Satellite Image Classes Categorization Schemes for United Nations Framework Convention on Climatic Change (UNFCCC)

2018 ◽  
pp. 101-113
Author(s):  
Joyce Gosata Maphanyane ◽  
Gofetamang Phunyuka
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Electromagnetic Waves ◽  
Satellite Image ◽  
National Representation ◽  
Thematic Resolution ◽  
The One

This chapter looks at the disparities between the UNFCCC – GHG – Land-Use and Land-Cover Change (LULCC) remote sensing images classification scheme with that of Botswana for the GHG inventory for the National Representation. This chapter has points out that the Botswana Scheme maximizes the LANDSAT System electromagnetic waves capabilities and maps produced give more classes and better thematic resolution for the classification of land cover classes. Suggestions are made for these two schemes to be reconciled and use the one which gives the best GHG calculated results for inventories for Inter-Governmental Panel on Climate Change (IPCC) Reporting

scholarly journals Landuse Pattern and Landcover of Akure Forest Reserve and Its Environs

2019 ◽  
pp. 852-861
Author(s):  
O. O. Ojo ◽  
A. A. Shittu ◽  
T. J. Adebolu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Forest Reserve ◽  
Gis And Remote Sensing ◽  
Forestry Management ◽  
Ondo State ◽  
Cover Class ◽  
Broad Objective

This study investigated the pattern of land use and land cover of forest reserve in Akure, Ondo State, Nigeria. Currently, deforestation constitutes one of the global development challenges. The broad objective of this study is to identify land use and land cover class within the study area using satellite imagery (ies) to determine the rate/trend of change of this Forest Reserve from 1988 to 2018. The research method includes the use of Geographical Positioning System, and processing of field data through GIS and Remote sensing tool (ILWIS). The research was able to identify various land use and land cover within the Akure forest reserve with the help of GIS and remote sensing tools, the boundary of Akure forest reserve and its environs was delineated, and further result of the classification of Landsat shows that as at 2018 the forest reserve is covered with majorly light vegetation with 51.79%. The study recommended that there Department of Forestry and Ministry of Physical Planning and Urban Development must ensure Policy that will encourage local people and institutional participation in forestry management and conservation along with safeguarding indigenous people’s traditional rights and tenure with rightful sharing of benefits.

scholarly journals Land Use and Land Cover Mapping of Davangere using Google Earth Engine

2019 ◽  
Vol 8 (3) ◽  
pp. 474-477
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Error Matrix ◽  
Sensing Applications ◽  
Google Earth Engine

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defence, intelligence, commerce, economics and administrative planning. One among these applications is the construction of land use and land cover maps through image classification process. Land Use / Land Cover (LULC) information is a crucial input in designing efficient strategies for managing natural resources and monitoring environmental changes from time to time. The present study aims to know the extent of land cover and its usage in Davangere region of Karnataka, India. In this study, satellite image of Davangere during October-November 2018 was used for LULC supervised classification with the help of remote sensing tools like QGIS and Google Earth Engine. Six LULC classes were decided to locate on the map and the accuracy assessment was done using theoretical error matrix and Kappa coefficient. The key findings include LULC under Water bodies (8%), Built up Area (15.1%), Vegetation (9%), Horticulture (20.8%), Agriculture (39.3%) and Others (7%) with overall accuracy of 94.8% and Kappa coefficient of 0.866 indicating almost accurate goodness of classification

scholarly journals MULTITEMPORAL ANALYSIS OF FOREST COVER CHANGE USING REMOTE SENSING AND GIS OF KANHA TIGER RESERVE, CENTRAL INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 211-219 ◽  
Author(s):  
R. M. Devi ◽  
B. Sinha ◽  
J. Bisaria ◽  
S. Saran
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Central India ◽  
Adaptation Strategies ◽  
Land Use Land Cover ◽  
Tiger Reserve ◽  
The Impact

<p><strong>Abstract.</strong> Forest ecosystems play a key role in global ecological balance and provide a variety of tangible and intangible ecosystem services that support the livelihoods of rural poor. In addition to the anthropogenic pressure on the forest resources, climate change is also impacting vegetation productivity, biomass and phenological patterns of the forest. There are many studies reported all over the world which use change in Land Use Land Cover (LULC) to assess the impact of climate change on the forest. Land use change (LC) refers to any anthropogenic or natural changes in the terrestrial ecosystem at a variety of spatial or temporal scale. Changes in LULC induced by any causes (natural/anthropogenic) play a major role in global as well as regional scale pattern which in turn affects weather and climate. Remote sensing (RS) data along with Geographic Information System (GIS) help in inventorying, mapping and monitoring of earth resources for effective and sustainable landscape management of forest areas. Accurate information about the current and past LULC including natural forest cover along with accurate means of monitoring the changes are very necessary to design future adaptation strategies and formulation of policies in tune of climate change. Therefore, this study attempts to analyze the changes of LULC of Kanha Tiger Reserve (KTR) due to climate change. The rationale for selecting KTR is to have a largely intact forest area without any interference so that any change in LULC could be attributed to the impact of climate change. The change analysis depicted changes in land use land cover (LULC) pattern by using multi-temporal satellite data over a period of time. Further, these detected changes in different LULC class influence the livelihoods of forest-dependent communities. As the study site is a Sal dominated landscape; the findings could be applied in other Sal dominated landscape of central India in making future policies, adaptation strategies and silvicultural practices for reducing the vulnerability of forest-dependent communities.</p>

