Design of a Remote-Controlled Platform for Green Roof Plants Monitoring via Hyperspectral Sensors

The combination of an appropriate design and careful management of green infrastructures may contribute to mitigate flooding (stormwater quantity) and pollutant discharges (stormwater quality) into receiving water bodies and to coping with other extreme climate impacts (such as temperature regime) on a long-term basis and water cycle variability. The vegetation health state ensures the green infrastructure’s effectiveness. Due to their remarkable spatial and spectral resolution, hyperspectral sensing devices appear to be the most suited for green infrastructure vegetation monitoring according to the peculiar spectral features that vegetation exhibits. In particular, vegetation health-state detection is feasible due to the modifications the typical vegetation spectral signature undergoes when abnormalities are present. This paper presents a ground spectroscopy monitoring survey of the green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. The spectroradiometer, placed on a fixed stand, was used to identify stress conditions of vegetation located in areas where drought could affect the plant health state. Broadband vegetation indices were employed for this purpose. For the test case presented, data acquired agreed well with direct observations on the ground. The analyses carried out showed the remarkable performances of the broadband indices Red Difference Vegetation Index (Red DVI), Simple Ratio (SR) and Triangular Vegetation Index (TVI) in highlighting the vegetation health state and encouraged the design of a remote-controlled platform for monitoring purposes.

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HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w2-169-2015 ◽

2015 ◽

Vol XL-3/W2 ◽

pp. 169-175 ◽

Cited By ~ 6

Author(s):

W. Pervez ◽

S. A. Khan ◽

Valiuddin

Keyword(s):

Machine Learning ◽

Vegetation Index ◽

Hyperspectral Image ◽

Atmospheric Correction ◽

Hyperspectral Data ◽

Supervised Machine Learning ◽

Spectral Signature ◽

Processing Application ◽

Significant Difference

Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

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Set storage to the rain – Experimental and model-based evidence in mitigating extreme rainfall excess with green roofs

10.5194/egusphere-egu2020-1608 ◽

2020 ◽

Author(s):

Kristian Förster ◽

Daniel Westerholt ◽

Lukas Bargel ◽

Philipp Kraft ◽

Gilbert Lösken

Keyword(s):

Natural Water ◽

Green Infrastructure ◽

Water Cycle ◽

Green Roof ◽

Extreme Rainfall ◽

Green Roofs ◽

Physical Experiment ◽

Water Levels ◽

Added Value ◽

Test Plot

Green infrastructure plays a key role in contemporary concepts to mitigate flooding in urban environments. Concepts like water sensitive cities, sponge cities, and water sensitive urban design aim to mimic features of the natural water cycle even in highly urbanized districts. For instance, green roofs &#8211; as a key element of green infrastructure &#8211; reduce runoff due to their storage capacity. Hence, evapotranspiration is also increased at the expense of runoff, which better matches the characteristics of the natural water cycle. In this presentation, we demonstrate the added value of green roofs for stormwater mitigation. First, a green roof test plot with a slope of zero degrees and dimensions of 20&#160;m in length and 1&#160;m in width is built under laboratory conditions. The vertical extent is 0.08&#160;m filled with a homogeneous substrate layer with a 300&#160;g&#160;m-2 drainage mat below. The runoff leaving the green roof at one of the 1 m edges is collected in tanks, which allows to continuously monitor the outflow. The water level in the green roof is observed using cameras. In this physical experiment, a sprinkler system is set up in order to generate an artificial rainfall event that mimics a design storm with a rainfall volume of 27 l m-2 in total falling within 15&#160;minutes. This corresponds to a return period of 100&#160;years at the experimental site in Hanover, Germany. A numerical model utilizing the open source Catchment Modelling Framework (CMF) is developed to represent the green roof in a physically based model representation, which solves the Darcy flow along a 1D numerical grid with a grid spacing of 0.2&#160;m. The model captures the dynamics of the green roof&#8217;s hydrological response very well. The comparison of observed and modelled runoff time series, each with a temporal resolution of 1 minute, suggest a Nash-Sutcliffe model efficiency of 0.64. The root mean square error (RMSE) of modelled water levels in the green roof amounts to 1.2&#160;cm. Both the physical experiment and the model suggest a runoff coefficient of 9% after 15&#160;minutes. At present, we also focus on analyzing other configurations of green roofs with altered dimensions and slope (50 experiments in total with up to three repetitions each). These results highlight that (i) CMF represents the hydrology of the green roof with high accuracy, and (ii) green roofs are a very efficient measure of green infrastructure that helps to reduce runoff even for design storms well beyond return periods usually considered in urban drainage planning. This is especially relevant in the process of transforming grey to green infrastructure in the light of climate change adaptation.&#160;

