scholarly journals Change detection of remotely sensed image using NDVI subtractive and classification methods.

2019 ◽  
Vol 14 (29) ◽  
pp. 125-137
Author(s):  
Israa Jameel Muhsin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Remotely Sensed ◽  
Time Interval ◽  
Landsat 8 ◽  
Classification Methods ◽  
Survey Techniques ◽  
Remotely Sensed Image

Change detection is a technology ascertaining the changes ofspecific features within a certain time Interval. The use of remotelysensed image to detect changes in land use and land cover is widelypreferred over other conventional survey techniques because thismethod is very efficient for assessing the change or degrading trendsof a region. In this research two remotely sensed image of Baghdadcity gathered by landsat -7and landsat -8 ETM+ for two time period2000 and 2014 have been used to detect the most important changes.Registration and rectification the two original images are the firstpreprocessing steps was applied in this paper. Change detection usingNDVI subtractive has been computed, subtractive between the bandsof the two images and the ratio of the red to blue bands was alsocomputed. Change detection mask using minimum distanceclassification or detection after classification have be also used tocompute the changes between the resultant classes, many statisticalproperties of the original and process image have been illustrated inthis research

Change Detection of Land Cover at Zheng Lanqi County of Inner Mongolia

2012 ◽  
Vol 518-523 ◽  
pp. 1371-1374
Author(s):  
Chu La Sa ◽  
Gui Xiang Liu ◽  
Mu Lan Wang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Inner Mongolia ◽  
Remotely Sensed ◽  
Landsat Tm ◽  
Remotely Sensed Images ◽  
Second Period

In the study we mapped and analyzed the land use/cover changes in Zheng Lanqi county by visual interpreting the 3 sets of Landsat TM and ETM remotely sensed images received in 1990, 2000 and 2005.The 6 broad types of land use/ cover were interpreted for the study area. Through analyzing land use/cover changes, our study indicated that the grassland and built-up area is dominant landscape in the study area.The grassland in the study area shrank 588.68km2 for urbanization and farmland cultivation for first periods. The unchanged land is 10639.75km2 and 10743.18km2 for the two periods (1990-2000 and 2000-2005), respectively. This indicated that the landscape conversion in second period became stable than that of first period, and environment is improved since 2000.

scholarly journals Monitoring Land Use and Land Cover Change of Forest Ecosystems of Shendurney Wildlife Sanctuary, Western Ghats, India

2021 ◽  
pp. 20-27
Author(s):  
H. Bilyaminu ◽  
P. Radhakrishnan ◽  
K. Vidyasagaran ◽  
K. Srinivasan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Forest Ecosystem ◽  
Forest Ecosystems ◽  
Evergreen Forest ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Wildlife Sanctuary

Understanding forest degradation due to human and natural phenomena is crucial to conserving and managing remnant forest resources. However, forest ecosystem assessment over a large and remote area is usually complex and arduous. The present study on land use and land cover change detection of the Shendurney Wildlife Sanctuary forest ecosystems was carried out to utilize the potential application of remote sensing (RS) and geographic information system (GIS). Moreover, to understand the trend in the forest ecosystem changes. The supervised classification with Maximum Likelihood Algorithm and change detection comparison approach was employed to study the land use and land cover changes, using the Landsat Enhanced Thematic Mapper (ETM±) and Landsat 8 OLI-TIRS using data captured on July 01, 2001, and January 14, 2018. The study indicated the rigorous land cover changes. It showed a significant increase in the proportion of degraded forest with negligible gain in the proportion of evergreen forest from 21.31% in 2001 to 22.97% in 2018.  A substantial loss was also observed in moist deciduous from 27.11 % in 2001 to 17.23 % in 2018. The result of the current study indicated the degree of impacts on forests from the various activities of their surroundings. This study provides baseline information for planning and sustainable management decisions.

scholarly journals Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

2020 ◽  
Vol 12 (18) ◽  
pp. 3062 ◽  
Author(s):  
Michel E. D. Chaves ◽  
Michelle C. A. Picoli ◽  
Ieda D. Sanches
Keyword(s):  
Systematic Review ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Near Infrared ◽  
Data Cube ◽  
Optical Data ◽  
Landsat 8 ◽  
Detailed Knowledge ◽  
Sentinel 2

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

scholarly journals New Change Detection Techniques to monitor land cover dynamics in mine environment

2014 ◽  
Vol XL-8 ◽  
pp. 875-879 ◽  
Author(s):  
S. Pathak
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Environmental Changes ◽  
Time Interval ◽  
Land Use Patterns ◽  
Detection Techniques ◽  
Use Patterns ◽  
Object Based ◽  
Ideal Situation

Land use and land cover are dynamic and is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to simulate environmental changes. Land use/cover (LU/LC) change detection is very essential for better understanding of landuse dynamic during a known period of time for sustainable management. Mining is one of the most dynamic processes with direct as well as indirect impact on the environment. Hence, mine area provides ideal situation for evaluating the chronological changes in land-use patterns. Digital change detection of satellite data at different time interval helps in analyzing the changes in the spatial extent of mine along with the associated activities. In present study, various algorithms Iteratively Re-weighted Multivariate Alteration Detection (MAD) on raw data where class wise comparison becomes a difficult proposition and object based segmentation and change detection as post classification comparison were assessed.

