scholarly journals MONITORING POST-DISASTER MANGROVE FOREST RECOVERIES IN LAWAAN-BALANGIGA, EASTERN SAMAR USING TIME SERIES ANALYSIS OF MOISTURE AND VEGETATION INDICES

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 295-301
Author(s):  
K. V. Ticman ◽  
S. G. Salmo III ◽  
K. E. Cabello ◽  
M. Q. Germentil ◽  
D. M. Burgos ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Positive Trend ◽  
Mangrove Forests ◽  
Moisture Index ◽  
Biophysical Parameters ◽  
Spatial Coverage

Abstract. The mangrove forests of Lawaan-Balangiga in Eastern Samar lost significant cover due to the Typhoon Haiyan that struck the region in 2013. The mangroves in the area have since shown signs of recovery in terms of growth and spatial coverage, but these widely varied with locations. This study aims to further examine the status of recovery of mangroves across different locations by analysing the time series trends of selected vegetation and moisture indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), and Normalized Difference Moisture Index (NDMI). These indices were extracted from Landsat 8 surface reflectance images, spanning 2014 to 2020, using Google Earth Engine (GEE). The time series analyses showed similar NDVI, MSAVI and NDMI values and trends after the 2013 typhoon event. The trend slopes also indicated high correlation (0.91 – 1.00) between and among the indices, with NDVI having the highest correlation with MSAVI (∼1.00). The study was able to corroborate the previous study on mangroves in Lawaan-Balangiga, by presenting positive trend results in the identified recovered areas. These trends, however, would still have to be validated by collecting and comparing biophysical parameters in the field. The next step of the research would be to identify the factors that contribute to the varying rates of recovery in the areas and to evaluate how this can affect the carbon sequestration rates of recovering mangroves.

scholarly journals Impact of COVID-19 Induced Lockdown on Environmental Quality in Four Indian Megacities Using Landsat 8 OLI and TIRS-Derived Data and Mamdani Fuzzy Logic Modelling Approach

2020 ◽  
Vol 12 (13) ◽  
pp. 5464 ◽  
Author(s):  
Sasanka Ghosh ◽  
Arijit Das ◽  
Tusar Kanti Hembram ◽  
Sunil Saha ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Environmental Quality ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fuzzy Ahp ◽  
Landsat 8 ◽  
Moisture Index ◽  
Biophysical Parameters ◽  
The World

The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM10 concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world.

scholarly journals Tracking Red Palm Mite Damage in the Western Hemisphere Invasion with Landsat Remote Sensing Data

Insects ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 627
Author(s):  
Jose Carlos Verle Rodrigues ◽  
Michael H. Cosh ◽  
E. Raymond Hunt ◽  
Gilberto J. de Moraes ◽  
Geovanny Barroso ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cocos Nucifera ◽  
Google Earth ◽  
Thematic Mapper ◽  
Western Hemisphere ◽  
Landsat 8 ◽  
Red Palm Mite

Red palm mites (Raoiella indica Hirst, Acari: Tenuipalpidae) were first observed in the western hemisphere on the islands and countries surrounding the Caribbean Sea, infesting the coconut palm (Cocos nucifera L.). Detection of invasive pests usually relies upon changes in vegetation properties as result of the pest activity. These changes may be visible in time series of satellite data records, such as Landsat satellites, which have been available with a 16-day repeat cycle at a spatial resolution of 30 m since 1982. Typical red palm mite infestations result in the yellowing of the lower leaves of the palm crown; remote sensing model simulations have indicated that this feature may be better detected using the green normalized difference vegetation index (GNDVI). Using the Google Earth Engine programming environment, a time series of Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus and Landsat 8 Operational Land Imager data was generated for plantations in northern and northeast Brazil, El Salvador, and Trinidad-Tobago. Considering the available studied plantations, there were little or no differences of GNDVI before and after the dates when red palm mites were first revealed at each location. A discussion of possible alternative approaches are discussed related to the limitations of the current satellite platforms.

scholarly journals Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing

2021 ◽  
Vol 42 (4) ◽  
pp. 2181-2202
Author(s):  
Taiara Souza Costa ◽  
◽  
Robson Argolo dos Santos ◽  
Rosângela Leal Santos ◽  
Roberto Filgueiras ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Coefficient ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Biophysical Parameters ◽  
Planting Dates ◽  
Maize Crop ◽  
Spatio Temporal

