scholarly journals SOIL SALINITY MAPPING USING SATELLITE REMOTE SENSING: A CASE STUDY OF LOWER CHENAB CANAL SYSTEM, PUNJAB

2020 ◽  
Vol 4 (1) ◽  
pp. 07-09
Author(s):  
Muhammad Mohsin Waqas ◽  
Yasir Niaz ◽  
Sikandar Ali ◽  
Ishfaq Ahmad ◽  
Muhammad Fahad ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation System ◽  
Accuracy Assessment ◽  
Cropping System ◽  
Severe Problem ◽  
Error Matrix ◽  
Spatially Distributed ◽  
Landsat Satellite

Salinity is the most important factor of consideration for the water management policies. The water availability from the rootzone reduced with the increase in the soil salinity due to the increase in the osmatic pressure. In Pakistan, salinity is the major threat to the agriculture land due to the tradition practices of irrigation and extensive utilization of the groundwater to meet the cope the irrigation water requirement of high intensity cropping system. The salinity impact is spatially variable on the canal commands area of the irrigation system. There is dire need to map the spatially distributed soil salinity with the high resolution. Landsat satellite imagery provides an opportunity to have 30m pixel information in seven spectral wavelength ranges. In this study, the soil salinity mapping was performed using pixel information on visible and infrared bands for 2015. These bands were also used to infer Normalized Difference Vegetation Index (NDVI). The raw digital numbers were converted into soil salinity information. The accuracy assessment was carried out using ground trothing information obtained using the error matrix method. Four major classes of non-saline, marginal saline, moderate saline and strongly, saline area was mapped. The overall accuracy of the classified map was found 83%. These maps can be helpful to delineate hot spots with severe problem of soil salinity in order to prepare reciprocate measures for improvement.

scholarly journals Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 241
Author(s):  
Asish Saha ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Somayeh Panahi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Research Work ◽  
Dominant Factor ◽  
Support Vector ◽  
Accuracy Evaluation ◽  
Ensemble Approach ◽  
Flood Susceptibility

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871).

Change on detection of vegetation cover and soil salinity using GIS technique in Diyala Governorate, Iraq

2021 ◽  
Vol 30 (1) ◽  
pp. 148-158
Author(s):  
Haneen Adeeb ◽  
Yaseen Al-Timimi
Keyword(s):  
Soil Salinity ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Inverse Correlation ◽  
Salinity Changes ◽  
Gis Technique ◽  
Salinity Index ◽  
Remote Sensing Techniques ◽  
Strong Inverse Correlation

Soil salinity is one of the most important problems of land degradation, that threatening the environmental, economic and social system. The aim of this study to detect the changes in soil salinity and vegetation cover for Diyala Governorate over the period from 2005 to 2020, through the use of remote sensing techniques and geographic information system. The normalized difference vegetation index (NDVI) and salinity index (SI) were used, which were applied to four of the Landsat ETM+ and Landsat OLI satellite imagery. The results showed an increase in soil salinity from 7.27% in the period 2005–2010 to 27.03% in 2015–2020, as well as an increase in vegetation from 10% to 24% in the same period. Also the strong inverse correlation between the NDVI and the SI showed that vegetation is significantly affected and directly influenced by soil salinity changes

Environmental benefits of traditional irrigation ditches in the Sierra Nevada (Spain) ecosystem by analysing the spatial-temporal evolution of NDVI on different time scales

2021 ◽  
Author(s):  
Javier Aparicio ◽  
Rafael Pimentel ◽  
María José Polo
Keyword(s):  
Soil Moisture ◽  
Sierra Nevada ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Global Analysis ◽  
Environmental Benefits ◽  
Mountain Range ◽  
Mountain Area ◽  
Traditional Irrigation ◽  
Landsat Satellite

