scholarly journals Potential Water Harvesting Sites Identification Using Spatial Multi-Criteria Evaluation in Maysan Province, Iraq

2020 ◽  
Vol 9 (4) ◽  
pp. 235
Author(s):  
Imzahim A. Alwan ◽  
Nadia A. Aziz ◽  
Mustafa N. Hamoodi
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rainwater Harvesting ◽  
Supply And Demand ◽  
Geographical Information ◽  
Water Harvesting ◽  
Climate Conditions ◽  
Promising Tool ◽  
Multi Criteria Evaluation ◽  
Semi Arid Region

Rainwater harvesting is a promising tool for supplementing surface water and groundwater to overcome the imbalance between water supply and demand under changing climate conditions. Multi-Criteria Evaluation is one of the well-known methods of decision-making. In this study, the geographical information system (GIS)-based Multi-Criteria Evaluation is used to select the optimum rainwater harvesting sites in Maysan province, Iraq. Fuzzy membership is used for standardization of the criteria, and Fuzzy Gamma overlay for a combination of multi-layers using ArcGIS 10.5. Seven criteria layers, including slope, stream order, soil type, precipitation, evaporation, roads, and the Normalized Difference Vegetation Index (NDVI) are derived to identify rainwater-harvesting catchment. The results determined the optimum sites for water storage within the study area. The resultant potential rainwater harvesting catchment map can be used as a reference to enhance the effectiveness of water management, especially in drought-stricken areas that offer significant potential for sustainable agricultural production in the semi-arid region.

scholarly journals Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1953 ◽  
Author(s):  
Safa Bousbih ◽  
Mehrez Zribi ◽  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Remotely Sensed Data ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Sentinel 2 ◽  
Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

scholarly journals Herbivory Rate on Woody Species of the Caatinga and NDVI as Indicators of Plant Stress (Taxa de Herbivoria em Espécies Arbóreas da Caatinga e o Uso do Índice de Vegetação por Diferença Normalizada (NDVI) como Indicador de Estresse em Planta)

2012 ◽  
Vol 4 (5) ◽  
pp. 909
Author(s):  
Jarcilene Almeida-Cortez ◽  
Mateus Dantas de Paula ◽  
Martin Duarte de Oliveira ◽  
Cátia Inês Rodrigues dos Santos
Keyword(s):  
Primary Production ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Stress ◽  
Woody Species ◽  
Abiotic Conditions ◽  
Close Relationship ◽  
Negative Effect ◽  
Semi Arid Region

Espécies de plantas distribuídas em uma paisagem são submetidas a um mosaico de condições abióticas que podem ter efeito negativo sobre o desenvolvimento (stress geração) e expô-las à predação por herbívoros. Esse estresse pode causar adicionalmente assimetria foliar e uma redução na produção primária. A taxa fotossintética, relacionada com a produtividade da planta, pode ser medida por índices espectrais, tais como o NDVI (índice de vegetação da diferença normalizada), calculado a partir de imagens de satélite. No presente trabalho, testou-se a hipótese de que ambientes com baixa produtividade primária (NDVI baixo) irá possuir maior assimetria foliar e maiores taxas de herbivoria. Os resultados mostram que na região de Caatinga semi árida de Pernambuco, Brasil, a folha de assimetria diminui com valores mais elevados de NDVI, indicando uma estreita relação entre esta medida da planta e o índice espectral. Por outro lado, a correlação entre herbivoria e produção primária ou assimetria foliar não foi significativa, sugerindo que os herbívoros vão além da simples seleção de indivíduos mais estressados. Palavras-Chave: Assimetria flutuante, herbivoria, NDVI   Taxa de Herbivoria em Espécies Arbóreas da Caatinga e o Uso do Índice de Vegetação por Diferença Normalizada (NDVI) como Indicador de Estresse em Planta   ABSTRACT Plant species distributed on a landscape are submitted to a mosaic of abiotic conditions that may have a negative effect on development (generating stress) and expose them to predation by herbivores. This stress can cause additionally leaf asymmetry and a reduction on primary production. The photosynthetic rate, related to plant productivity, can be measured by spectral indexes, such as the NDVI (normalized difference vegetation index), calculated from satellite images. In the present work, we test the hypothesis that environments with low primary productivity (low NDVI) will possess larger leaf asymmetry and higher herbivory rates. Our results show that in the Caatinga semi-arid region of Pernambuco, Brazil, the leaf asymmetry reduces with higher NDVI values, indicating a close relationship between this plant measure and the spectral index. On the other side, the correlation between herbivory and primary production or leaf asymmetry was not significant, suggesting that herbivores go beyond just selecting more stressed individuals.   Keywords: Leaf asymmetry, NDVI, herbivory

scholarly journals Land use and Land cover Mapping Based on Normalized Difference Vegetation Index using Remote Sensing and Geographical Information System in Banjar River Watershed of Narmada Basin