This study proposes to estimate the actual crop evapotranspiration, using the SAFER model, as well as calculate the crop coefficient (Kc) as a function of the normalized difference vegetation index (NDVI) and determine the biomass of an irrigated maize crop using images from the Operational Land Imager (OLI) and Thermal Infrared (TIRS) sensors of the Landsat-8 satellite. Pivots 21 to 26 of a commercial farm located in the municipalities of Bom Jesus da Lapa and Serra do Ramalho, west of Bahia State, Brazil, were selected. Sowing dates for each pivot were arranged as North and South or East and West, with cultivation starting firstly in one of the orientations and subsequently in the other. The relationship between NDVI and the Kc values obtained in the FAO-56 report (KcFAO) revealed a high coefficient of determination (R2 = 0.7921), showing that the variance of KcFAO can be explained by NDVI in the maize crop. Considering the center pivots with different planting dates, the crop evapotranspiration (ETc) pixel values ranged from 0.0 to 6.0 mm d-1 during the phenological cycle. The highest values were found at 199 days of the year (DOY), corresponding to around 100 days after sowing (DAS). The lowest BIO values occur at 135 DOY, at around 20 DAS. There is a relationship between ETc and BIO, where the DOY with the highest BIO are equivalent to the days with the highest ETc values. In addition to this relationship, BIO is strongly influenced by soil water availability.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

2020 ◽  
Vol 9 (4) ◽  
pp. 257 ◽  
Author(s):  
Kiwon Lee ◽  
Kwangseob Kim ◽  
Sun-Gu Lee ◽  
Yongseung Kim
Keyword(s):  
Open Source ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Urban Forestry ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

scholarly journals SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

2020 ◽  
Vol 11 (2) ◽  
pp. 94-110 ◽  
Author(s):  
Syed Riad Morshed Riad Morshed ◽  
Md. Abdul Fattah ◽  
Asma Amin Rimi ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Moisture Index ◽  
Type Transformation ◽  
Spatial Environment ◽  
Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

scholarly journals The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 139 ◽  
Author(s):  
Yingying Yang ◽  
Taixia Wu ◽  
Shudong Wang ◽  
Jing Li ◽  
Farhan Muhanmmad
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Distribution Data ◽  
Landsat 8 ◽  
Oxygen Balance

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

scholarly journals Greening and Browning Trends across Peru’s Diverse Environments

2020 ◽  
Vol 12 (15) ◽  
pp. 2418 ◽  
Author(s):  
Molly H. Polk ◽  
Niti B. Mishra ◽  
Kenneth R. Young ◽  
Kumar Mainali
Keyword(s):  
Time Series ◽  
Protected Area ◽  
Landscape Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Elevation Gradient ◽  
Positive Trend ◽  
Scientific Legacy ◽  
Alexander Von Humboldt ◽  
Moderate Resolution Imaging Spectroradiometer

If he were living today, Alexander von Humboldt would be using current technology to evaluate change in the Andes. Inspired by von Humboldt’s scientific legacy and the 2019 celebrations of his influence, we utilize a Moderate Resolution Imaging Spectroradiometer (MODIS)time-series vegetation index to ask questions of landscape change. Specifically, we use an 18-year record of Normalized Difference Vegetation Index (NDVI) data as a proxy to evaluate landscape change in Peru, which is well known for its high biological and ecological diversity. Continent-level evaluations of Latin America have shown sites with a positive trend in NDVI, or “greening” and “browning”, a negative trend in NDVI that suggests biophysical or human-caused reductions in vegetation. Our overall hypothesis was that the major biomes in Peru would show similar NDVI change patterns. To test our expectations, we analyzed the NDVI time-series with Thiel-Sen regression and evaluated Peru overall, by protected area status, by biome, and by biome and elevation. Across Peru overall, there was a general greening trend. By protected area status, surprisingly, the majority of greening occurred outside protected areas. The trends were different by biome, but there were hotspots of greening in the Amazon, Andean Highlands, and Drylands where greening dominated. In the Tropical Subtropical Dry Broadleaf Forest biome, greening and browning signals were mixed. Greening trends varied across the elevation gradient, switching from greening, to browning, and then back to greening as elevation increased. By biome and elevation, the results were variable. We further explored biome-specific drivers of greening and browning drawing on high-resolution imagery, the literature, and field expertise, much as we imagine von Humboldt might have approached similar questions of landscape dynamism.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

2019 ◽  
Vol 11 (21) ◽  
pp. 2479 ◽  
Author(s):  
Huiying Li ◽  
Mingming Jia ◽  
Rong Zhang ◽  
Yongxing Ren ◽  
Xin Wen
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Google Earth ◽  
Late Winter ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Ndvi Time Series ◽  
Google Earth Engine ◽  
Species Mapping ◽  
Sentinel 2

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

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