<p>In Mediterranean mountain regions, traditional irrigation systems still persist in areas where the  modernization approaches do not succeed in being operational. It is common that these systems alter the soil uses, vegetation distribution and hydrological natural regime. </p><p>This is the case of the extensive network of irrigation ditches in the Sierra Nevada Mountain Range in southeastern Spain (an UNESCO  Reserve of the Biosphere, with areas as Natural and National Park), which originated in Muslim times, and is still operational in some areas. These ditches have contributed to maintaining local agricultural systems and populations in basins dominated by snow conditions, and they constitute a traditional regulation of water resources in the area. The network is made up of two types of irrigation ditches: “careo” and irrigation ditches. The first, the "careo", collects the meltwater and infiltrates it along its course, maintaining a high level of soil moisture and favouring deep percolation volumes that can be later consumed by the population through springs and natural fountains. The second, the irrigation ones, are used to transport water from the natural sources to the agricultural plots downstream the mountain area. In 2014, several irrigation ditches were restored in the Natural Park. This is a chance to further explore and quantify the role of this network in the hydrological budget on a local basis.  </p><p>The aim of this work is to evaluate to what extent the existence of these intermittent water networks affects the evolution of the surrounding vegetation. For this, one of the restored systems,  the Barjas Ditch in the village of Cañar, with a successful water circulation along its way, was selected from the increase of the soil water content in the ditch influence area and, indirectly a differential development of vegetation. Two analyses are performed using remote sensing information. The Normalized Difference Vegetation Index, NDVI, which is a spectral index used to estimate the quantity, quality and development of vegetation that can therefore be used indirectly as an indicator of the state of soil moisture, was used as the indicator of evolution. For this purpose, a historical set of LandSat satellite images  (TM, ETM+ and OLI) has been used. On the one hand, a global analysis on the whole mountainous range was carried out, comparing NDVI patterns in areas affected and non-affected by the ditches. On the other hand, the restored  Barjas ditch is used to assess vegetation changes before and after the restoration.</p>

scholarly journals Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 548 ◽  
Author(s):  
Xinpeng Tian ◽  
Zhiqiang Gao
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Aerosol Loading ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis ◽  
Reflecting Surface

The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined DT/DB AOD products for the years 2000–2016 are used. These products are validated using AErosol RObotic NETwork (AERONET) data from twenty-three ground sites situated in high aerosol loading areas and with available measurements at least 500 days. The results show that the numbers of collections (N) of DB and DT/DB retrievals were much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright reflecting surface and heavy pollution conditions. The percentage falling within the expected error (PWE) of the DT retrievals (45.6%) is lower than that for the DB (53.4%) and DT/DB (53.1%) retrievals. The DB retrievals have 5.3% less average overestimation, and 25.7% higher match ratio than DT/DB retrievals. It is found that the current merged aerosol algorithm will miss some cases if it is determined only on the basis of normalized difference vegetation index. As the AOD increases, the value of PWE of the three products decreases significantly; the undervaluation is suppressed, and the overestimation is aggravated. The retrieval accuracy shows distinct seasonality: the PWE is largest in autumn or winter, and smallest in summer. The most severe overestimation and underestimation occurred in the summer. Moreover, the DT, DB and DT/DB products over different land cover types still exhibit obvious deviations. In urban areas, the PWE of DB product (52.6%) is higher than for the DT/DB (46.3%) and DT (25.2%) products. The DT retrievals perform poorly over the barren or sparsely vegetated area (N = 52). However, the performance of three products is similar over vegetated area. On the whole, the DB product performs better than the DT product over the heavy aerosol loading area.

Assessing Hemlock Decline Using Visible and Near-Infrared Spectroscopy: Indices Comparison and Algorithm Development

2005 ◽  
Vol 59 (6) ◽  
pp. 836-843 ◽  
Author(s):  
Jennifer Pontius ◽  
Richard Hallett ◽  
Mary Martin
Keyword(s):  
Linear Regression ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Stress Index ◽  
Regression Equations ◽  
Near Infrared Reflectance ◽  
Full Spectrum ◽  
Hemlock Decline

Near-infrared reflectance spectroscopy was evaluated for its effectiveness at predicting pre-visual decline in eastern hemlock trees. An ASD FieldSpec Pro FR field spectroradiometer measuring 2100 contiguous 1-nm-wide channels from 350 nm to 2500 nm was used to collect spectra from fresh hemlock foliage. Full spectrum partial least squares (PLS) regression equations and reduced stepwise linear regression equations were compared. The best decline predictive model was a 6-term linear regression equation ( R2 = 0.71, RMSE = 0.591) based on: Carter Miller Stress Index (R694/R760), Derivative Chlorophyll Index (FD705/FD723), Normalized Difference Vegetation Index ((R800 – R680)/(R800 + R680)), R950, R1922, and FD1388. Accuracy assessment showed that this equation predicted an 11-class decline rating with a 1-class tolerance accuracy of 96% and differentiated healthy trees from those in very early decline with 72% accuracy. These results indicate that narrow-band sensors could be developed to detect very early stages of hemlock decline, before visual symptoms are apparent. This capability would enable land managers to identify early hemlock woolly adelgid infestations and monitor forest health over large areas of the landscape.

scholarly journals COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED LAND COVER CHANGE DETECTION METHODS

2016 ◽  
Vol XLI-B7 ◽  
pp. 579-583
Author(s):  
Zhenlei Xie ◽  
Ruoming Shi ◽  
Ling Zhu ◽  
Shu Peng ◽  
Xu Chen
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Detection Method ◽  
Accuracy Assessment ◽  
Object Oriented ◽  
Detection Methods ◽  
Commission Error ◽  
Time Saving

