scholarly journals A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

2021 ◽  
Vol 936 (1) ◽  
pp. 012038
Author(s):  
Benedict ◽  
Lalu Muhamad Jaelani
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Data ◽  
Temporal Analysis ◽  
Google Earth ◽  
Vegetation Density ◽  
Angle Sensor ◽  
Study Results ◽  
Google Earth Engine

Abstract Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

scholarly journals HazMapper: a global open-source natural hazard mapping application in Google Earth Engine

2021 ◽  
Vol 21 (5) ◽  
pp. 1495-1511
Author(s):  
Corey M. Scheip ◽  
Karl W. Wegmann
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Volcanic Eruptions ◽  
Relative Difference ◽  
Google Earth ◽  
Satellite Networks ◽  
Pyroclastic Flows ◽  
Hazard Mapping ◽  
Polar Regions ◽  
Google Earth Engine

Abstract. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.

scholarly journals Estimating Proportion of Vegetation Cover at the Vicinity of Archaeological Sites Using Sentinel-1 and -2 Data, Supplemented by Crowdsourced OpenStreetMap Geodata

2020 ◽  
Vol 10 (14) ◽  
pp. 4764 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hazard Analysis ◽  
Archaeological Site ◽  
Google Earth ◽  
Archaeological Sites ◽  
Radar Images ◽  
Optical Images ◽  
Sentinel 2

Monitoring vegetation cover is an essential parameter for assessing various natural and anthropogenic hazards that occur at the vicinity of archaeological sites and landscapes. In this study, we used free and open access to Copernicus Earth Observation datasets. In particular, the proportion of vegetation cover is estimated from the analysis of Sentinel-1 radar and Sentinel-2 optical images, upon their radiometric and geometric corrections. Here, the proportion of vegetation based on the Radar Vegetation Index and the Normalized Difference Vegetation Index is estimated. Due to the medium resolution of these datasets (10 m resolution), the crowdsourced OpenStreetMap service was used to identify fully and non-vegetated pixels. The case study is focused on the western part of Cyprus, whereas various open-air archaeological sites exist, such as the archaeological site of “Nea Paphos” and the “Tombs of the Kings”. A cross-comparison of the results between the optical and the radar images is presented, as well as a comparison with ready products derived from the Sentinel Hub service such as the Sentinel-1 Synthetic Aperture Radar Urban and Sentinel-2 Scene classification data. Moreover, the proportion of vegetation cover was evaluated with Google Earth red-green-blue free high-resolution optical images, indicating that a good correlation between the RVI and NDVI can be generated only over vegetated areas. The overall findings indicate that Sentinel-1 and -2 indices can provide a similar pattern only over vegetated areas, which can be further elaborated to estimate temporal changes using integrated optical and radar Sentinel data. This study can support future investigations related to hazard analysis based on the combined use of optical and radar sensors, especially in areas with high cloud-coverage.

scholarly journals Temporal Patterns in Illumination Conditions and Its Effect on Vegetation Indices Using Landsat on Google Earth Engine

2020 ◽  
Vol 12 (2) ◽  
pp. 211 ◽  
Author(s):  
Pablo Martín-Ortega ◽  
Luis Gonzaga García-Montero ◽  
Nicole Sibelet
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Temporal Patterns ◽  
Google Earth ◽  
Surface Reflectance ◽  
Enhanced Vegetation Index ◽  
Google Earth Engine ◽  
Level 1 ◽  
Irregular Topography

Vegetation indices (VI) describe vegetation structure and functioning but they are affected by illumination conditions (IC). Moreover, the fact that the effect of the IC on VI can be stronger than other biophysical or seasonal processes is under debate. Using Google Earth Engine and the latest Landsat Surface Reflectance level 1 data, we evaluated the temporal patterns of IC and two VI, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) in a mountainous tropical forest during the years 1984–2017. We evaluated IC and VI at different times, their relationship with the topography and the correlations between them. We show that IC is useful for understanding the patterns of variation between VI and IC at the pixel level using Landsat sensors. Our findings confirmed a strong correlation between EVI and IC and less between NDVI and IC. We found a significant increase in IC, EVI, and NDVI throughout time due to an improvement in the position of all Landsat sensors. Our results reinforce the need to consider IC to interpret VI over long periods using Landsat data in order to increase the precision of monitoring VI in irregular topography.

scholarly journals Multi-Year Mapping of Disturbance and Reclamation Patterns over Tronox’s Hillendale Mine, South Africa with DBEST and Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 760
Author(s):  
Sifiso Xulu ◽  
Philani T. Phungula ◽  
Nkanyiso Mbatha ◽  
Inocent Moyo
Keyword(s):  
South Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Abandoned Mines ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Petroleum Resources ◽  
Google Earth Engine ◽  
The Government