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Hyperspectral Monitoring of Green Roof Vegetation Health State in Sub-Mediterranean Climate: Preliminary Results

Sensors ◽

10.3390/s17040662 ◽

2017 ◽

Vol 17 (4) ◽

pp. 662 ◽

Cited By ~ 7

Author(s):

Patrizia Piro ◽

Michele Porti ◽

Simone Veltri ◽

Emanuela Lupo ◽

Monica Moroni

Keyword(s):

Mediterranean Climate ◽

Green Roof ◽

Health State ◽

Preliminary Results ◽

Vegetation Health

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Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

Sensors ◽

10.3390/s21051869 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1869

Author(s):

Yangyang Zhang ◽

Jian Yang ◽

Lin Du

Keyword(s):

Satellite Data ◽

Vegetation Index ◽

Hyperspectral Data ◽

Quantitative Detection ◽

Inversion Method ◽

Vegetation Monitoring ◽

Area Index ◽

Band Combination ◽

Band Combinations ◽

Sentinel 2

Leaf area index (LAI) is a key biophysical variable to characterize vegetation canopy. Accurate and quantitative LAI estimation is significant for monitoring vegetation growth status. ZhuHai-1 (ZH-1), which is a commercial remote sensing micro-nano satellite, provides a possibility for quantitative detection of vegetation with high spatial and spectral resolution. However, the band characteristics of ZH-1 are closely related to the accuracy of vegetation monitoring. In this study, a simulation dataset containing 32 bands of ZH-1 was generated by using the PROSAIL model, which was used to analyze the performance of 32 bands for LAI estimation by using the hybrid inversion method. Meanwhile, the effect of different band combinations on LAI estimation was discussed based on sensitivity analysis and the correlation between bands. Then, the optimal band combination from ZH-1 hyperspectral satellite data for LAI estimation was obtained. LAI estimation was performed based on the selected optimal band combination of ZH-1 satellite images in Xiantao city, Hubei province, and compared with the Sentinel-2 normalized difference vegetation index (NDVI) values and LAI product. The results demonstrated that the obtained LAI map based on the optimal band combination of ZH-1 was generally consistent with the overall distribution of Sentinel-2 NDVI and the LAI product, but had a moderate correlation with Sentinel-2 LAI (R = 0.60), which may not favorably indicate the validity of indirect validation. However, the method of this study on the analysis of hyperspectral data bands has application potential to provide a reference for selecting appropriate bands of hyperspectral satellite data to estimate LAI and improve the application of hyperspectral data such as ZH-1 in vegetation monitoring.

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Environmental Product Declarations for plants and soils: how to quantify carbon uptake in landscape design and construction?

The International Journal of Life Cycle Assessment ◽

10.1007/s11367-021-01926-w ◽

2021 ◽

Author(s):

Matti Kuittinen ◽

Ranja Hautamäki ◽

Eeva-Maria Tuhkanen ◽

Anu Riikonen ◽

Mari Ariluoma

Keyword(s):

Life Cycle ◽

Green Infrastructure ◽

Landscape Design ◽

Climate Impacts ◽

Mitigation Potential ◽

Environmental Product Declarations ◽

Construction Products ◽

Further Development ◽

Change Mitigation ◽

Design And Construction

Abstract Purpose Currently, no clear guidance exists for ISO and EN standards of calculating, verifying, and reporting the climate impacts of plants, mulches, and soils used in landscape design and construction. In order to optimise the potential of ecosystem services in the mitigation of greenhouse gas emissions in the built environment, we unequivocally propose their inclusion when assessing sustainability. Methods We analysed the life cycle phases of plants, soils, and mulches from the viewpoint of compiling standard-based Environmental Product Declarations. In comparison to other construction products, the differences of both mass and carbon flows were identified in these products. Results Living and organic products of green infrastructure require an LCA approach of their own. Most importantly, if conventional life cycle guidance for Environmental Product Declarations were to be followed, over time, the asymmetric mass and carbon flows would lead to skewed conclusions. Moreover, the ability of plants to reproduce raises additional questions for allocating environmental impacts. Conclusions We present a set of recommendations that are required for compiling Environmental Product Declarations for the studied products of green infrastructure. In order to enable the quantification of the climate change mitigation potential of these products, it is essential that work for further development of LCA guidance be mandated.