Change detection method is an efficient way in the aim of land cover product updating on the basis of the existing products, and at the same time saving lots of cost and time. Considering the object-oriented change detection method for 30m resolution Landsat image, analysis of effect of different segmentation scales on the method of the object-oriented is firstly carried out. On the other hand, for analysing the effectiveness and availability of pixel-based change method, the two indices which complement each other are the differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) were used. To demonstrate the performance of pixel-based and object-oriented, accuracy assessment of these two change detection results will be conducted by four indicators which include overall accuracy, omission error, commission error and Kappa coefficient.

scholarly journals MAPPING OF <i>SARGASSUM</i> DISTRIBUTION IN THE EASTERN COAST OF SOUTHERN LEYTE USING SENTINEL 2 SATELLITE IMAGERY

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 289-295
Author(s):  
J. J. Lasquites ◽  
A. C. Blanco ◽  
A. Tamondong
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Marine Ecosystem ◽  
Brown Seaweed ◽  
The Philippines ◽  
Eastern Coast ◽  
Department Of Agriculture ◽  
Sentinel 2

Abstract. Sargassum is a brown seaweed distributed in the Philippines and recognized as an additional source of income for fishing communities. Due to uncontrolled harvesting of the seaweed, the Department of Agriculture regulated its collection and harvesting by imposing seasonal restrictions. Hence, the need to identify the locations and cover of healthy Sargassum is vital to address the demand in the market while maintaining ecological balance in the marine ecosystem. Two Sentinel-2 satellite imagery (10 m resolution) acquired on December 08, 2017 (peak growth) and May 27, 2018 (senescence stage) were used to map the presence of Sargassum in the eastern coast of Southern Leyte. Supervised classification using maximum likelihood algorithm and accuracy assessment were conducted before generating the map. Three classes were considered namely Sargassum, clouds and land. Furthermore, Anselin Local Moran’s I (cluster and outlier analysis) was conducted to determine which areas have significant clustering of “healthy” Sargassum using the normalized difference vegetation index (NDVI). For both image dates, high classification accuracies of Sargassum were obtained in the islands. However, there are misclassifications of Sargassum in Silago (UA = 78.72%) and Hinunangan (PA = 82.35%) using the May image. Furthermore, misclassification of Sargassum were obtained in Silago (PA = 93.6%) and Hinundayan (PA = 96.23%) using the December image. Clusters of high NDVI values are more evident in December. Healthy Sargassum are apparent in the coast of Silago and mostly found near shore and in rocky substrates.

scholarly journals Cartographie De L’état Du Couvert Végétal Du Nord De La Côte D’ivoire À Partir D’images Satellites: Exemple De La Zone De Korhogo

2016 ◽  
Vol 12 (29) ◽  
pp. 204
Author(s):  
Avy StéphaneKoff ◽  
Abderrahman Ait Fora ◽  
Hicham Elbelrhiti
Keyword(s):  
Cote D'ivoire ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Côte D’Ivoire ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
The State ◽  
Cote D’Ivoire ◽  
Côte D'ivoire ◽  
Landsat Satellite

The purpose of this study is to determine the state of the vegetation cover in the region of Korhogo through remote sensing. Nowadays, the problem of desertification in the Sahel is serious. This could be explained by the phenomenon of climate change. We want to map the state of the vegetation cover in the study area. This study therefore focuses on the state of the vegetation cover in the region of Korhogo in northern Côte d’Ivoire. We will use one Landsat satellite image from December 16th 2000 and proceed with image processing. Processing techniques by the normalized difference vegetation index, the index armor and colorful composition 472. After these treatments in our pictures, we observe the behavior of vegetation. We can then get an overview of the vegetation in this area.

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 DETECTION OF ACID SLUDGE CONTAMINATED AREA BASED ON NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE

2017 ◽  
Vol 11 (1) ◽  
pp. 21
Author(s):  
Nanik Suryo Haryani ◽  
Sayidah Sulma ◽  
Junita Monika Pasaribu
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Dry Season ◽  
Wet Season ◽  
Multi Temporal ◽  
Acid Sludge ◽  
Landsat Satellite ◽  
Solid Form

The solid form of oil heavy metal waste is  known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI.  In this research, NDVI is analyzed using the  remote sensing data  on dry season and wet season.  Between 1997 to 2012, NDVI value in dry season  is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season  NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995).  The high NDVI value shows the leaf health or  thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort.

Sign in / Sign up

Export Citation Format

Share Document