scholarly journals Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

2015 ◽  
Vol 8 (3-4) ◽  
pp. 11-20 ◽  
Author(s):  
András Gulácsi ◽  
Ferenc Kovács
Keyword(s):  
Drought Index ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Short Wave ◽  
Drought Indices ◽  
High Temporal Resolution ◽  
Spectral Indices ◽  
Water Index ◽  
Water Indices

Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.

scholarly journals SUAS Based Multispectral Imagery for Monitoring Wetland Inundation and Vegetation

2019 ◽  
Author(s):  
Dustin Dehm ◽  
Richard Becker ◽  
Alexandra Godre
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Correction Method ◽  
Water Levels ◽  
Water Volume ◽  
Unmanned Aerial Systems ◽  
Short Term ◽  
Water Index ◽  
Normalized Difference Water Index

Mapping short-term wetland vegetation and water storage changes is valuable for monitoring the biogeochemical processes of wetland systems. Old Woman Creek National Estuarine Research Reserve is a dynamic freshwater estuary which experiences intermittent changes in water level over the course of a year. Small unmanned aerial systems (sUAS) are useful tools in monitoring changes as they are rapidly deployed, repeatable, and high-resolution. In this study, commercial quadcopters were paired with a red/green/near-infrared MAPIR Survey 3W camera to produce normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) maps to observe short-term changes at OWC. Orthomosaics were produced for flights on 8 days throughout 2018 and early 2019. The orthomosaics were calibrated to bottom-of-atmosphere reflectance using the Empirical Line Correction method, after which NDVI and NDWI maps were created. The NDVI maps allowed vegetation extent and density changes over time and for National Estuarine Reserve System (NERRS) Classification Codes to be applied to zones of interest. NDWI provided water extent at different water levels and when paired with LiDAR and bathymetric data yielded water volume and residence time estimates.

scholarly journals Mapeo de la recuperación de la vegetación quemada mediante la clasificación de índices espectrales pre- y post-incendio

2017 ◽  
pp. 37 ◽  
Author(s):  
M. A Peña ◽  
J. Ulloa
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Confusion Matrix ◽  
The State ◽  
Spectral Indices ◽  
Landsat Images ◽  
Full Data ◽  
Burnt Area ◽  
Water Index ◽  
Normalized Difference Water Index

<p>This study analyzed the state of recovery of the burnt vegetation in the National Park of Torres del Paine between December, 2011 and March, 2012. The calculation and comparison of the NVDI (normalized difference vegetation index) of the burnt area throughout a time series of 24 Landsat images acquired before, during and after the fire (2009- 2015), showed the temporal variation in the biomass levels of the burnt vegetation. The subsequent classification and comparison of the spectral indices: NDVI, NBR (normalized burnt ratio) and NDWI (normalized difference water index) on a full-data available and phenologically matched pre- and post-fire image pair (acquired in October 2009 and 2014), enabled to analyze and mapping the state of recovery of the burnt vegetation. The results show that the area of the lowest classes of all the spectral indices of the pre-fire date became the most dominant on the post-fire date. The pre- and post- fire NDVI class crossing by a confusion matrix showed that the highest and most prevailing pre-fire NDVI classes, mostly corresponding to hydromorphic forests and Andean scrubs, turned into the lowest class in 2014. The remaining area, comprising Patagonian steppe, reestablished its biomass levels in 2014, mostly exhibiting the same pre-fire NDVI classes. These results may provide guidelines to monitor and manage the regeneration of the vegetation impacted by this fire.</p>

scholarly journals Identificación de perturbaciones en el bosque húmedo tropical colombiano usando series temporales de imágenes satelitales Landsat mediante el algoritmo Landtrendr

2019 ◽  
pp. 25
Author(s):  
L. Hurtado ◽  
I. Lizarazo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Response ◽  
Short Wave ◽  
Forest Vegetation ◽  
Landsat Images ◽  
Spectral Bands

