Normalized difference vegetation index analysis of forest cover change detection in Paro Dzongkhag, Bhutan

2022 ◽  
pp. 417-425
Author(s):  
Sangey Pasang ◽  
Rigzin Norbu ◽  
Suren Timsina ◽  
Tshering Wangchuk ◽  
Petr Kubíček
Keyword(s):  
Change Detection ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Forest Cover Change ◽  
Index Analysis

scholarly journals Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning

2020 ◽  
Vol 5 (3) ◽  
pp. 335
Author(s):  
R. Sanjeeva Reddy ◽  
G. Anjan Babu ◽  
A. Rama Mohan Reddy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Change Detection ◽  
Spatial Data ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Support Vector ◽  
Forest Cover Change

Spatial data classification is famous over recent years in order to extract knowledge and insights into the data. It occurs because vast experimentation was used with various classifiers, and significant improvement was examined in accuracy and performance. This study aimed to analyze forest cover change detection using machine learning. Supervised and unsupervised learning methods were used to analyze spatial data. A Vector machine was used to support the supervised learning, and a neural network method was used to support unsupervised learning. The Normalized Difference Vegetation Index (NDVI) was used to identify the bands and extract pixel information relevant to the vegetation. The supervised method shows better results because of its robust performance and better analysis of spatial data classification using vegetation index. The proposed system experimentation was implemented by analyzing the results obtained from Support Vector Machine (SVM) and NN (Neural Network) methods. It is demonstrated in the results that the use of NDVI mainly enhances the performance and increases the classifier's accuracy to a greater extent. Keywords: Spatial data; Normalized Difference Vegetation Index; NDVI;Vegetation index, Support Vector Machine; Neural Network; Forest Cover Change Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Remote Sensing

2015 ◽  
Author(s):  
Frank D. Eckardt
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Laser Pulses ◽  
Global Scale ◽  
Dynamic Topography ◽  
Forest Cover Change ◽  
Atmospheric Remote Sensing

This article on remote sensing or earth observation focuses on mapping and monitoring systems that produce global-scale data sets which are easily accessible to the wider public. It makes particular reference to low-earth-orbiting remote sensing platforms and sensors and associated image archives such as provided by the Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) programs. It also draws attention to handheld space photography, synthetic aperture radar (SAR), and the high-spatial-resolution capability obtained from the commercial remote sensing sector. This entry examines applications that are of global interest and are facilitated through image and data portals. Particular emphasis is placed on products such as the normalized difference vegetation index, real-time fire mapping, forest cover change, geomorphology, and global elevation data as well as actual true- and false-color imagery. All of these can be readily imported as shape or raster files into a Geographic Information System (GIS). Key papers dealing with the global monitoring of the biosphere, dynamic topography, and gravity are being cited. Special emphasis is placed on current capabilities in monitoring recent and ongoing changes in the tropics as well as Arctic and Antarctic environment. Numerous remote sensing systems capture the state and dynamics of rainforests, ice caps, glaciers, and shelf and sea ice, some of which are available in near-real-time trend analysis. Not all sensors produce images; some measure passive microwaves, send laser pulses, or detect small fluctuations in gravitational attraction. Nevertheless, all instruments measure changes in earth’s surface state, indicative of seasonal cycles and long-term trends as well as human impact. This article also makes reference to historic developments, social benefits, and ethical considerations in remote sensing as well as the modern role of aerial photography and airborne platforms. Most people will never get to see a satellite or its instruments, they might not even get to see the available data or imagery, but these systems are directly informing the masses or indirectly shaping the perception of a changing and dynamic world. Future revisions to this article will consider oceanographic and atmospheric remote sensing capabilities.

