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.

Recurrence Quantification Techniques of vegetation time-series indices in semiarid grasslands

2020 ◽  
Author(s):  
Andres Almeida-Ñauñay ◽  
Rosa M. Benito ◽  
Miguel Quemada ◽  
Juan Carlos Losada ◽  
Ana Maria Tarquis
Keyword(s):  
Time Series ◽  
Recurrent Events ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Properties ◽  
Distinct Pattern ◽  
Climatic Variables ◽  
Recurrence Quantification ◽  
Semi Arid

&lt;p&gt;Grassland ecosystems are extremely complex and set up intricate structures, whose characteristics and dynamic properties are greatly influenced by climate and meteorological patterns. Climate change and global warming are factors that could impact negatively in the quality and productivity of these ecosystems.&lt;/p&gt;&lt;p&gt;Remote sensing techniques have been demonstrated as a powerful tool for monitoring extensive areas. In this study, two semi-arid grassland plots were selected in the centre of Spain. This region is characterized by low precipitation and moderate productivity per unit. Through scientific research, spectral vegetation indices (VIs) have been developed to characterize vegetation cover. The most common VI is the Normalized Difference Vegetation Index (NDVI). However, in vegetation scarcity conditions, bare soil reflectance is increased, and the feasibility of NDVI is reduced. This study aims to perform a method to compare soil and agro-climatic variables effect on vegetation time-series indices.&lt;/p&gt;&lt;p&gt;The construction of the time series was based on multispectral images of MODIS TERRA (MOD09A1.006) product acquired from 2002 till 2018. Three pixels with a temporal resolution of 8 days and a spatial resolution of 500 x 500 m were chosen in each area. To estimate and analyse VIs series, Red (620-670 nm) and Near Infrared (841-876 nm) channels were extracted and filtered by the quality of pixel. All spectral bands showed statistically significant differences confirming that both areas presented different soil properties. Moreover, average annual precipitation was different in each area of study.&lt;/p&gt;&lt;p&gt;NDVI calculation is only based on NIR and RED bands. To improve the estimation of vegetation in semi-arid areas, several indices have been developed to minimize the soil effect. Each one of them incorporates soil influence in a different way, i.e., Soil Adjusted Vegetation Index (SAVI) adds a constant soil adjustment factor (L), whereas, MSAVI, incorporate an L variable and dependant on soil characteristics.&lt;/p&gt;&lt;p&gt;Recurrence plots (RP) and recurrence quantification analysis (RQA) were computed to characterize the influence of agro-climatic variables in vegetation index dynamics. Characterization was based on various RQA measures, such as Determinism (DET), average diagonal length (LT) or entropy (ENT).&lt;/p&gt;&lt;p&gt;Our results showed different RPs depending on the area, VI utilized and precipitation. MSAVI patterns were further distinct, meanwhile, NDVI showed a noisy pattern. LT values in MSAVI were higher than in SAVI implying that MSAVI recurrent events are much longer than SAVI. Simultaneously, LT and DET values in ZSO, with a higher rain, were above ZEA values in MSAVI.&lt;/p&gt;&lt;p&gt;This indicates that incorporating more detailed information of soil and precipitation reinforce vegetation index estimation and allow to obtain a more distinct pattern of the time series. Therefore, in arid-semiarid grasslands, they should be considered.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;ACKNOWLEDGEMENTS&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish &lt;em&gt;Ministerio de Ciencia Innovaci&amp;#243;n y Universidades&lt;/em&gt; of Spain and the funding from the Comunidad de Madrid (Spain) and Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330, are highly appreciated.&lt;/p&gt;

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

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 Measurement Method for Height-Independent Vegetation Indices Based on an Active Light Source

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1830
Author(s):  
Yongqian Ding ◽  
Yizhuo Jiang ◽  
Hongfeng Yu ◽  
Chuanlei Yang ◽  
Xueni Wu ◽  
...  
Keyword(s):  
Light Source ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Application Rate ◽  
Test Point ◽  
Central Wavelength ◽  
Nitrogen Application Rate

A coefficient CW, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices (VIs). Two criteria for designing the spectrometer based on an active light source were proposed to keep CW constant. A designed spectrometer, which was equipped with an active light source, adopting 730 and 810 nm as the central wavelength of detection wavebands, was used to test the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) in wheat fields with two nitrogen application rate levels (NARLs). Twenty test points were selected in each kind of field. Five measuring heights (65, 75, 85, 95, and 105 cm) were set for each test point. The mean and standard deviation of the coefficient of variation (CV) for NDVI in each test point were 3.85% and 1.39% respectively, the corresponding results for RVI were 2.93% and 1.09%. ANOVA showed the measured VIs possessed a significant ability to discriminate the NARLs and had no obvious correlation with the measurement heights. The experimental results verified the feasibility and validity of the method for measuring height-independent VIs.

scholarly journals Performance Evaluation of Newly Proposed Seaweed Enhancing Index (SEI)

2019 ◽  
Vol 11 (12) ◽  
pp. 1434 ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z. Zaidi ◽  
Muhammad Abdullah
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Extensive Field ◽  
Reference Index ◽  
Almost All ◽  
Shortwave Infrared ◽  
Better Than

