scholarly journals Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires

2019 ◽  
Vol 12 (1) ◽  
pp. 83
Author(s):  
Saverio Vicario ◽  
Maria Adamo ◽  
Domingo Alcaraz-Segura ◽  
Cristina Tarantino
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Land Cover ◽  
Forest Fires ◽  
National Park ◽  
Vegetation Index ◽  
Hierarchical Prior ◽  
Vegetation Indexes ◽  
System 2 ◽  
Cloud Patterns

Vegetation index time series from Landsat and Sentinel-2 have great potential for following the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity. Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution, producing irregularity in time series of satellite images. We propose a Bayesian approach based on a harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior distribution that integrate information across the years. From the model, the mean and standard deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak’s day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to the model’s abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO Land Cover Classification System 2.

scholarly journals ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 223-227 ◽  
Author(s):  
Yi-Ta Hsieh ◽  
Shou-Tsung Wu ◽  
Chaur-Tzuhn Chen ◽  
Jan-Chang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Aerial Images ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Shadow Area ◽  
Vegetation Indexes ◽  
Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

scholarly journals Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

2021 ◽  
Vol 13 (19) ◽  
pp. 3951
Author(s):  
Kim André Vanselow ◽  
Harald Zandler ◽  
Cyrus Samimi
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Demographic Data ◽  
Mountainous Areas ◽  
Generalized Additive Mixed Models ◽  
Additive Mixed Models ◽  
Irregular Data

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

scholarly journals Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia

2020 ◽  
Vol 12 (1) ◽  
pp. 197
Author(s):  
Debbie Chamberlain ◽  
Stuart Phinn ◽  
Hugh Possingham
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Great Barrier Reef ◽  
National Park ◽  
Landsat 8 ◽  
Barrier Reef ◽  
Change Analysis ◽  
Estuarine Wetland ◽  
Open Forest

Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends divulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes.

Estimating the Vegetation phenology Time of recovery after a critical perturbation from Landsat time series within the frame of a Bayesian Harmonic model

2020 ◽  
Author(s):  
saverio vicario ◽  
Maria adamo ◽  
Cristina tarantino ◽  
palma blonda
Keyword(s):  
Time Series ◽  
National Park ◽  
Vegetation Index ◽  
Carbon Sink ◽  
Atmospheric Correction ◽  
Harmonic Model ◽  
Statistical Framework ◽  
Functional Attributes ◽  
The Stability ◽  
Index Time Series

<p>In Murgia Alta National Park the repeated fire perturb the stability of the environment and it s capacity to be a carbon sink. Thanks to the Landsat archive we can observed change in phenology t over the two decade (2000-2019). Unfortunately the phenological signal extracted from Landsat time series bear several uncertainties caused by missing data and error in atmospheric correction that makes difficult to reconstruct the trajectory of each pixel. Applying a Bayesian Harmonic model we can obtain not only expected values for the vegetation index time series but also confidence interval both for vegetation index and derived statistics. We took the phenological statistical framework of the Ecological Functional Attributes (EFA) to obtain annual statics and evaluate the time of recovery to obtain EFA with no statistical difference from the pre-perturbation time.</p><p>The results highlighted that only of subset of burned forest recover EFA values after 10 years of critical events. In particular the values of intra year variability tend to be higher due to the different trajectory of young shoots. The burned grassland time of recovery is much shorter given that the vast majority of pixel recover pre-event EFA in less than 4 year.</p>

scholarly journals Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Long Zhao ◽  
Pan Zhang ◽  
Xiaoyi Ma ◽  
Zhuokun Pan
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Information Extraction ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Land Resource ◽  
Spectral Angle Mapper ◽  
Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

scholarly journals Fluxo de CO2 e os Índices de Vegetação do Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brasil

2020 ◽  
Vol 13 (07) ◽  
pp. 3585
Author(s):  
Luana De Castro Pereira ◽  
Arnon Batista Nunes ◽  
Israel Lobato Rocha ◽  
Janeil Lustosa De Oliveira ◽  
Maria Letícia Stefany Monteiro Brandão ◽  
...  
Keyword(s):  
Carbon Balance ◽  
National Park ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Carbon Flow ◽  
Natural Environments ◽  
Area Index ◽  
Vegetation Indexes ◽  
Co2 Flow ◽  
Good Coverage