This study was devised to examine the pattern of disturbance and reclamation by Tronox, which instigated a closure process for its Hillendale mine site in South Africa, where they recovered zirconium- and titanium-bearing minerals from 2001 to 2013. Restoring mined-out areas is of great importance in South Africa, with its ominous record of almost 6000 abandoned mines since the 1860s. In 2002, the government enacted the Mineral and Petroleum Resources Development Act (No. 28 of 2002) to enforce extracting companies to restore mined-out areas before pursuing closure permits. Thus, the trajectory of the Hillendale mine remains unstudied despite advances in the satellite remote sensing technology that is widely used in this field. Here, we retrieved a collection of Landsat-derived normalized difference vegetation index (NDVI) within the Google Earth Engine and applied the Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to examine the progress of vegetation transformation over the Hillendale mine between 2001 and 2019. Our results showed key breakpoints in NDVI, a drop from 2001, reaching the lowest point in 2009–2011, with a marked recovery pattern after 2013 when the restoration program started. We also validated our results using a random forests strategy that separated vegetated and non-vegetated areas with an accuracy exceeding 78%. Overall, our findings are expected to encourage users to replicate this affordable application, particularly in emerging countries with similar cases.

scholarly journals Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

2022 ◽  
Vol 14 (2) ◽  
pp. 284
Author(s):  
Changchun Li ◽  
Weinan Chen ◽  
Yilin Wang ◽  
Yu Wang ◽  
Chunyan Ma ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Period ◽  
Google Earth ◽  
Henan Province ◽  
Sar Images ◽  
Modis Ndvi ◽  
Optical Images ◽  
Google Earth Engine

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

scholarly journals Remote Sensing of Shaded Area in Vineyards

2005 ◽  
Vol 15 (4) ◽  
pp. 859-863 ◽  
Author(s):  
Lee Johnson ◽  
Thibaut Scholasch
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Image Data ◽  
Crop Coefficient ◽  
Photon Flux ◽  
Optical Remote Sensing ◽  
Study Results ◽  
Infrared Spectral

Airborne multispectral image data were compared with intercepted photosynthetic photon flux (PPF) in commercial winegrape (Vitis vinifera) vineyards of Napa Valley, Calif. An empirically based calibration was applied to transform raw image pixel values to surface reflectance. Reflectance data from the red and near-infrared spectral regions were combined into a normalized difference vegetation index. Strong linear response was observed between the vegetation index and PPF interception ranging from 0.15 to 0.50. Study results suggest the possibility of using optical remote sensing to monitor and map vineyard shaded area, thus providing spatially explicit input to water budget models that invoke evapotranspiration crop coefficient based calculations.

scholarly journals Uso e ocupação do solo do município de novo progresso no Estado do Pará-Brasil

2021 ◽  
Vol 10 (1) ◽  
pp. e51210112060
Author(s):  
Raimara Reis do Rosário ◽  
Mateus Trindade Barbosa ◽  
Francimary da Silva Carneiro ◽  
Merilene do Socorro Silva Costa
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
De Novo ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Google Earth Engine ◽  
Para Determinar

O objetivo foi analisar o processo de uso e ocupação do solo do município de Novo Progresso no Estado do Pará, interligando-o com as atividades de maior importância econômica desenvolvidas nesta região. Utilizou-se o shapefile de limite do município de Novo Progresso na plataforma online Google Earth Engine (GEE), que disponibilizou um mosaico de imagens orbitais, do satélite Landsat-8/OLI-TIRS, referentes ao ano de 2019. O processo de classificação foi feito a partir do Code Editor do GEE, utilizando um Índice espectral de vegetação para auxiliar a classificação (Normalized Difference Vegetation Index – NDVI). Foi utilizado o Software QGis 3.10.6 para elaborar os mapas de localização do município e o de classificação de uso e cobertura do solo. Os dados foram tabulados em planilhas para determinar as taxas de crescimento do período analisado. Para realizar a avaliação da confiabilidade da classificação foi utilizado o método de Exatidão Global e o Índice Kappa. Foi possível identificar que no ano de 2019, houve a incidência de 3.064.396,65 ha (80,3%) de floresta densa, uma área de 496.104,07 ha (13,0%) com solo exposto, 248.052,03 ha (6,5%) de floresta secundária, e apenas 7.632,37 ha (0,2%) com predominância de hidrografia, totalizando uma área de 3.816.185,13 ha.  As áreas que encontram-se com o solo exposto não estão diretamente relacionadas com o crescimento populacional, mas sim a forma como é estabelecido o uso do solo, com base nas principais atividades desenvolvidas na região considerando que a lógica produtiva ocorre de forma desordenada, não respeitando os critérios de desenvolvimento sustentável.

scholarly journals Identifying Drought Status in Duhok Governorate (Iraqi Kurdistan Region) from 1998 through 2012 using Landsat Time Series Dataset

2020 ◽  
Vol 1 (1) ◽  
pp. 17-23
Author(s):  
Heman Gaznayee ◽  
Ayad Al-Quraishi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Drought Severity ◽  
Kurdistan Region ◽  
Water Index ◽  
Iraqi Kurdistan ◽  
Study Results ◽  
Normalized Difference Water Index

