scholarly journals Application of Remote Sensing on El Niño Extreme Effect in Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)

2021 ◽  
Vol 6 (1) ◽  
pp. 46-56
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Remote Sensing ◽  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
El Nino ◽  
Extreme Temperature ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
The Impact

The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.

scholarly journals USING REMOTE SENSING IN STUDY OF EL NIÑO EXTREME DIFFERENCES BETWEEN 1997/1998 AND 2015/2016

2021 ◽  
Vol 4 (17) ◽  
pp. 83-94
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Remote Sensing ◽  
El Niño ◽  
Land Surface ◽  
Spatial Information ◽  
El Nino ◽  
Hot Spot ◽  
Remote Sensing Data ◽  
El Niño Events ◽  
The Impact ◽  
El Nino Events

The years 1997/1998 and 2015/2016 saw the occurrence of El Niño occur among the worst in human history. Until now there is still a lack of research in studying the degree of El Niño's strength impact on climate and weather, especially in the tropic region. The objective of this study is to study the effectiveness of remote sensing technology in identifying the differences between the 1997/1998 and 2015/2016 El Niño events. This study uses six satellite data and temperature data from the Malaysia Meteorology Department (MMD). The first step of remote sensing data will be through pre-processing, converting digital Numbers (DN) to Land Surface Temperature (LST). The results of the study found that there was a change in the pattern of LST columns during the 1997/1998 and 2015/2016 El Niño events. Spatial patterns change based on Oceanic Niño Index (ONI) values. The results of this study are important because of the importance of spatial information to those responsible for preparing measures to overcome and reduce the impact of El Niño on the population. at the developing country level, including Malaysia, there is still a lack of information technology infrastructure in channeling useful information to the community. Through the information, this spatial information provides critical hot spot information that needs more attention.

scholarly journals COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

scholarly journals MAPPING AND MONITORING WETLANDS USING SENTINEL-2 SATELLITE IMAGERY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 271-277 ◽  
Author(s):  
G. Kaplan ◽  
U. Avdan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Kappa Coefficient ◽  
Water Index ◽  
Object Based ◽  
The World ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Mapping and monitoring of wetlands as one of the world`s most valuable natural resource has gained importance with the developed of the remote sensing techniques. This paper presents the capabilities of Sentinel-2 successfully launched in June 2015 for mapping and monitoring wetlands. For this purpose, three different approaches were used, pixel-based, object-based and index-based classification. Additional, for more successful extraction of wetlands, a combination of object-based and index-based method was proposed. It was proposed the use of object-based classification for extraction of the wetlands boundaries and the use of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for classifying the contents within the wetlands boundaries. As a study area in this paper Sakarbasi spring in Eskisehir, Turkey was chosen. The results showed successful mapping and monitoring of wetlands with kappa coefficient of 0.95.

scholarly journals Geospatial Analysis of Land Use/Land Cover Dynamics on Lake Abaya-Chamo Wetland in Southern Rift-Valley of Ethiopia

2021 ◽  
Author(s):  
Tariku Zekarias ◽  
Vanum Govindu ◽  
Yechale Kebede ◽  
Abren Gelaw
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Anthropogenic Factors ◽  
Southern Ethiopia ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Change Matrix ◽  
The Impact

Abstract Wetlands worldwide and in Ethiopia have long been subject to severe degradation due to anthropogenic factors. This study was aimed at analyzing the impact of land use/cover dynamics on Lake Abaya-Chamo wetland in 1990–2019. Data were acquired via Landsat TM of 1990, ETM + of 2000, and OLI of 2010 and 2019 images plus using interview. Unsupervised and supervised classifications (via ERDAS14 and ArcGIS10.5) were applied to detect land use/cover classes. Normalized difference vegetation index, normalized difference water index, change matrix model and Kappa coefficients were used for analysis of the land use/cover dynamics in the lake-wetland. It was found that forest; water, shrub land, agricultural land, settlement and swamp area were the main land use/cover classes. While ‘settlement’ and ‘water body’ of the lake-wetland increased at progressively increasing magnitudes of changes in three periods within 1990–2019, ‘shrub land’ and ‘swamp’ declined at progressively increasing magnitudes of loss in the same periods. The NDWI result revealed that ‘swamp’ area shrank by 48.9% (2,991 ha) due to siltation-led expansion of the lake-water in three decades. Siltation, rapid population growth-led expansion of settlement and irrigation-based farming were the main drivers of the land use/cover dynamics and degradation of the lake-wetland. Thus, consistent mapping and integrated actions should be taken to curb the threats on the sustainability of the lake-wetland in Southern Ethiopia.

