20 research outputs found
Modelling seagrass blue carbon stock in seagrass-mangrove habitats using remote sensing approach
Modelling seagrass blue carbon stocks are essential to complement the satellitebased remote sensing in detecting the underground seagrass carbon stocks. The green carbon initiatives have for long reported the detailed mapping and estimation procedural as well as the audit protocol of the global terrestrial carbon stocks. Research on the blue carbon mapping and its related modelling and estimation, on the other hand, is rarely if ever published as part of its importance is realised but remained scattered. Therefore, this study aimed at investigating blue carbon stocks in seagrass habitats by estimating the total carbon stored in seagrass using the satellite-based technique. The specific objectives are to : 1) assess and adapt some selected models for deriving seagrass total above-ground carbon (STAGC); 2) formulate new approach based-on selected models to combine with in-situ data, to model and estimate blue carbon stocks from seagrass total below-ground carbon (STBGC); 3) develop a novel technique using the selected models with soil organic carbon (SOC) to model and estimate the blue carbon stocks from seagrass total soil organic carbon (STSOC); and 4) integrate all the models (STAGC, STBGC, and STSOC) to produce a framework for the mapping and estimation of seagrass total blue carbon stock (STBCS). Suitable logistic functions were selected and applied on the satellite images to investigate seagrass, and soil carbon stocks along the seagrass meadows of Peninsular Malaysia (PM) coastline All the Landsat ETM+’s shortwave visible bands (blue, green, red) were employed for detecting and mapping seagrass stocks boundary within the coastline of PM. The derivation of STAGC was adopted from the existing bottom reflectance index (BRI) based technique via establishing a strong relationship between BRI with seagrass total aboveground biomass (STAGB). While for STBGC estimation, the STAGB^ (STAGB obtained from BRI image) were correlated with seagrass total below-ground biomass derived from insitu measurement (STBGB^^ro). Both these STAGB^ and STBGB^.^ro were converted into STAGC and STBGC using a conversion factor. Furthermore, the derivation of seagrass total soil organic carbon derived via laboratory test (STSOCi^b) was achieved through correlating BRI values with corresponding in-situ samples of soil organic carbon (SOC) obtained from the laboratory analysis by the Carbon-Hydrogen Nitrogen Sulphur (CHNS) analyser. These models were generated from the three major sample areas (Johor, Penang, and Terengganu), which were used to estimate the entire seagrass carbon stocks in the coastline of PM. The models revealed a robust correlation results for BRI versus STAGB (R2 = 0.962, p< 0.001), STAGB^, versus STBGB/A,wro (R2 = 0.933, p< 0.001,), and BRI and STSOC (R2 = 0 .989, p< 0.001) respectively. The STBCS for the whole seagrass meadows along the coastline of PM was finally realised, demonstrating a good agreement in accuracy assessment (Root Mean Square Error (RMSE) = +- <1MtC/ha\). It is, therefore, concluded that the new approach introduced by this research on STBGC and STSOC estimation was tested and proved significant on the entire STBCS quantification for the PM coastline. The contributions are critical to fast-track the United Nations Framework Convention on Climate Change (UNFCCC) agreement to report the STBCS contents. Hence, this study has managed to propose a new fundamental initiative for estimating STBCS for speedy realisation of 2020 agenda on targets 14.2 and 14.5 of United Nations’ Sustainable Development Goal 14th (life below the water)
Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties
Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil).
We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ± 0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns
Assessment and Remediation of Soils Contaminated by Potentially Toxic Elements (PTE)
Many soils worldwide are contaminated with potentially toxic elements (PTEs). These elements can be taken up by plant roots and accumulate in plants’ organs, thus becoming a danger for the health of humans and animals. Therefore, it is still essential and urgent to understand the behavior of such contaminants in soil and find sustainable approaches to reduce the risk posed by their presence in soil systems. This volume contains ten original research articles. Four articles deal with the assessment of bioavailability of PTEs in contaminated soils, three articles report results on the application of phytoremediation to PTEs contaminated soils, one paper is related to the source–sink relationships of PTEs at basin scale, and two manuscripts address the issue of PTE contamination in urban soils
Proceedings of the European Conference on Agricultural Engineering AgEng2021
This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora,
Portugal, from 4 to 8 July 2021.
This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference.
Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and
management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application
technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings
Applying satellite remote sensing, unmanned systems, and models for water quality analysis
Transport of soils and sediment by runoff diminish the surface water quality of lakes/reservoirs and shrink reservoir capacity. Nutrients move with transported soil and sediment due to soil erosion by floods and high wind. Nutrients from land management practices such as farming, animal feeding operations, and wastewater treatment stimulate the growth of cyanobacteria in surface water bodies. Excessive nutrient loading or eutrophication with favorable hydrodynamics result in excessive growth of cyanobacteria called Harmful Algal Blooms (HABs). These cyanobacteria sometimes release microcystins which are toxic to humans when consumed. The biological decay of HABs cause ecological dead zones due to depletion of dissolved oxygen while reducing the utility of the reservoirs. From an ecosystem service provision and human health perspective, HABs represent a great water quality threat.Therefore monitoring and modeling the formation of HABs in surface water bodies is important. In freshwater reservoirs of Oklahoma and Kansas, HAB formations and occurrences have increased due to floods, agricultural practices, and rising earth temperatures due to global warming and climate change. In Oklahoma, Grand Lake O' the Cherokees (Grand Lake) had experienced a severe HAB outbreak in 2011. In Kansas, Marion reservoir experiences HAB formation almost every year. These two reservoirs provide an opportunity to study and understand HAB formation, predict them and provide warning advisories. All the in-situ data collected using unmanned systems in the freshwater reservoirs have been used to develop water quality and watershed models to understand the relationship between sediment, nutrient loadings and HAB formations