Simulation Pattern of Forest Cover Change in Onigambari Reserve, Ibadan, Oyo State, Nigeria using spectral vegetation index and Markov chain Techniques

2021 ◽  
Author(s):  
Anthony Tobore ◽  
Khadijat Alabi ◽  
Ganiyu Oyerinde ◽  
Bolarinwa Senjobi
Keyword(s):  
Markov Chain ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Sustainable Forest Management ◽  
Decision Makers ◽  
Landsat 8 ◽  
Forest Cover Change ◽  
Sparse Vegetation ◽  
Landsat 7

Abstract Forest cover change (FCC) varies from region to region and is thus considered as one of the drivers of climate change. This study identified the pattern of the FCC for the years 2010 and 2020 using spectral vegetation index and Markov chain Techniques. The Markov chain (MC) and Cellular automata (CA) methods were utilized to simulate the forest cover map for the year 2030. The spectral vegetation index of Landsat 7 Enhanced thematic mapper plus (ETM+) and Landsat 8 Operational land images (OLI) were used to assess the forest cover loss for the year 2010 and 2020 focusing on the Normalized difference vegetation index, (NDVI), Green normalized difference vegetation index (GNDVI) and Difference vegetation index (DVI). Based on the validation result, the accuracy of the forest cover simulation model is more than 75 percent (%). The simulation result shows that if the current deforestation and encroachment continues, the forest cover will continue to be endangered and thus leading to a decrease in dense forest, plantation, and sparse vegetation by 20.9%, 16.1%, and 20% respectively. This study will be helpful for planners and decision-makers in ensuring sustainable forest management.

scholarly journals Effects of Windthrows on Forest Cover, Tree Growth and Soil Characteristics in Drought-Prone Pine Plantations

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 817
Author(s):  
Jesús Julio Camarero ◽  
Michele Colangelo ◽  
Antonio Gazol ◽  
Manuel Pizarro ◽  
Cristina Valeriano ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Pinus Halepensis ◽  
Soil Characteristics ◽  
Tree Cover ◽  
Mediterranean Forests ◽  
Soil Microbial Community Structure ◽  
Soil Features ◽  
Disturbance Intensity

Windstorms are forest disturbances which generate canopy gaps. However, their effects on Mediterranean forests are understudied. To fill that research gap, changes in tree, cover, growth and soil features in Pinus halepensis and Pinus sylvestris plantations affected by windthrows were quantified. In each plantation, trees and soils in closed-canopy stands and gaps created by the windthrow were sampled. Changes in tree cover and radial growth were assessed by using the Normalized Difference Vegetation Index (NDVI) and dendrochronology, respectively. Soil features including texture, nutrients concentration and soil microbial community structure were also analyzed. Windthrows reduced tree cover and enhanced growth, particularly in the P. halepensis site, which was probably more severely impacted. Soil characteristics were also more altered by the windthrow in this site: the clay percentage increased in gaps, whereas K and Mg concentrations decreased. The biomass of Gram positive bacteria and actinomycetes increased in gaps, but the biomass of Gram negative bacteria and fungi decreased. Soil gaps became less fertile and dominated by bacteria after the windthrow in the P. halepensis site. We emphasize the relevance of considering post-disturbance time recovery and disturbance intensity to assess forest resilience within a multi-scale approach.

Comparative Study of Change Detection and Urban Expansion Using Multi-Date Multi-Source Data: A Case Study of Greater Khartoum

2012 ◽  
Vol 23 (2) ◽  
pp. 139-172
Author(s):  
Abdullah Salman Alsalman Abdullah Salman Alsalman
Keyword(s):  
Image Processing ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Test Area ◽  
Image Differencing ◽  
Erdas Imagine

Noting that Khartoum represents the most rapidly expanding city in the Sudan and taking into account that change detection operations are seldom , the present study has been initiated to attempt to produce work that synthesizes land use/land cover (LULC) to investigate change detection using GIS, remote sensing data and digital image processing techniques; estimate, evaluate and map changes that took place in the city from 1975 to 2003. The experiment used the techniques of visual inspection, write-function-memoryinsertion, image differencing, image transformation i.e. normalized difference vegetation index (NDVI), tasseled cap, principal component analysis (PCA), post-classification comparison and GIS. The results of all these various techniques were used by the authors to study change detection of the geographic locale of the test area. Image processing and GIS techniques were performed using Intergraph Image analyst 8.4 and GeoMedia professional version 6, ERDAS Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and analyzed in a comparative manner and a conclusion regarding the best method for change detection of the test area was derived.

scholarly journals An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

2015 ◽  
Vol 8 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Daniel Hölbling ◽  
Barbara Friedl ◽  
Clemens Eisank
Keyword(s):  
Change Detection ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Detection Methods ◽  
Detection And Attribution ◽  
Object Based ◽  
Wide Range ◽  
Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

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