Drought is a natural hazard that has a significant impact on the various aspects (i.e., economic, agricultural, environmental, and social). This study was carried out to evaluate drought severity and frequency during the growing season (April month) in Duhok Governorate (DUG), the Iraqi Kurdistan Region (IKR), for the period from 1998 through 2012 based on Landsat-based spectral indices. In this study, 15 mosaics assembled for 15 years consist of two scenes of Landsat time series, in a total of 30 TM and ETM+ images (WRS2: 170/34 & 170/35) acquired in 1998 to 2012. Annual precipitation data were collected from 18 meteorological stations distributed in the (DUG) for the study period. Drought status was investigated using the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI2), and Normalized Difference Water Index (NDWI). The study results showed an increase in drought severity and frequency in the (DUG) during the fifty years, particularly in 2000 and 2008. Whereas, the NDVI-based vegetation cover area has been reduced by 21.5% and 50.2% in 2000 and 2008, respectively. Additionally, the lowest values of the MSAVI2 (0.012 and 0.266) occurred in 2000 and 2008. As a result, the percentage of the vegetation cover reduction was 14.0% and 23.9%, respectively. Moreover, drop-in precipitation averages have occurred in those two drought years 2000 and 2008, as well as a significant reduction in the vegetation cover. On the other side, the most significant shrinkage in Duhok Dam (DUD) was by 1.13, 1.44, and 1.36 km2 in 2007, 2008, and 2009. It can be concluded that there are increasing drought episodes in the last two decades, declining in the water body surface area, and decreasing the precipitation averages in DUG from 1998 through 2012.

scholarly journals Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

2020 ◽  
Vol 12 (16) ◽  
pp. 6497
Author(s):  
Zhengrong Liu ◽  
Huanjun Liu ◽  
Chong Luo ◽  
Haoxuan Yang ◽  
Xiangtian Meng ◽  
...  
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Regional Scale ◽  
Google Earth ◽  
Spatial And Temporal Patterns ◽  
Large Area ◽  
Disaster Monitoring ◽  
Google Earth Engine

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Google Earth Engine provides the possibility of quickly extracting the disaster range over a large area. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (RNDVI_TM(i)), the NDVI median phenology standardization model (RNDVI_AM(i)(j)), and the NDVI median spatiotemporal standardization model (RNDVI_ZM(i)(j)). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for RNDVI_TM(i), RNDVI_AM(i), and (j)RNDVI_ZM(i)(j), respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and was applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

scholarly journals Analisis Fenomena Pulau Panas Perkotaan Kota Bandung Menggunakan Google Earth Engine

2019 ◽  
Author(s):  
Muhammad Malik Ar-Rahiem ◽  
Muhamad Riza Fakhlevi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Google Earth Engine

Pulau Panas Perkotaan (Urban Heat Island) adalah fenomena antropogenik akibat pengaruh urbanisasi. Kawasan perkotaan yang terbangun memiliki temperatur yang lebih hangat dibandingkan kawasan sekitarnya. Fenomena Pulau Panas Perkotaan di Kota Bandung diteliti menggunakan data Suhu Permukaan Tanah (Land Surface Temperature) yang diakuisisi dari satelit Landsat 8. Lima tahun data satelit dianalisis menggunakan piranti daring Google Earth Engine untuk menganalisis variasi temporal Pulau Panas Perkotaan di Kota Bandung dan sekitarnya. Suhu yang diakuisisi dari satelit dikonversi menjadi estimasi suhu permukaan dengan mempertimbangkan nilai Normalized Difference Vegetation Index. Hasil dari penelitian ini adalah peta persebaran rata-rata dan median suhu permukaan di Cekungan Bandung tahun 2013-2018, serta grafik seri waktu suhu permukaan di 3 jenis tata guna lahan yang mewakili daerah kota (sekitar Jalan Sudirman), hutan kota (Hutan Babakan Siliwangi), dan hutan (Tamah Hutan Raya Djuanda). Suhu rata-rata Kota Bandung pada tahun 2013-2018 adalah 26,93 oC (median seluruh data) dan 25,57oC (rata-rata seluruh data). Sementara perbandingan berdasarkan tata guna lahan; daerah kota memiliki suhu permukaan rata-rata 27,30 oC, daerah hutan kota memiliki suhu 21,31oC, dan daerah hutan memiliki suhu 18,60oC. Peta persebaran suhu panas permukaan dari citra Landsat 8 menunjukkan bahwa daerah hutan secara konsisten memiliki suhu paling rendah, diikuti dengan hutan kota, dan kemudian daerah kota menjadi area yang paling panas dengan suhu maksimal hingga 33,73oC. Penggunaan Google Earth Engine yang berbasis komputasi awan sangat memudahkan pengolahan data citra satelit dalam jumlah besar yang selama ini tidak memungkinkan dilakukan dengan cara konvensional (mengunduh dan memproses di komputer).

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