scholarly journals Forest cover change in Onigambari reserve, Ibadan, Nigeria: Application of vegetation index and Markov chain techniques

2021 ◽  
Author(s):  
Alabi Khadijat ◽  
Tobore Anthony ◽  
Oyerinde Ganiyu ◽  
Senjobi Bolarinwa
Keyword(s):  
Markov Chain ◽  
Vegetation Index ◽  
Forest Cover ◽  
Forest Cover Change ◽  
Ibadan Nigeria

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 Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok, India

2014 ◽  
Vol 123 (6) ◽  
pp. 1349-1360 ◽  
Author(s):  
Anirban Mukhopadhyay ◽  
Arun Mondal ◽  
Sandip Mukherjee ◽  
Dipam Khatua ◽  
Subhajit Ghosh ◽  
...  
Keyword(s):  
Markov Chain ◽  
Forest Cover ◽  
Markov Chain Model ◽  
Chain Model ◽  
Forest Cover Change ◽  
Change Prediction ◽  
Hybrid Methodology

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.

scholarly journals Investigation on Zagros forests cover changes under the recent droughts using satellite imagery

2019 ◽  
Vol 65 (No. 1) ◽  
pp. 9-17 ◽  
Author(s):  
Marjan Goodarzi ◽  
Mehdi Pourhashemi ◽  
Zahra Azizi
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Annual Rainfall ◽  
Forest Cover Change ◽  
Water Index ◽  
Forest Dieback ◽  
Zagros Forests ◽  
Average Annual Temperature

Oak decline phenomenon has recently led to considerable dieback within Zagros forests, western Iran. In the present study, Landsat imagery (2005 to 2016) and synoptic station data were used to study the forest dieback in Dorood, Lorestan province. Sixteen vegetation indices were calculated and values in each year were obtained. The correlations between the index and climatic parameters of rainfall, temperature and relative humidity were investigated. Results showed that the correlation of some indices with rainfall and the correlation of other indices with temperature were more than 70%. Optimized soil adjusted vegetation index had 80% correlation with annual rainfall and the modification of normalized difference water index was correlated with average annual temperature by 75%. Using the numerical value changes of the indices, a map of forest cover change was prepared in four classes; healthy, weak, moderate and severe dieback and the process of its change were compared with the trend of variations in regard with rainfall values in the study period. There was a close relationship between changes in the area of forest cover dieback and rainfall and temperature values.

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

scholarly journals Mapping Annual Forest Change Due to Afforestation in Guangdong Province of China Using Active and Passive Remote Sensing Data

2019 ◽  
Vol 11 (5) ◽  
pp. 490 ◽  
Author(s):  
Wenjuan Shen ◽  
Mingshi Li ◽  
Chengquan Huang ◽  
Xin Tao ◽  
Shu Li ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Forest Cover ◽  
Southern China ◽  
Carbon Accounting ◽  
Gradient Boosting ◽  
Forest Cover Change ◽  
Land Cover Types ◽  
Stochastic Gradient Boosting ◽  
Biomass Dynamics

Accurate acquisition of spatial distribution of afforestation in a large area is of great significance to contributing to the sustainable utilization of forest resources and the evaluation of the carbon accounting. Annual forest maps (1986–2016) of Guangdong, China were generated using time series Landsat images and PALSAR data. Initially, four PALSAR-based classifiers were used to classify land cover types. Then, the optimal mapping algorithm was determined. Next, an accurate identification of forest and non-forest was carried out by combining Landsat-based phenological variables and PALSAR-based land cover classifications. Finally, the spatio-temporal distribution of forest cover change due to afforestation was created and its forest biomass dynamics changes were detected. The results indicated that the overall accuracy of forest classification of the improved model based on the PALSAR-based stochastic gradient boosting (SGB) classification and the maximum value of normalized difference vegetation index (NDVI; SGB-NDVI) were approximately 75–85% in 2005, 2010, and 2016. Compared with the Japan Aerospace Exploration Agency (JAXA) PALSAR-forest/non-forest, the SGB-NDVI-based forest product showed great improvement, while the SGB-NDVI product was the same or slightly inferior to the Global Land Cover (GLC) and vegetation tracker change (VCT)-based land cover types, respectively. Although this combination of multiple sources contained some errors, the SGB-NDVI model effectively identified the distribution of forest cover changes by afforestation events. By integrating aboveground biomass dynamics (AGB) change with forest cover, the trend in afforestation area closely corresponded with the trend in forest AGB. This technique can provide an essential data baseline for carbon assessment in the planted forests of southern China.

scholarly journals The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 670 ◽  
Author(s):  
Wan Shafrina Wan Mohd Jaafar ◽  
Khairul Nizam Abdul Maulud ◽  
Aisyah Marliza Muhmad Kamarulzaman ◽  
Asif Raihan ◽  
Syarina Md Sah ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Forest Cover ◽  
Environmental Changes ◽  
Rapid Change ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Forest Cover Change

Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.

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