scholarly journals A Geo-spatial Classification Time Series Change Detection Using Remote Sensing Images of Seshachalam Region

2020 ◽  
pp. 167-175
Author(s):  
R.Sanjeeva Reddy ◽  
Anjan Babu G ◽  
A. Rama Mohan Reddy
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Forest Cover ◽  
Ecological Zone ◽  
Time Intervals ◽  
Technical Data ◽  
Image Differencing ◽  
Area Of Interest ◽  
Scientific Analysis ◽  
Forest Cover Changes

Time series is a scientific process of determining an ordered sequence of values of a variable within equally spaced time intervals. Mostly this is applied when looking at technical data and its influences on the neighboring surroundings. This type of scientific analysis that can be applied twofold. Firstly, it can be used to obtain a knowledge of the triggering forces and structure that produced the observed data. Additionally, it can be used to fit a model and to predict, monitor the area of interest. This scientific form of analysis can be applied in various sectors so long as the data can be measured over time. The following are some of the applications: Economic and sales forecasting, Crop Yield prediction, Forest cover changes, urbanization, among many other uses. In this analysis, we will focus on time series change detection using image differencing of a classified image and representing the outcome area in a bar graph. The area of study is Seshachalam Hills, Tirupathi an ecological zone in Andhra Pradesh, India.

Exploiting Sentinel-1 polarized data for the classification of areas and time intervals where coherently apply a change detection method for the retrieval of superficial soil moisture at the field scale

2021 ◽  
Author(s):  
Giulia Graldi ◽  
Alfonso Vitti
Keyword(s):  
Soil Moisture ◽  
Change Detection ◽  
Agricultural Area ◽  
Time Interval ◽  
Maximum Difference ◽  
Time Intervals ◽  
In Situ Data ◽  
Area Of Interest

<p>The estimation of superficial soil moisture is performed with a Change Detection (CD) method applied over an agricultural area in Spain, in the basin of the Duero river. The CD method is applied on Sentinel-1 SAR images over a time period of three years. For the period  and area of interest are available in situ soil moisture measurments of the REMEDHUS network belonging to the International Soil Moisture Network (ISMN). Two years of data are used for the calibration procedure (2018 and 2019), one year (2020) for validation purposes.<br>According to the Corine Land Cover classification of 2018, the agricultural area is mainly coverd by low vegetation. The backscatterd SAR signal is indeed modelled as the inchoerent sum of the volumetric contribution of the canopy, and the soil attenuated contribution.<br>The Sentinel-1 VH polarized band is used for the classification of the areas with homogeneous volumetric contribution, where the condition of constant vegetation contribution is respected in order to apply the CD method. Furthermore, those areas will be identified exploiting the bimodal distribution of the VH band histogram in the upper phase of the vegetative stage of the crop.<br>The soil roughness contribution to the superficial component of the backscattered signal couldn’t be neglected due to agricultural practices such as tillage and harvesting. Furthermore the data are processed at a very high resolution, in order to exploit the full spatial resolution of the SAR data. The VV polarized band will be used to identify the variations of the SAR signal due to changes in the soil roughness, and time periods with constant roughness contribution will be identified in order to apply the CD method. It is expected that the variations of the VV backscattering coefficient due to changes in soil roughness are higher than the ones caused by soil moisture changes, except for meteoric events.<br>The CD is thus applied on areas and time intervals where only soil moisture content is supposed to vary, and the maximum variation is calculated in each time interval. Finally, the calculation of the soil moisture is performed by scaling the maximum difference of SAR signal with the maximum difference of the in situ data.<br>In previous studies performed on the same area, a SAR vegetation index was used to classify homogeneous volumetric contribution, and soil rougness was neglected. Even if the trend of the solution fits well the precipitation events and the trend of the in situ data (RMSE=0.096m<sup>3</sup>/m<sup>3</sup>, R=0.583m<sup>3</sup>/m<sup>3</sup>), the results presented singularities. The above presented method for the superficial soil moisture calculation is expected to smooth the singularities present in the results of our previous studies.</p>

Landsat time series analysis for temperate forest cover change detection in the Sierra Madre Occidental, Durango, Mexico

2018 ◽  
Vol 73 ◽  
pp. 230-244 ◽  
Author(s):  
Alís Novo-Fernández ◽  
Shannon Franks ◽  
Christian Wehenkel ◽  
Pablito M. López-Serrano ◽  
Matthieu Molinier ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Change Detection ◽  
Temperate Forest ◽  
Forest Cover ◽  
Forest Cover Change ◽  
Sierra Madre Occidental ◽  
Series Analysis ◽  
Sierra Madre

SURVEY OF FOREST COVER CHANGES BY MEANS OF MULTIFRACTAL ANALYSIS

2018 ◽  
Vol 14 (1) ◽  
pp. 51-60
Author(s):  
Emilian DANILA ◽  
VALENTIN Hahuie ◽  
Puiu Lucian GEORGESCU ◽  
Luminița MORARU
Keyword(s):  
Multifractal Analysis ◽  
Forest Cover ◽  
Forest Cover Changes

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals The effects of armed conflict on forest cover changes across temporal and spatial scales in the Colombian Amazon

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Oscar V. Bautista-Cespedes ◽  
Louise Willemen ◽  
Augusto Castro-Nunez ◽  
Thomas A. Groen
Keyword(s):  
Armed Conflict ◽  
Forest Cover ◽  
Spatial Scales ◽  
Armed Forces ◽  
Peace Negotiations ◽  
Post Conflict ◽  
Forest Cover Changes ◽  
The Government ◽  
Colombian Amazon

AbstractThe Amazon rainforest covers roughly 40% of Colombia’s territory and has important global ecological functions. For more than 50 years, an internal war in the country has shaped this region. Peace negotiations between the government and the Revolutionary Armed Forces of Colombia (FARC) initiated in 2012 resulted in a progressive de-escalation of violence and a complete ceasefire in 2016. This study explores the role of different deforestation drivers including armed conflict variables, in explaining deforestation for three periods between 2001 and 2015. Iterative regression analyses were carried out for two spatial extents: the entire Colombian Amazon and a subset area which was most affected by deforestation. The results show that conflict variables have positive relationships with deforestation; yet, they are not among the main variables explaining deforestation. Accessibility and biophysical variables explain more variation. Nevertheless, conflict variables show divergent influence on deforestation depending on the period and scale of analysis. Based on these results, we develop deforestation risk maps to inform the design of forest conservation efforts in the post-conflict period.

scholarly journals Patterns and Drivers of Deforestation and Forest Degradation in Myanmar

2021 ◽  
Vol 13 (14) ◽  
pp. 7539
Author(s):  
Zaw Naing Tun ◽  
Paul Dargusch ◽  
DJ McMoran ◽  
Clive McAlpine ◽  
Genia Hill
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Local Governments ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Map Algebra ◽  
Change Detection Analysis ◽  
Detection Analysis

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

Inland wetland time-series digital change detection based on SAVI and NDWI indecies: Wadi El-Rayan lakes, Egypt

2020 ◽  
Vol 19 ◽  
pp. 100347 ◽  
Author(s):  
Asmaa Nasser Mohamed Eid ◽  
C.O. Olatubara ◽  
T.A. Ewemoje ◽  
Mohamed Talaat El-Hennawy ◽  
Haitham Farouk
Keyword(s):  
Time Series ◽  
Change Detection
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