Developing land-state maps of Tram Chim National Park in Vietnam based on monitoring results

2020 ◽  
Vol 963 (9) ◽  
pp. 53-64
Author(s):  
V.F. Kovyazin ◽  
Thi Lan Anh Dang ◽  
Viet Hung Dang
Keyword(s):  
National Park ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Likelihood Method ◽  
Wetland Area ◽  
Water Index ◽  
Effi Ciency ◽  
Normalized Difference Water Index ◽  
Specially Protected Natural Areas ◽  
Protected Natural Areas

Tram Chim National Park in Southern Vietnam is a wetland area included in the system of specially protected natural areas (SPNA). For the purposes of land monitoring, we studied Landsat-5 and Sentinel-2B images obtained in 1991, 2006 and 2019. The methods of normalized difference vegetation index (NDVI) and water objects – normalized difference water index (NDWI) were used to estimate the vegetation in National Park. The allocated land is classifi ed by the maximum likelihood method in ENVI 5.3 into categories. For each image, a statistical analysis of the land after classifi cation was performed. Between 1991 and 2019, land changes occurred in about 57 % of the Tram Chim National Park total area. As a result, the wetland area has signifi cantly reduced there due to climate change. However, the area of Melaleuca forests in Tram Chim National Park has increased due to the effi ciency of reforestation in protected areas. Melaleuca forests are also being restored.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals AEROSPACE MONITORING OF THE FOREST ECOSYSTEM DYNAMICS WITHIN THE TERRITORY OF VRACHANSKI BALKAN NATURE PARK

2018 ◽  
pp. 41-46
Author(s):  
Adlin Dancheva
Keyword(s):  
Remote Sensing ◽  
Forest Ecosystem ◽  
Vegetation Index ◽  
Forest Ecosystems ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing And Gis ◽  
Ecosystem Dynamics ◽  
Anthropogenic Pressure ◽  
Water Index ◽  
Nature Park

In this paper the application of Remote Sensing and GIS as a means of performing aero – space monitoring of forest ecosystems dynamics is being considered. The purpose of this work is to create a model for monitoring the dynamic of forest ecosystems, based on Remote Sensing and GIS. The results of eco-monitoring can be used to update plans and policies for forest ecosystem management. The territory of Vrachanski Balkan Nature park was chosen as the subject of research as there is a certain anthropogenic pressure there. The results presented are obtained by spatial-time analysis of certain aerospace data indices. To carry out the study optical satellite images were used, on the basics of which three indices were calculated: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Greenness Index (NDGI). A comparative analysis has been created and results of the degree of correlation between the different indices are presented, as well as indicators from the different test areas and related changes in the individual points in time. The results of the survey aim to assess the dynamics and condition of the forest vegetation on the territory of Vrachanski Balkan Nature park and can be utilised in activities related to monitoring, mapping and forest management.

scholarly journals The impact of the 2015/2016 El Niño on global photosynthesis using satellite remote sensing

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170409 ◽  
Author(s):  
Xiangzhong Luo ◽  
Trevor F. Keenan ◽  
Joshua B. Fisher ◽  
Juan-Carlos Jiménez-Muñoz ◽  
Jing M. Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Carbon Sink ◽  
Global Climate ◽  
Prognostic Models ◽  
Positive Anomaly ◽  
The Impact

The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015–2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of −0.7 ± 1.2 PgC in 2015, but a slight positive anomaly of 0.05 ± 0.89 PgC in 2016, which when combined with observations of the growth rate of atmospheric carbon dioxide concentrations suggests that the reduction of the land residual sink was likely dominated by photosynthesis in 2015 but by respiration in 2016. The six satellite-based products unanimously identified a major photosynthesis reduction of −1.1 ± 0.52 PgC from savannahs in 2015 and 2016, followed by a highly uncertain reduction of −0.22 ± 0.98 PgC from rainforests. Vegetation in the Northern Hemisphere enhanced photosynthesis before and after the peak El Niño, especially in grasslands (0.33 ± 0.13 PgC). The patterns of satellite-based photosynthesis ensemble mean were corroborated by SIF, except in rainforests and South America, where the anomalies of satellite-based photosynthesis products also diverged the most. We found the inter-model variation of photosynthesis estimates was strongly related to the discrepancy between moisture forcings for models. These results highlight the importance of considering multiple photosynthesis proxies when assessing responses to climatic anomalies. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

scholarly journals Monitoring of Inland Surface Water Quality Using Remote Sensing on the Example of Wigry Lake

2018 ◽  
Vol 63 ◽  
pp. 00017
Author(s):  
Michał Lupa ◽  
Katarzyna Adamek ◽  
Renata Stypień ◽  
Wojciech Sarlej
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Water Quality ◽  
Lake Area ◽  
Landsat Images ◽  
Secchi Disc ◽  
Water Index ◽  
Normalized Difference Water Index

The study examines how LANDSAT images can be used to monitor inland surface water quality effectively by using correlations between various indicators. Wigry lake (area 21.7 km2) was selected for the study as an example. The study uses images acquired in the years 1990–2016. Analysis was performed on data from 35 months and seven water condition indicators were analyzed: turbidity, Secchi disc depth, Dissolved Organic Material (DOM), chlorophyll-a, Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The analysis of results also took into consideration the main relationships described by the water circulation cycle. Based on the analysis of all indicators, clear trends describing a systematic improvement of water quality in Lake Wigry were observed.

scholarly journals Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

2019 ◽  
Vol 26 (3) ◽  
pp. 117
Author(s):  
Tri Muji Susantoro ◽  
Ketut Wikantika ◽  
Agung Budi Harto ◽  
Deni Suwardi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Landsat 8 ◽  
Gas Field ◽  
Research Results ◽  
The Third ◽  
Third Phase ◽  
The Impact

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

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