<p>Time series analysis of satellite images for detection of deforestation and forest disturbances at specific dates has been a subject of research over the last few years. There are many limitations to identify the exact date of deforestation due mainly to the large volume of data and the criteria required for its correct characterization. A further limitation in the analysis of multispectral time series is the identification of true deforestation considering that forest vegetation may undergo different changes over time. This study analyzes deforestation in a zone within the Colombian Amazon using the Normalized Difference Vegetation Index (NDVI) based on semestral median mosaics generated from Landsat images collected from 2000 to 2017. Several samples representing trends of change over the time series were extracted and classified according to their degree of change and persistence in the series, using four categories: (i) deforestation, (ii) degradation, (iii) forest plantation, and (iv) regeneration. Specific deforestation samples were analyzed in the same way using the soil-adjusted vegetation index (SAVI) to reduce the effect of spectral response variations due to soil reflectance changes. It is concluded that the two indices used, together with the near infrared (NIR) and short-wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by deforestation on forest vegetation is identified with acceptable accuracy. The analysis of time series using the Landtrendr algorithm confirmed a reliable change detection in each of the forest disturbance categories.</p>

scholarly journals Evaluation of PROBA-V Collection 1: Refined Radiometry, Geometry, and Cloud Screening

2018 ◽  
Vol 10 (9) ◽  
pp. 1375 ◽  
Author(s):  
Carolien Toté ◽  
Else Swinnen ◽  
Sindy Sterckx ◽  
Stefan Adriaensen ◽  
Iskander Benhadj ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Temporal Stability ◽  
Detection Algorithm ◽  
Short Wave ◽  
Absolute Calibration ◽  
Cloud Detection ◽  
Detection Scheme

PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Système Pour l’Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme; (ii) quantification of the effect of the reprocessing by comparing C1 to C0; and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfaces. Stepwise updates to the visible and near infrared (VNIR) absolute calibration in C0 and the application of degradation models to the SWIR calibration in C1 result in sudden changes between C0 and C1 Blue, Red, and NIR TOC reflectance in the first year, and more gradual differences for short-wave infrared (SWIR). Other changes result in some bias between C0 and C1, although the root mean squared difference (RMSD) remains well below 1% for top-of-canopy (TOC) reflectance and below 0.02 for the normalized difference vegetation index (NDVI). Comparison to METOP/AVHRR shows that the recent reprocessing campaigns on SPOT/VGT and PROBA-V have resulted in a more stable combined time series.

scholarly journals SPATIO-TEMPORAL ANALYSIS OF URBAN HEAT ISLAND IN MANDAUE CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 361-367
Author(s):  
A. M. Rejuso ◽  
A. C. Cortes ◽  
A. C. Blanco ◽  
C. A. Cruz ◽  
J. B. Babaan
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Temporal Analysis ◽  
High Heat ◽  
Temporal Variations ◽  
The Philippines ◽  
Water Index ◽  
Spatio Temporal

Abstract. Extensive urbanization alters the natural landscape as vegetation were replaced with infrastructures composed of materials with low albedo and high heat capacity often resulting to increase in land surface temperatures (LST). The present study focused on the spatial and temporal variations of LST in Mandaue City, one of the metropolitan cities in the Philippines that had undergone a rapid rate of urbanization over the past years. Climate Engine (CE), a cloud computing tool that processes satellite images, was used in this study. Preprocessed LST, normalized difference water index (NDWI), normalized difference vegetation index (NDVI), shortwave infrared (SWIR 1) and near-infrared (NIR) layers were directly downloaded from CE while the normalized difference built-up index (NDBI) maps were calculated. Time-series dataset of these indices were analyzed to determine the impacts of reduced vegetation cover and increased built-up areas on surface temperature from years 2013 to 2019. The spatial distribution of LST were analyzed using Univariate Local Moran’s I in GeoDa to identify hotspots within the city. Analysis results showed that the hotspots are barangays Tipolo (100%), Bakilid (100%), Ibabao-Estancia (93.5%), Alang-Alang (87.2%), Guizo (84.4%), Subangdaku (84.1%), and Centro (79.4%). The results indicated that there is a linear relationship between LST and NDBI (r = 0.659, p < 0.01) while an inverse relationship was observed between LST with NDVI (r = −0.527, p < 0.1) and NDWI (r = −0.620, p < 0.01).

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 An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

2021 ◽  
Vol 13 (4) ◽  
pp. 766
Author(s):  
Yuanmao Zheng ◽  
Qiang Zhou ◽  
Yuanrong He ◽  
Cuiping Wang ◽  
Xiaorong Wang ◽  
...  
Keyword(s):  
Urban Development ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Development Planning ◽  
Urban Land ◽  
Sustainable Urban Development ◽  
Land Use Patterns ◽  
Water Index ◽  
Nighttime Light

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

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 Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

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