scholarly journals Aplication Multi Vegetation Index to Mapping Magrove Distribution Coast Environtment Northeast Province of Aceh, Indonesia

2017 ◽  
Vol 1 (1) ◽  
pp. 97
Author(s):  
Muhammad Hanif ◽  
Tommy Adam
Keyword(s):  
Vegetation Index ◽  
Accuracy Assessment ◽  
Research Use ◽  
Image Transformation ◽  
Mangrove Area ◽  
Density Slicing ◽  
Coast Environment ◽  
Landsat Satellite ◽  
Index Comparison ◽  
Data Analysis Result

The mangrove such forest very important at coastal ecosystem and environment. The purpose of the research to mapping mangrove distribution at the coast environment using multi vegetation index, comparison accuracy assessment to mapping mangrove area. The method of the research use by multi imagery transformation as NDVI, Infrared II, SAVI, EVI and Maximum Likelihood. Data on the research have using by Landsat OLI8, tools use by ENVI 5.0 and ArcGIS 10.1. Optimizing the used of data from Landsat satellite imagery for mapping mangrove found where sharper appearance mangrove area in the gray scale image of the results of the analysis of vegetation transformation NDVI, Infrared II, SAVI and EVI showing difference specification, but also found has founded difference objects of interpreted it was showing like shadows of cloud be the another object. To classification on mangrove object is seen from the results of density slicing of transformation value to classing vegetation. The percentage accuracy of image prove some dominant image transformation is able to indicate a more optimal mangrove and mangrove separating the object is not present, but the accuracy of the data analysis result has variations, refers to the number of samples used

Separation of Different Vegetation Types in ASTER and Landsat Satellite Images Using Satellite‐derived Vegetation Indices

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Mazlan Hashim ◽  
Sharifeh Hazini
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Vegetation Indices ◽  
Critical Issue ◽  
Vegetation Types ◽  
Enhanced Vegetation Index ◽  
Density Slicing ◽  
Landsat Satellite

Separation of different vegetation types in satellite images is a critical issue in remote sensing. This is because of the close reflectance between different vegetation types that it makes difficult segregation of them in satellite images. In this study, to facilitate this problem, different satellite derived vegetation indices including: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index 2 (EVI2) were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-5 TM data. The obtained NDVI, EVI, and EVI2 images were then analyzed and interpreted in order to evaluate their effectiveness to discriminate rice and citrus fields from ASTER and Landsat data. In doing so, the Density Slicing (DS) classification technique followed by the trial and error method was implemented. The results indicated that the accuracies of ASTER NDVI and ASTER EVI2 for citrus mapping are about 75% and 65%, while the accuracies of Landsat NDVI and Landsat EVI for rice mapping are about 60% and 65%, respectively. The achieved results demonstrated higher performance of ASTER NDVI for citrus mapping and Landsat EVI for rice mapping. The study concluded that it is difficult to detect and map rice fields from satellite images using satellite-derived indices with high accuracy. However, the citrus fields can be mapped with the higher accuracy using satellite-derived indices.

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).

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 A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1767
Author(s):  
Davide Cammarano ◽  
Hainie Zha ◽  
Lucy Wilson ◽  
Yue Li ◽  
William D. Batchelor ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Farming Systems ◽  
Small Scale ◽  
Sustainable Food ◽  
Environmentally Sustainable ◽  
Management Zone ◽  
The North ◽  
Landsat Satellite ◽  
The Village

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

scholarly journals An Approach Based on Landsat Images for Shoreline Monitoring to Support Integrated Coastal Management—A Case Study, Ezbet Elborg, Nile Delta, Egypt

2020 ◽  
Vol 9 (4) ◽  
pp. 199 ◽  
Author(s):  
Mohamed T. Elnabwy ◽  
Emad Elbeltagi ◽  
Mahmoud M. El Banna ◽  
Mohamed M.Y. Elshikh ◽  
Ibrahim Motawa ◽  
...  
Keyword(s):  
Coastal Management ◽  
Nile Delta ◽  
Accuracy Assessment ◽  
Shoreline Change ◽  
Coastal Zones ◽  
Landsat Images ◽  
Change Analysis ◽  
Integrated Coastal Management ◽  
Rtk Gps ◽  
Landsat Satellite

Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area.

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 Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq

2021 ◽  
pp. 39
Author(s):  
Awad A. Sahar ◽  
Muaid J. Rasheed ◽  
Dhia A. A.-H. Uaid ◽  
Ammar A. Jasim
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Accuracy Assessment ◽  
Urban Life ◽  
Dynamic Features ◽  
Detection Techniques ◽  
Sandy Area ◽  
North East ◽  
South Of Iraq ◽  
Landsat Satellite

<p>Sandy areas are the main problem in regions of arid and semi-arid climate in the world that threaten urban life, buildings, agricultural, and even human health. Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulations. In this study, two new indices were developed to separate the sandy areas from the non-sandy areas. The first one is called the Normalized Differential Sandy Areas Index (NDSAI) that has been based on the assumption that the sandy area has the lowest water content (moisture) than the other land cover classes. The second other is called the Sandy Areas Surface Temperature index (SASTI) which was built on the assumption that the surface temperature of sandy soil is the highest. The results of proposed indices have been compared with two indices that were previously proposed by other researchers, namely the Normalized Differential Sand Dune Index NDSI and the Eolain Mapping Index (EMI). The accuracy assessment of the sandy indices showed that the NDSAI provides very good performance with an overall accuracy of 89 %. The SASTI can isolate many sandy and non-sandy pixels with an overall accuracy about 86 %. The performance of the NDSI is low with an overall accuracy about 82 %. It fails to classify or isolate the vegetation area from the sandy area and might have better performance in desert environments. The performing of NDSAI that is calculated with the SWIR1 band of the Landsat satellite is better than the performing of NDSI that is calculated with the SWIR2 band of the same satellite. EMI performance is less robust than other methods as it is not useful for extracting sandy surfaces in area with different land covers. Change detection techniques were used by comparing the areas of the sandy lands for the periods from 1987 to 2017. The results showed an increase in sandy areas over four decades. The percentage of this increase was about 20 % to 30 % during 2002 and 2017 compared to 1987.</p>

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.

Sign in / Sign up

Export Citation Format

Share Document