As emissões dos gases de efeito estufa na atmosfera trazem consequências para o meio ambiente e saúde pública. Logo, ambientes naturais, como as Florestas Nativas do Cerrado são essenciais no processo de equilíbrio de carbono, pela fixação do mesmo. Com o objetivo estimar o fluxo de CO2 com base em diferentes índices de vegetação do Parque Nacional das Nascentes do Rio Parnaíba (PNNRP), essa pesquisa, utilizou-se dos seguintes índices: Pré Processamento das Imagens (PPI), Índice de Vegetação por Diferença Normalizada – NDVI, Índice de Vegetação Fotossintético – PRI, Índice de Vegetação Ajustado ao Solo – SAVI, Índice de Área Foliar- IAF e CO2FLUX.  Referente ao Índice de Vegetação por Diferença Normalizada (NDVI), verificou-se que a maior parte da área PNNRP se encontra sob a vegetação considerada densa, sendo os  valores de SAVI encontrados próximos aos valores de NDVI, que pode estar relacionado a uma boa cobertura vegetal presente, indicando pouca influência das características do solo sob os índices de vegetação. A partir dos resultados encontrados através do IAF do PNNRP verificou que em áreas que os valores são maiores encontram-se as vegetações com o melhor desenvolvimento. Levando em conta os valores relacionados ao CO2Flux, IAF, NDVI e os demais índices, percebeu-se a capacidade do Parque no aproveitamento da luz solar e a realização da fotossíntese, além de abrigar uma vegetação saudável, podendo assim afirmar o grande potencial do PNNRP em armazenar carbono. Portanto, evidencia-se que o Parque Nacional das Nascentes do Rio Parnaíba possuí uma alto potencial de fluxo de carbono.   CO2 flow and vegetation indices of the Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brazil A B S T R A C TEmissions of greenhouse gases into the atmosphere have consequences for the environment and public health. Therefore, natural environments, such as the Cerrado's Native Forests are essential in the carbon balance process, due to its fixation. With the objective of estimating the CO2 flow based on different vegetation indexes of the Nascentes do Rio Parnaíba National Park (PNNRP), this research used the following indexes: Pre-Processing of Images (PPI), Vegetation Index by Difference Normalized - NDVI, Photosynthetic Vegetation Index - PRI, Soil Adjusted Vegetation Index - SAVI, Leaf Area Index - IAF and CO2FLUX. Regarding the Index of Vegetation by Normalized Difference (NDVI), it was found that most of the PNNRP area is under dense vegetation, with SAVI values found close to NDVI values, which may be related to good coverage present, indicating little influence of soil characteristics on vegetation indexes. From the results found through the IAF of the PNNRP verified that in areas with higher values are the vegetation with the best development. Taking into account the values related to CO2Flux, IAF, NDVI and other indexes, the Park's capacity to use sunlight and photosynthesis was observed, as well as to house healthy vegetation, thus confirming the great potential of PNNRP in storing carbon. Therefore, it is evident that the Parnaíba River National Park has a high carbon flow potential.Keywords: biomass, cerrado biome, carbon flow

scholarly journals THE USE OF SENTINEL 1/2 VEGETATION INDEXES WITH GEE TIME SERIES DATA IN DETECTING LAND COVER CHANGES IN THE SINOP NUCLEAR POWER PLANT CONSTRUCTION SITE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 701-706
Author(s):  
E. Çolak ◽  
M. Chandra ◽  
F. Sunar
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Land Cover ◽  
Nuclear Power ◽  
Vegetation Index ◽  
Developing Economies ◽  
Vegetation Indices ◽  
Series Data ◽  
Plant Construction

Abstract. Recently, the demand for nuclear power plants has been increasing in developing countries in line with global energy demands. Turkey, one of the developing economies, is also making plans for nuclear power generation since 1970. The Sinop Nuclear Power Plant was a planned nuclear plant located in the Turkey's most northern point in an area where 99% of the land is forest, in Sinop Peninsula. If disputes are resolved and its construction continues, the plant is expected to be put into service in 2028. On the other hand, due to the construction of the nuclear power plant, the land cover in and around the plant site has changed, potentially causing major environmental changes. As an example, more than 650000 trees have been cut down so far for the construction of a nuclear power plant, which may have a negative impact on the region's ecological balances by endangering biodiversity and causing ecological damage. The aim of this study is to detect changes in forest areas from the start of nuclear power plant construction through December 2020 using Sentinel 1 SAR and Sentinel 2 optical time series images. For this purpose, different radar and optical vegetation indices such as Modified Radar Vegetation Index (mRVI), Modified Radar Forest Degradation Index (mRFDI), and Normalized Difference Vegetation Index (NDVI) were applied using Google Earth Engine (GEE) Sentinel 1/2 satellite time series for 2015–2020 period. As a result, the indices used were found to yield findings consistent with the reported negative land cover change. In addition, correlation analysis were made between the radar vegetation indices used and a very high negative correlation (−0.99) was found. The annual distributions of the values of the three indices used were statistically evaluated using boxplots.

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