Smart observation of management impacts on peatlands function (SmartBog)

Peatlands are an important ecosystem due to their role in carbon sequestration as well as other ecosystem services including; climate regulation and water regulation. Peatlands cover a small fraction (~3 %) of the terrestrial surface. Nevertheless, they account for approximately one-third of global Soil Organic Carbon stock. In Ireland, peatlands cover ~21% of the land area and account for between 50-75% of the total SOC stock. However, much of this area has been degraded through anthropogenic activities such as drainage and peat extraction. Therefore, there is a need to develop a system to identify management-related impacts on peatland function. The system will directly support rehabilitation and conservation activities, aiding identification of candidate sites for rewetting and restoration. Both high-resolution satellite data (Copernicus Sentinel-2) and very high-resolution aerial photography will be used. Peatlands will be delineated using the Derived Irish Peat map (DIPM2) in both datasets. Semi-automatic object-based image analysis and machine learning-based techniques will be used to extract the extent of drains on Irish peatlands. Furthermore, a multi-scale approach will be implemented to generate Normalized Difference Vegetation Index maps. NDVI will be generated from both in-situ and remote sensors (Sentinel-2/Aerial imagery). Overall, the outputs generated from these datasets (LULC, drainage and NDVI maps) will be integrated into a GIS framework. The main aim of this study is to assess the impact of anthropogenic management of peatlands, using GIS, Earth Observation and Machine Learning (ML)

Effects of Hepatitis C on Hematological Parameters in local areas of Mirpurkhas

Introduction: Various diseases have emerged as a major cause of morbidity and mortality in patients with human immunodeficiency virus (HIV) and hepatitis B virus (HCV) confection, now that antiretroviral therapies become more effective e and has prolonged life expectancy in HIV-infected patients1. One of the most frequently identified extra-hepatic abnormalities often seen at the time of diagnosis of HIV is the hematological abnormalityObjective: To determine possible effects of Hepatitis C in local Population of MirpurkhasMethodology:140 diagnosed patients of Hepatitis C were selected from OPD/Ward MMCH and Civil Hospital Mirpurkhas, Patients of Hypertension, Heart Failure, Renal diseases and respiratory disease were excluded Their ALT, GGT, Alk Sodium was determined by kit method. Their RBC count, TLC, Platelet Count was counted and ESR was determinedResults: It was a prospective study and out of 140 hepatitis C patients 86 were male and 54 were female. The mean age was 54.77 ± 14.046 years. The mean height was 159.42 ± 11.188 cm and the mean weight was 53.69±10.604 Kgs. The mean BMI was calculated as 21.235 ± 5.0607 kgs/m2 (Table.1). The mean Hemoglobin was estimated as 10.639± 2.6924 gm% the mean RBC count was found 3.832 ± .8460 millions/cmm. The mean total leukocyte count was 9111.63 ± 4612.845 per cmm and the mean Platelet count was 160447.67 ± 93788.194 /dl. the mean ESR was 51.70 ± 26.320 (Table.2) The mean Alkaline phosphatase was 273.76± 96.818 IU, the mean GGT was 83.40 ± 102.650 IU and the mean ALT was 74.98 ± 58.614 IU. (Table.3.) The Correlation of hepatitis C was estimated by Pearson’s correlation using SPSS 15 and found that hepatitis has a significant correlation with Hemoglobin, RBC count, TLC and Platelet count (r=.167, .165, .181, 238 and p=.092̽*, .031*, 018* and .002** respectively) and it has an inverse correlation with ESR (r=-.213, p=.005**)Conclusion: Our data shows that hepatitis C has positive correlation with Hb, RBC Count, TLC and Platelet count while the hepatitis has a significant inverse correlation with ESR. More work is required to establish criteria regarding correlation between Hepatitis C and Hematological parameter

Peatland land use dynamics

The dataset is part of the published scientific paper Habib W, Connolly J, A national-scale assessment of land use change in peatlands between 1989 and 2020 using Landsat data and Google Earth Engine—a case study of Ireland. Regional Environmental Change, 2023. The overall objective of this study is to understand peatland land use dynamics in Ireland for the periods 1989–1991, 2004-2006 and 2018–2020. The compressed file contains three GIS files (raster), each representing the aforementioned periods

Mapping artificial drains in peatlands—A national‐scale assessment of Irish raised bogs using sub‐meter aerial imagery and deep learning methods

International audiencePeatlands, constituting over half of terrestrial wetland ecosystems across the globe, hold critical ecological significance and are large stores of carbon (C). Irish oceanic raised bogs are a rare peatland ecosystem offering numerous ecosystem services, including C storage, biodiversity support and water regulation. However, they have been degraded over the centuries due to artificial drainage, followed by peat extraction, afforestation and agriculture. This has an overall negative impact on the functioning of peatlands, shifting them from a moderate C sink to a large C source. Recognizing the importance of these ecosystems, efforts are underway for conservation (rewetting and rehabilitation), while accurately accounting for C stock and greenhouse gas (GHG) emissions. However, the implementation of these efforts requires accurate identification and mapping of artificial drainage ditches. This study utilized very high-resolution (25 cm) aerial imagery, and a deep learning (U-Net) approach to map the visible artificial drainage (unobstructed by vegetation or infill) in raised bogs at a national scale. The results show that artificial drainage is widespread, with $ 20 000 km of drains mapped. The overall accuracy of the model was 80% on an independent testing dataset. The data were also used to derive the Frac ditch which was 0.03 (fraction of artificial drainage on industrial peat extraction sites). This is lower than IPCC Tier 1 Frac ditch and can aid in IPCC Tier 2 reporting for Ireland. This is the first study to map drains with diverse sizes and patterns on Irish-raised bogs using optical aerial imagery and deep learning methods. The map will serve as an important baseline dataset for evaluating the artificial drainage ditch conditions. It will prove useful for sustainable management, conservation and refined estimations of GHG emissions. The model's capacity for generalization implies its potential in mapping artificial drains in peatlands at a regional and global scale, thereby enhancing the comprehension of the global effects of artificial drainage ditches on peatlands

Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools

Abstract Wetlands are one of the major contributors of methane (CH4) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emissions at chamber or eddy covariance scales, but these are not often aggregated to the site/ecosystem scale. This study provides a robust approach to map dominant vegetation communities and to use these areas to upscale methane fluxes from chamber to site scale using a simple weighted-area approach. The proposed methodology was tested at three peatlands in Ireland over a duration of 2 years. The annual vegetation maps showed an accuracy ranging from 83 to 99% for near-natural to degraded sites respectively. The upscaled fluxes were highest (2.25 and 3.80 gC m−2 y−1) at the near-natural site and the rehabilitation (0.17 and 0.31 gC m−2 y−1), degraded (0.15 and 0.27 gC m−2 y−1) site emissions were close to net-zero throughout the study duration. Overall, the easy to implement methodology proposed in this study can be applied across various landuse types to assess the impact of peatland rehabilitation on methane emissions by mapping ecological change

Mapping and monitoring peatland conditions from global to field scale

Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity

Mapping and monitoring peatland conditions from global to field scale

Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity