorrelation, while the other two compensate for temporal decorrelation but differ in structure function. The comparison was done on 214 field plots of the 10 m radius of the BioSAR2010 campaign. Different models relating PolInSAR height and biomass were developed by using polynomial, exponential, power series, and piece-wise linear regression. Different strategies for training and test subset selection were followed to obtain the best possible regression models. The study showed that the RMoG L model provided the most accurate biomass predictions. The relation between RMoG L height and biomass is well expressed by the exponential model with an average RMSE equal to 48 ton ha − 1 and R 2 value equal to 0.62. The relative errors for estimated biomass were equal to 46% for the RVoG model, to 37% for the RMoG, and to 30% for the RMoG L model. We concluded that taking the temporal decorrelation into account for estimating tree height has a significant effect on providing accurate biomass estimates.
Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. Based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pleiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images.
Assessment of Irrigation Performance in Large River Basins under Data Scarce Environment - A Case of Kabul River Basin, Afghanistan

The Kabul River basin (KRB) of Afghanistan, a lifeline of around 10 million people, has multiplicity of governance, management, and development-related challenges leading to inequity, inadequacy, and unreliability of irrigation water distribution. Prior to any uplifting intervention, there is a need to evaluate the performance of irrigation system on a long term basis to identify the existing bottlenecks. Although there are several indicators available for the performance evaluation of the irrigation schemes, we used the coefficient of variation (CV) of actual evapotranspiration (ETa) in space (basin, sub-basin, and provincial level), relative evapotranspiration (RET), and temporal CV of RET, to assess the equity, adequacy, and reliability of water distribution, respectively, from 2003 to 2013. The ETa was estimated through a surface energy balance system (SEBS) algorithm and the ETa estimates were validated using the advection aridity (AA) method with a R2 value of 0.81 and 0.77 at Nawabad and Sultanpur stations, respectively. The global land data assimilation system (GLDAS) and moderate-resolution imaging spectroradiometer (MODIS) products were used as main inputs to the SEBS. Results show that the mean seasonal sub-based RET values during summer (May–September) (0.37 ± 0.06) and winter (October–April) (0.40 ± 0.08) are below the target values (RET ≥ 0.75) during 2003–2013. The CV of the mean ETa, within sub-basins and provinces for the entire study period, has an equitable distribution of water from October–January (0.09 ± 0.04), whereas the highest inequity (0.24 ± 0.08) in water distribution is during early summer. The range of the CV of the mean ETa (0.04–0.06) on a monthly and seasonal basis shows the unreliability of water supplies in several provinces or sub-basins. The analysis of the temporal CV of mean RET highlights the unreliable water supplies across the entire basin. The maximum ETa during the study period was estimated for the Shamal sub-basin (552 ± 43 mm) while among the provinces, Kunar experienced the highest ETa (544 ± 39 mm). This study highlights the dire need for interventions to improve the irrigation performance in time and space. The proposed methodology can be used as a framework for monitoring and implementing water distribution plans in future.
A Conservative Downscaling of Satellite-Detected Chemical Compositions: NO2 Column Densities of OMI, GOME-2, and CMAQ

A conservative downscaling technique was applied when comparing nitrogen dioxide (NO2) column densities from space-borne observations and a fine-scale regional model. The conservative downscaling was designed to enhance the spatial resolution of satellite measurements by applying the fine-scale spatial structure from the model, with strict mass conservation at each satellite footprint pixel level. With the downscaling approach, NO2 column densities from the Ozone Monitoring Instrument (OMI; 13 × 24 km nadir footprint resolution) and the Global Ozone Monitoring Experiment-2 (GOME-2; 40 × 80 km) show excellent agreement with the Community Multiscale Air Quality (CMAQ; 4 × 4 km) NO2 column densities, with R = 0.96 for OMI and R = 0.97 for GOME-2. We further introduce an approach to reconstruct surface NO2 concentrations by combining satellite column densities and simulated surface-to-column ratios from the model. Compared with the Environmental Protection Agency’s (EPA) Air Quality System (AQS) surface observations, the reconstructed surface concentrations show a good agreement; R = 0.86 for both OMI and GOME-2. This study demonstrates that the conservative downscaling approach is a useful tool to compare coarse-scale satellites with fine-scale models or observations in urban areas for air quality and emissions studies. The reconstructed fine-scale surface concentration field could be used for future epidemiology and urbanization studies.
Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Continuous-based predictors of habitat characteristics derived from satellite imagery are increasingly used in species distribution models (SDM). This is especially the case of Normalized Difference Vegetation Index (NDVI) which provides estimates of vegetation productivity and heterogeneity. However, when NDVI predictors are incorporated into SDM, synchrony between biological observations and image acquisition must be questionned. Due to seasonal variations of NDVI during the year, landscape patterns of habitats are revealed differently from one date to another leading to variations in models’ performance. In this paper, we investigated the influence of acquisition time period of NDVI to explain and predict bird community patterns over France. We examined if the NDVI acquisition period that best fit the bird data depends on the dominant land cover context. We also compared models based on single time period of NDVI with one model built from the Dynamic Habitat Index (DHI) components which summarize variations in vegetation phenology throughout the year from the fraction of radiation absorbed by the canopy (fPAR). Bird species richness was calculated as response variable for 759 plots of 4 km2 from the French Breeding Bird Survey. Bird specialists and generalists to habitat were considered. NDVI and DHI predictors were both derived from MODIS products. For NDVI, five time periods in 2010 were compared, from late winter to begin of autumn. A climate predictor was also used and Generalized Additive Models were fitted to explain and predict bird species richness. Results showed that NDVI-based proxies of dominant habitat identity and spatial heterogeneity explain more bird community patterns than DHI-based proxies of annual productivity and seasonnality. We also found that models’ performance was both time and context-dependent, varying according to the bird groups. In general, best time period of NDVI did not match with the acquisition period of bird data because in case of synchrony, differences in habitats are less pronounced. These findings suggest that the most powerful approach to estimate bird community patterns is the simplest one. It only requires NDVI predictors from a single appropriate time period, in addition to climate, which makes the approach very operational.
Dendrochronology of Seaside Juniper (Juniperus maritima)