Seaweed is a valuable coastal resource for its use in food, cosmetics, and other items. This study proposed new remote sensing based seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave-infrared (SWIR) of Landsat 8 satellite data. Nine Landsat 8 satellite images of years 2014, 2016, and 2018 for the January, February, and March months were utilized to test the performance of SEI. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using the SEI, normalized difference vegetation index (NDVI), and floating algae index (FAI). Seaweed locations recorded during a field survey on February 26, 2014, were used to determine threshold values for all three indices. The accuracy of SEI was compared with NDVI while placing FAI as the reference index. The accuracy of NDVI and SEI were assessed by matching their spatial extent of seaweed cover with FAI enhanced seaweed area. SEI images of January 2016, February 2018, and March 2018 enhanced less than 50 percent of the corresponding FAI total seaweed areas. However, on these dates the NDVI performed very well, matching more than 95 percent of FAI seaweed coverage. Except for these three times, the performance of SEI in the remaining six images was either similar to NDVI or even better than NDVI. SEI enhanced 99 percent of FAI seaweed cover on January 2018 image. Overall, seaweed area not covered by FAI was greater in SEI than NDVI in almost all images, which needs to be further explored in future studies by collecting extensive field information to validate SEI mapped additional area beyond the extent of FAI seaweed cover. Based on these results, in the majority of the satellite temporal images selected for this study, the performance of the newly proposed index—SEI, was found either better than or similar to NDVI.

scholarly journals Spatio-Temporal Variation of Vegetation in Godavari Eastern Delta

2019 ◽  
pp. 1-7
Author(s):  
G. Kishore Kumar ◽  
M. Raghu Babu ◽  
A. Mani ◽  
M. Matin Luther ◽  
V. Srinivasa Rao
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
Satellite Image ◽  
Ground Truth ◽  
Likelihood Method ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Wide Range

Spatial variability in land use changes creates a need for a wide range of applications, including landslide, erosion, land planning, global warming etc. This study presents the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI) in Godavari eastern delta. Four spectral indices were investigated in this study. These indices were NIR (red and near infrared) based NDVI, green and NIR based GVI (Green Vegetation Index), red and NIR based soil adjusted vegetation index (SAVI), and red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for 2011-12 kharif, rabi and 2016-17 kharif, rabi of Godavari eastern delta. Different threshold values of NDVI are used for generating the false colour composite of the classified objects. For this purpose, supervised classification is applied to Landsat images acquired in 2011-12 and 2016-17. Image classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from satellite images of 2011-12 and 2016-17. There was 11% and 30% increase in vegetation during kharif and rabi seasons from 2011-12 to 2016-17. The vegetation analysis can be used to provide humanitarian aid, damage assessment in case of unfortunate natural disasters and furthermore to device new protection strategies.

scholarly journals NDVI AND CUSTOMIZED CNN FOR LAND COVER SATELLITE IMAGE CLASSIFICATION

2021 ◽  
Vol 9 (06) ◽  
pp. 205-209
Author(s):  
Amee Daiya ◽  
◽  
Dharmesh Bhalodiya ◽  
Keyword(s):  
Image Classification ◽  
Domain Knowledge ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Satellite Image ◽  
Deep Learning Algorithm ◽  
Spectral Bands

The efficient and the simplest deep learning algorithm of image classification is Convolutional Neural Network (CNN). In this paper we developed a customized CNN architecture for the classification of multi-spectral images from SAT-4 datasets. The sets Near-Infrared (NIR) band information as it can sense vegetation health. The domain knowledge of Normalized Difference Vegetation Index (NDVI) motivated us to utilize Red and NIR spectral bands together in the second level of experimentation for the classification.

scholarly journals Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series

2020 ◽  
Vol 12 (3) ◽  
pp. 478 ◽  
Author(s):  
Yuzhu Hao ◽  
Zhenjie Chen ◽  
Qiuhao Huang ◽  
Feixue Li ◽  
Beibei Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Use Change ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Three Dimensions ◽  
Global Changes ◽  
Landsat Images ◽  
Object Level

High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.

scholarly journals Performance Assessment of Newly Developed Seaweed Enhancing Index

2018 ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z. Zaidi ◽  
Muhammad Abdullah
Keyword(s):  
Coastal Waters ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Field Surveys ◽  
Spectral Bands ◽  
Breeding Grounds ◽  
Shortwave Infrared ◽  
Better Than

Seaweeds are regarded as one of the valuable coastal resources because of their usage in human food, cosmetics, and other industrial items. They also play a significant role in providing nourishment, shelter, and breeding grounds for fish and many other sea species. This study introduces a newly developed seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave infrared (SWIR) of Landsat 8 satellite data. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using SEI, and its performance was compared with other commonly used indices - Normalized Difference Vegetation Index (NDVI) and Floating Algae Index (FAI). The accuracy of the mapping results obtained from SEI, NDVI, and FAI was checked with field verified seaweed locations. The purpose of the field surveys was to validate the results of this study and to evaluate the performance of SEI with NDVI and FAI. The performance of SEI was found better than NDVI and FAI in enhancing submerged patches of the seaweed pixels what other indices failed to do.

Sign in / Sign up

Export Citation Format

Share Document