2017 ◽  
Vol 12 (3) ◽  
pp. 678-684
Author(s):  
Jagriti Tiwari ◽  
S.K. Sharma ◽  
R.J. Patil
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Water Body ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographical Information ◽  
River Watershed

The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.

scholarly journals Rainwater Harvesting Using GIS Technique: A Case Study of Diyala Governorate, Iraq

2021 ◽  
Vol 32 (2) ◽  
pp. 96
Author(s):  
Dhuha S. Al-Khafaji ◽  
Asraa Khtan Abdulkareem ◽  
Qusai Y. Al-Kubaisi
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rainwater Harvesting ◽  
Cost Effective ◽  
Stream Order ◽  
Time Saving ◽  
Harvesting Systems ◽  
Gis Environment ◽  
Landsat Satellite

To improve the management of water resources in Iraq, there are several methods, including the use of rainwater harvesting techniques. In this study, the Digital Elevation Model (DEM) and Landsat satellite imagery were used under the GIS environment to identify the suitable zones for rainwater harvesting. The accomplishment of rainwater harvesting systems strongly depends on their technical designing and identifying the suitable sites. Six criteria have been used to identify the rainwater harvesting sites in the Diyala governorate. The procedure of identifying the suitable sites for rainwater harvesting was applied twice for the Diyala governorate. Firstly, it was applied by using the criteria of rainfall, slope, stream order, distance to roads, and land use, and secondly, rainfall, slope, stream order, distance to roads, and Normalized Difference Vegetation Index (NDVI) criteria were used for this purpose. As a result, the study area was divided into three suitability zones: low, moderate, and high according to the specific criteria that were used to identify the rainwater harvesting suitable sites. It was found that in the application of land use criterion the low suitability zone represents 26%, 58% represents the moderate, and 16% for the high suitability zone, while in the method of NDVI it was found that 29% represents the zone that has low suitability, 57% represents the moderate, and 14% represents the high suitability zone. The compared results led to conclude that the land use is the most influential criterion for identifying the rainwater harvesting suitability sites and found that most of the Eastern parts of Diyala governorate are promising areas for rainwater harvesting and ArcGIS is a very useful, time-saving, and cost-effective tool for identifying the rainwater harvesting suitable sites.

scholarly journals Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment – Results from UAS based high resolution remote sensing

2021 ◽  
Author(s):  
Marc Wehrhan ◽  
Daniel Puppe ◽  
Danuta Kaczorek ◽  
Michael Sommer
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Spatial Patterns ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Biomass ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Promising Tool ◽  
Technical Features

Abstract. Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most of these studies were performed at relatively small plots with an intended low heterogeneity in soils and plant canopy composition, and results were extrapolated to larger spatial units up to global scale implicitly assuming similar environmental conditions. However, the emergence of new technical features and increasing knowledge on details in Si cycling leads to a more complex picture at landscape or catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation, but its biomass distribution and related Si stocks. Maximum Likelihood (ML) classification was applied to multispectral imagery captured by an Unmanned Aerial System (UAS) aiming the identification of land cover classes (LCC). Subsequently, the Normalized Difference Vegetation Index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si accumulating plants (Calamagrostis epigejos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epigejos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Si m−2), i.e., comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.

Developing City-Wide Hurricane Impact Maps using Real-Life Data on Infrastructure, Vegetation and Weather

2020 ◽  
pp. 036119812097271
Author(s):  
Mingyang Chen ◽  
Alican Karaer ◽  
Eren Erman Ozguven ◽  
Tarek Abichou ◽  
Reza Arghandeh ◽  
...  
Keyword(s):  
Geographical Information Systems ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Real Life ◽  
Google Earth ◽  
Geographical Information ◽  
Data Sets ◽  
Hurricane Impact ◽  
The City

Hurricanes affect thousands of people annually, with devastating consequences such as loss of life, vegetation and infrastructure. Vegetation losses such as downed trees and infrastructure disruptions such as toppled power lines often lead to roadway closures. These disruptions can be life threatening for the victims. Emergency officials, therefore, have been trying to find ways to alleviate such problems by identifying those locations that pose high risk in the aftermath of hurricanes. This paper proposes an integrated methodology that utilizes both Google Earth Engine (GEE) and geographical information systems (GIS). First, GEE is used to access Sentinel-2 satellite images and calculate the Normalized Difference Vegetation Index (NDVI) to investigate the vegetation change as a result of Hurricane Michael in the City of Tallahassee. Second, through the use of ArcGIS, data on wind speed, debris, roadway density and demographics are incorporated into the methodology in addition to the NDVI indices to assess the overall impact of the hurricane. As a result, city-wide hurricane impact maps are created using weighted indices created based on all these data sets. Findings indicate that the northeast side of the city was the worst affected because of the hurricane. This is a region where more seniors live, and such disruptions can lead to dramatic consequences because of the fragility of these seniors. Officials can pinpoint the identified critical locations for future improvements such as roadway geometry modification and landscaping justification.