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Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Remote Sensing ◽

10.3390/rs13030438 ◽

2021 ◽

Vol 13 (3) ◽

pp. 438

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Vegetation Index ◽

Coastal Wetland ◽

Thermal Emission ◽

Mean Squared Error ◽

Wetland Vegetation ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Vegetation Monitoring ◽

Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

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The Assessment of Urbanization Effect and Sustainable Drainage Solutions on Flood Hazard by GIS

Sustainability ◽

10.3390/su13042293 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2293

Author(s):

Seda Ertan ◽

Rahmi Nurhan Çelik

Keyword(s):

Information System ◽

Geographic Information System ◽

Green Infrastructure ◽

Evaluation System ◽

Flood Hazard ◽

Green Roof ◽

Geographic Information ◽

Hydrologic Model ◽

Land Use Patterns ◽

Urbanization Effect

Rapid and uncontrolled changes in land use patterns due to urbanization negatively affect urban rainfall-runoff processes and flood hazard. In this study, a method that included different sustainable drainage solutions, such as green infrastructure (GI) usage for flood hazard mitigation with various scenarios on a geographic information system (GIS) platform within a 1653 ha catchment of the Kağıthane Stream in İstanbul, Turkey is presented. Developed scenarios are as follows: scenario one (SN1) is the current situation; scenario two (SN2) used green roof application for buildings and a permeable surface for roads; scenario three (SN3) used only green roof application for buildings; scenario four (SN4) used a rainwater barrel for collecting roof water, a swale canal for collecting road water, and added additional structures to open areas to observe urbanization; scenario five (SN5) considered multiple GI implementations; and scenario six (SN6) considered full urbanization. The results indicate that greener infrastructure implementation provides benefits in reducing both the runoff coefficient and the peak flowrate, and the flood inundation area and number of structures affected by flood risk were decreased. The integrated evaluation system, which consisted of the geographic information system and the assessment of the 1D HEC-RAS hydrologic model, was applied to evaluate the GI usage and flood mitigation.

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A comparison of satellite-based indices for hazard assessment of MSW open dumps using spatial analysis

Waste Management & Research ◽

10.1177/0734242x18815963 ◽

2018 ◽

Vol 37 (3) ◽

pp. 219-236 ◽

Cited By ~ 1

Author(s):

Khalid Mahmood ◽

Zia Ul-Haq ◽

Fiza Faizi ◽

Syeda A. Batol

Keyword(s):

Hazard Assessment ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Moving Average ◽

Vegetation Indices ◽

Temporal Analysis ◽

Zone Size ◽

Vegetation Health ◽

Spatio Temporal ◽

Hazardous Zone

This study compares the suitability of different satellite-based vegetation indices (VIs) for environmental hazard assessment of municipal solid waste (MSW) open dumps. The compared VIs, as bio-indicators of vegetation health, are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) that have been subject to spatio-temporal analysis. The comparison has been made based on three criteria: one is the exponential moving average (EMA) bias, second is the ease in visually finding the distance of VI curve flattening, and third is the radius of biohazardous zone in relation to the waste heap dumped at them. NDVI has been found to work well when MSW dumps are surrounded by continuous and dense vegetation, otherwise, MSAVI is a better option due to its ability for adjusting soil signals. The hierarchy of the goodness for least EMA bias is MSAVI> SAVI> NDVI with average bias values of 101 m, 203 m, and 270 m, respectively. Estimations using NDVI have been found unable to satisfy the direct relationship between waste heap and hazardous zone size and have given a false exaggeration of 374 m for relatively smaller dump as compared to the bigger one. The same false exaggeration for SAVI and MSAVI is measured to be 86 m and -14 m, respectively. So MSAVI is the only VI that has shown the true relation of waste heap and hazardous zone size. The best visualization of distance-dependent vegetation health away from the dumps is also provided by MSAVI.

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