A hybrid optimization-based deep belief neural network for the classification of vegetation area in multi-spectral satellite image

2021 ◽  
Vol 24 (4) ◽  
pp. 363-379
Author(s):  
Anil B. Gavade ◽  
Vijay S. Rajpurohit
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Land Cover ◽  
False Positive Rate ◽  
Satellite Image ◽  
Harmony Search ◽  
Classification Problem ◽  
True Positive Rate ◽  
Positive Rate

Over the last few decades, multiple advances have been done for the classification of vegetation area through land cover, and land use. However, classification problem is one of the most complicated and contradicting problems that has received considerable attention. Therefore, to tackle this problem, this paper proposes a new Firefly-Harmony search based Deep Belief Neural Network method (FHS-DBN) for the classification of land cover, and land use. The segmentation process is done using Bayesian Fuzzy Clustering,and the feature matrix is developed. The feature matrix is given to the proposed FHS-DBN method that distinguishes the land coverfrom the land use in the multispectral satellite images, for analyzing the vegetation area. The proposed FHS-DBN method is designedby training the DBN using the FHS algorithm, which is developed by the combination of Firefly Algorithm (FA) and Harmony Search (HS) algorithm. The performance of the FHS-DBN model is evaluated using three metrics, such as Accuracy, True Positive Rate (TPR), and False Positive Rate (FPR). From the experimental analysis, it is concludedthat the proposed FHS-DBN model achieves ahigh classification accuracy of 0.9381, 0.9488, 0.9497, and 0.9477 usingIndian Pine, Salinas scene, Pavia Centre and university, and Pavia University scene dataset.

scholarly journals A comparison and classification of land use land cover to estimate their effect on environment: case study in Baghdad city

2021 ◽  
Vol 2114 (1) ◽  
pp. 012017
Author(s):  
Bushra A. Ahmed ◽  
Ghaida S. Hadi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Major Change ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Two Cities ◽  
Landsat Satellite

Abstract This study compared and classified of land use and land cover changes by using Remote Sensing (RS) and Geographic Information Systems (GIS) on two cities (Al-Saydiya city and Al-Hurriya) in Baghdad province, capital of Iraq. In this study, Landsat satellite image for 2020 were used for (Land Use/Land Cover) classification. The change in the size of the surface area of each class in the Al-Saydiya city and Al-Hurriya cities was also calculated to estimate their effect on environment. The major change identified, in the study, was in agricultural area in Al-Saydiya city compare with Al-Hurriya city in Baghdad province. The results of the research showed that the percentage of the green area from the total area in Al-Saydiya city is 34.95%, while in Al-Hurriya is 27.53%. Therefore, available results of land use and land cover changes can provide critical input to decision-making of environmental management and planning the future.

scholarly journals The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuanen County (Hubei, China)

2019 ◽  
Author(s):  
Lixia Chen ◽  
Zizheng Guo ◽  
Kunlong Yin ◽  
Dhruba Pikha Shrestha ◽  
Shikuan Jin
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Landslide Susceptibility ◽  
Aerial Photographs ◽  
Time Interval ◽  
Human Engineering ◽  
Classification Of Images

Abstract. Land use and land cover change can have effect on the land by increasing/decreasing landslide susceptibility (LS) in the mountainous areas. In the southwestern hilly and mountainous part of China, land use and land cover change (LUCC) has been taking place in the recent past due to infrastructure development and increase in economic activities. These development activities can also bring negative effects: the sloping area may become susceptible to landsliding due to undercutting of slopes. The study aims at evaluating the influence of land use and land cover change on landslide susceptibility at regional scale, based on the application of Geographic Information System (GIS) and Remote Sensing (RS) technologies. Specific objective is to answer the question: which land cover/land use change poses the highest risk so that mitigation measures can be implemented in time? The Zhushan Town, Xuanen County in the southwest of China was taken as the study area and the spatial distribution of landslides was determined from visual interpretation of aerial photographs and remote sensing images, as well as field survey. Two types of land use/land cover (LUC) maps, with a time interval covering 21 years (1992–2013), were prepared: the first was obtained through the neural net classification of images acquired in 1992, the second through the object-oriented classification of images in 2002 and 2013. Landslide susceptible areas were analyzed using logistic regression models. In this process, six landslide influencing factors were chosen as the landslide susceptibility indices. Moreover, we applied a hydrologic analysis method achieving slope unit (SU) delineation to optimize the partitioning of the terrain. The results indicate that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land and the human engineering activities land (HEAL). The areas of these two kind of LUC increased by 34.3 % and 1.9 %, respectively. The comparison of landslide susceptibility maps in various periods revealed that human engineering activities was the most important factor to increase LS in this region. Such results underline that a more reasonable land use planning in the urbanization process is necessary.

scholarly journals Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Ibochi Andrew Abah ◽  
Richard jeremiah Uriah
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Image Data ◽  
Image Process ◽  
Land Use Land Cover ◽  
Error Matrix

Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall accuracy and kappa coefficient for MDC as 94.00% and 0.91, SAM as 64.45% and 0.53, and SVM as 98.92% and 0.98 respectively. The overall accuracy obtained from SVM indicates that a perfect classification map will be produced from the algorithm. The advanced supervised classification should be utilized for classification of land use/ land cover for both high and medium resolution images for improved classification accuracy.

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