iv Acknowledgements . v List of Tables and Figures. viii Literature Review.1 Juniperus maritima . 1 Paleoecological History . 3 The Genus Juniperus . 5 Limiting Factors and Future Threats to Seaside Juniper . 7 Necessity for Revision of Prior Species Misidentifications in Literature . 8 Dendrochronology Performed on the Genus Juniperus . 9 Disturbance Regime Disruption in Western Juniper . 11 Dendrochronology of the Pacific Northwest . 13 Conclusion . 15 Study: Dendrochronology of Seaside Juniper.17 Introduction . 17 Study Species . 18 Study Sites . 19 Methods.21 Climate Envelope Model . 21 Site Selection . 22 Field methods . 23 Laboratory methods . 23 vii Statistical Analysis . 24 Results . 25 Climate Envelope Model . 25 Dendrochronology . 25 Discussion . 27 Future Research . 34 Conclusion . 36 Works Cited . 38 Tables and Figures . 46 Appendix A: Known presence locations of Seaside Juniper . 64
Numerical analysis of the food web of an intertidal mudflat ecosystem on the Atlantic coast of France

Food web modelling is an ideal way to describe ecosystems, because it accounts for the totality of the relationships between its various components. One difficulty of such an approach, how- ever, lies in the lack of data and information about some ecological relationships, resulting in under- defined systems. Inverse analysis can serve to complete steady-state food webs where the number of direct flow measurements is insufficient relative to the actual number of flows. We applied this method to the intertidal mudflat ecosystem of Brouage (eastern Marennes-Oleron Bay, SW France) and estimated the annual average carbon flows between the compartments of a coupled benthic and pelagic trophic food web from primary producers (microphytobenthos and phytoplankton) to top predators (fish and birds). The resulting network was very sensitive to the primary production of the microphytobenthos which was the most important flow in the system. Sensitivity analyses demon- strated the need for additional data on the nekton, pelagic protozoa, and bacterial compartments. The resulting network showed high bacterial activity, but indices resulting from network analysis showed low cycling in comparison with other ecosystems. The meiofauna had a small biomass, but consti- tuted a very active compartment compared to the macrofauna. Bird production was limited by macro- faunal production. Macrofaunal production reached the maximum allowed by the analysis. The inter- tidal mudflat ecosystem at Brouage is dominated by benthic production (including benthic primary producers, secondary producers, and predators) with an input of phytoplankton primary production.
DECENTRALIZATION, GROWTH AND OPTIMAL GOVERNMENT SIZE IN THE ITALIAN REGIONAL FRAMEWORK

The aim of this study is to empirically assess the existence of the BARS curve (Barro, Armey, Rahn, and Scully), as well as the relationship between public expenditure and decentralization, for Italian regions in the 1997-2009 period. Using panel data methodologies, we inspect the nexus between Regional government size (measured by the share of public expenditure on GDP) and the economic growth rate. The main results are twofold. First, when the degree of decentralization is reasonably low (below the 31% of expenditure decentralization), a BARS curve has been successfully discovered, and the optimal government size remains almost constant, assuming a value close to the 52%. The second one concerns the fact that, even though the optimal government size is almost constant, decentralization has a positive effect on economic activity. Finally, decentralization attenuates the negative impact of sub-optimal expenditure policy on growth process. Therefore, our results suggest that decentralization exerts a positive impact on public sector efficiency, contributing to stimulate the income growth.
A radio‐anatomical correlation study of the cisterna chyli

Surgical laparoscopic procedures in the retroperitoneal and supramesocolic spaces are increasingly frequent. There is a high risk of iatrogenic intraoperative injury of the retroperitoneal lymphatic structures during these procedures. A precise understanding of the anatomy of the thoracic duct (TD) and the cisterna chyli (CC) is essential for safe surgical procedures in this area. However, routine imaging procedures rarely and often incorrectly visualize the CC. The objective of this study was to evaluate the feasibility of a retrograde injection of the TD to fill the CC with a contrast agent in 16 human cadavers. Both magnetic resonance lymphography (MRI) and computed tomography (CT) studies could be performed on the same anatomical specimen, using a contrast medium which hardened, allowing gross dissection. MRI and CT detectability were evaluated, and imaging results were compared with the anatomical dissection. The CC of 12/16 cadavers were successfully injected, and four were unsuccessful due to technical difficulties, showing the effectiveness of the method. This technique can improve understanding of the anatomy of the TD and CC and provides an original option to study the complex anatomy of these structures by correlating precise cadaveric dissections with cross‐sectional imaging.