scholarly journals SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 31-35 ◽  
Author(s):  
A. E. Akay ◽  
B. Gencal ◽  
İ. Taş
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Short Paper ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Spatiotemporal Change ◽  
Change Detection Analysis ◽  
Flooded Forest ◽  
Detection Analysis

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

CLASSIFICATION METHODS FOR REMOTELY SENSED DATA: LAND USE AND LAND COVER CLASSIFICATION USING VARIOUS COMBINATIONS OF BANDS

2015 ◽  
Vol 74 (10) ◽  
Author(s):  
Nur Anis Mahmon ◽  
Norsuzila Ya’acob ◽  
Azita Laily Yusof ◽  
Jasmee Jaafar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Ground Truth Data ◽  
Sensing Applications ◽  
Band Combinations

Land use and land cover (LU/LC) classification of remotely sensed data is an important field of research by which it is commonly used in remote sensing applications. In this study, the different types of classification techniques were compared using different RGB band combinations for classifying several satellite images of some parts of Selangor, Malaysia. For this objective, the classification was made using Landsat 8 satellite images and the Erdas Imagine software as the image processing package. From the classification output, the accuracy assessment and kappa statistic were evaluated to get the most accurate classifier. Optimal performance was identified by validating the classification results with ground truth data. From the results of the classified images, the Maximum Likelihood technique (overall accuracy 82.5%) was the highest and most applicable for satellite image classifications as compared with Mahalanobis Distance and Minimum Distance. Whereas for land use and land cover mapping, the RGB 4, 3, 2 band combinations were found to be more reliable. An accurate classification can produce a correct LU/LC map that can be used for various purposes.  

scholarly journals Monitoring Land Encroachment and Land Use & Land Cover (LULC) Change in The Pachhua Dun, Dehradun District Using Landsat Images 1989 and 2020

2021 ◽  
Vol 4 (1) ◽  
pp. 71
Author(s):  
Rahul Thapa ◽  
Vijay Bahuguna
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Forest Cover ◽  
Agricultural Land ◽  
Rate Of Change ◽  
Sustainable Land Use ◽  
Global Changes ◽  
Landsat 8 ◽  
Lulc Change

Remote sensing and G.I.S help acquire information on changing land use and land cover (LULC), and it plays a pivotal role in measuring and monitoring such local and global changes. The present analysis has been executed on Landsat 5 TM, 1989 and Landsat 8 OLI/TIRS, 2020 images of Pachhua Dun, including Dehradun & Mussoorie urban agglomeration. The present study aims to detect the land encroachment or area of change; rate of change and monitoring spatio-temporal variation in LULC change between 1989-2020 using change detection technique, supervised maximum likelihood classification, and Overall accuracy & Kappa Coefficient (K) was applied as an accuracy assessment tool. The results derived from the change detection analysis exhibits that the highest growth rate was recorded in built-up areas +247.75% (110 km2) and revealed the annual rate of change of 3.55 km2. or  7.99%, the highest among all LULC class during the overall study period of 31 years. The result also found that among all LULC class, the most significant LULC conversion took place from agricultural land to built-up areas followed by open/scrubland and vegetation/forest cover; approximately 69.9km2 of the area under agricultural land was found to be converted into built-up areas. At the same time, 38.9 km2 area of vegetation/forest cover and 36.3 km2 of the area of open/scrubland have converted into agricultural land. Rising anthropogenic influence and unsustainable land-use practices in the study area have led to a large-scale human encroachment and rapid transformation of the natural landscape into the cultural landscape. This analysis provides the essential long-term Geospatial information related to LULC change for making optimum decision-making process and sustainable land-use planning in the Pachhua Dun-Dehradun District, Uttarakhand, India. 

scholarly journals An Automatic and Operational Method for Land Cover Change Detection Using Spatiotemporal Analysis of MODIS Data: A Northern Ontario (Canada) Case Study

2021 ◽  
Vol 10 (5) ◽  
pp. 325
Author(s):  
Ima Ituen ◽  
Baoxin Hu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Basic Unit ◽  
Operational Method ◽  
Northern Ontario ◽  
Modis Data ◽  
Forest Fire Management ◽  
Land Cover Change Detection

Mapping and understanding the differences in land cover and land use over time is an essential component of decision-making in sectors such as resource management, urban planning, and forest fire management, as well as in tracking of the impacts of climate change. Existing methods sometimes pose a barrier to the effective monitoring of changes in land cover and land use, since a threshold parameter is often needed and determined based on trial and error. This study aimed to develop an automatic and operational method for change detection on a large scale from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Super pixels were the basic unit of analysis instead of traditional individual pixels. T2 tests based on the feature vectors of temporal Normalized Difference Vegetation Index (NDVI) and land surface temperature were used for change detection. The developed method was applied to data over a predominantly vegetated area in northern Ontario, Canada spanning 120,000 sq. km from 2001–2016. The accuracies ranged between 78% and 88% for the NDVI-based test, from 74% to 86% for the LST-based test, and from 70% to 86% for the joint method compared with manual interpretation. Our proposed method for detecting land cover change provides a functional and viable alternative to existing methods of land cover change detection as it is reliable, repeatable, and free from uncertainty in establishing a threshold for change.

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