scholarly journals Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

2020 ◽  
Vol 12 (18) ◽  
pp. 2970
Author(s):  
Anna C. Talucci ◽  
Elena Forbath ◽  
Heather Kropp ◽  
Heather D. Alexander ◽  
Jennie DeMarco ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Basal Area ◽  
Vegetation Recovery ◽  
Climate Conditions ◽  
Northeastern Siberia ◽  
Larch Forests

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.

scholarly journals Digital aerial images land cover classification based on vegetation indices

2012 ◽  
Vol 31 (3) ◽  
pp. 5-23
Author(s):  
Maciej Dzieszko ◽  
Piotr Dzieszko ◽  
Sławomir Królewicz
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Aerial Images ◽  
Geographical Information ◽  
Wide Availability

Abstract . Knowledge of how land cover has changed over time improve assessments of the changes in the future. Wide availability of remote sensed data and relatively low cost of their acquisition make them very attractive data source for Geographical Information Systems (GIS). The main goal of this paper is to prepare, run and evaluate image classification using a block of raw aerial images obtained from Digital Mapping Camera (DMC). Classification was preceded by preparation of raw images. It contained geometric and radiometric correction of every image in block. Initial images processing lead to compensate their brightness differences. It was obtained by calculating two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Green Normalized Vegetation Index (gNDVI). These vegetation indices were the foundation of image classification. PCI Geomatics Geomatica 10.2 and Microimages TNT Mips software platforms were used for this purpose.

scholarly journals Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies

2018 ◽  
Author(s):  
Abbas Haghshenas ◽  
Yahya Emam
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Canopy Temperature ◽  
Image Mining ◽  
Growing Seasons ◽  
Gradient Based ◽  
Semi Arid Region ◽  
Mining Model ◽  
Experimental Plots

AbstractEfficient quantification of the sophisticated shading patterns inside the 3D vegetation canopies may improve our understanding of canopy functions and status, which is possible now more than ever, thanks to the high-throughput phenotyping (HTP) platforms. In order to evaluate the option of quantitative characterization of shading patterns, a simple image mining technique named “green-gradient based canopy segmentation model (GSM)” was developed based on the relative variations in the level of RGB triplets under different illuminations. For this purpose, an archive of ground-based nadir images of heterogeneous wheat canopies (cultivar mixtures) was analyzed. The images were taken from experimental plots of a two-year field experiment conducted during 2014-15 and 2015-16 growing seasons in the semi-arid region of southern Iran. In GSM, the vegetation pixels were categorized into the maximum possible number of 255 groups based on their green levels. Subsequently, mean red and mean blue levels of each group were calculated and plotted against the green levels. It is evidenced that the yielded graph could be readily used for (i) identifying and characterizing canopies even as simple as one or two equation(s); (ii) classification of canopy pixels in accordance with the degree of exposure to sunlight; and (iii) accurately prediction of various quantitative properties of canopy including canopy coverage (CC), Normalized difference vegetation index (NDVI), canopy temperature, and also precise classification of experimental plots based on the qualitative characteristics such as subjecting to water and cold stresses, date of imaging, and time of irrigation. It seems that the introduced model may provide a multipurpose HTP platform and open new windows to canopy studies.

scholarly journals Geospatial Hydrological Analysis in GIS Environment for Selecting Potential Water Harvest Sites: The Case of Badrah –Wasit

2018 ◽  
Vol 26 (2) ◽  
pp. 328-337 ◽  
Author(s):  
Khalid Ahmed Ali
Keyword(s):  
Water Resource Management ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Water Harvesting ◽  
Analysis Method ◽  
Weighted Overlay ◽  
Gis Technique ◽  
Overlay Method ◽  
Gis Environment

In this study, GIS technique and remote sensing data have been integrated to createa suitability map for the probable sites of water harvesting in Badrah-Wasit, EasternIraq.Hydrological analysis used to find the potential water-harvesting sites, as well as to improve the water resource management. In this research, five criteria have been used, which is astream order, slope, distance to roads, rainfall and Normalized Difference Vegetation Index. These thematic layerswere evaluated with the multi-criteria analysis method, then combine and process together using weighted overlay method, then assigned suitable weights and integrated into a GIS to generate a suitability map.As a result, the region has been classified into three zones: high suitability zone (2%), moderate suitability zone (27%), and low suitability zone (35%) depending on the specific criteria used for this purpose and have high potential in terms of their suitability for water harvesting.

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