In addition in this chapter the author shall also orientate the research, and generalize the steps of data collection, image analysis and role of remote sensing — GIS in research of land
LITERATURE REVIE WS So H000 00800886 3 1.1 Overall study on applications of Remote Sensing and GIS in mapping land
Overall study on applications of Remote Sensing and GIS in mapping land
‘Currently there have been many research works on the convulsion of land cover with many different viewpoints, of which there are works that focus on argumentative research analysis, while others focus on methods of finding convulsion and there are works that combine both: convulsion discovery technique, result assessment and argument supplementation,
‘The topic “Research of convulsion of several forms of land use in the peri-urban areas of Tu Liem district Hanoi city on the basis of remote sensing and GIS technology application” of author Nguyen Thi Thuy Hang has solved problems such as extraction of information on convulsion of land use from multispectral and multi and time remote sensing data through several methods of digital image analysis processing, integrating remote sensing data analysis results with other data to assess the correlation between convulsion of land use and socioeconomic phenomena.
In their article, "Assessment of Land Use Convulsion of Thanh Tri District, Hanoi City in the Period of 1994-2003 on the Basis of Remote Sensing Method Combined with GIS," authors Nhu Thi Xuan, Dinh Thi Bao Hoa, and Nguyen Thi Thuy Hang examine land use changes in the Thanh Tri district This area has been significantly impacted by rapid urbanization, making it an important case study for understanding the dynamic nature of land use conversion and its implications for urban planning and management within developing regions.
In addition to using remote sensing data in the research of convulsion, author Hoang
‘Thi Thanh Huong in the topic “Research of convulsion of land use in Long Bien distr Hanoi city during urbanization” has combined remote sensing material with the spatial analysis of the geo information system The topic pilots the new classification method of classification by subject, method of performance on remote sensing data with very high resolution (VHR) In addition, spatial analysis is used in GIS to compare classification results with the socioeconomic data to see the interaction between them The result shows that the remot snsing image with high spatial resolution can meet the requirements of accuracy of urban areas with fragmentation as in Vietnam.
In the topic “Establishment of vegetation map on the basis of remote sensing image analysis, processing” at the area of Tua Chua ~ Lai Chau (Hoang Xuan Thanh, 2006), the author used the supervised classification method for the Landsat image data of 2006 to classify 7 different vegetation classes with the Kapa index =0:7
In the topic “Application of remote sensing in monitoring the urban land convulsion of Vinh city, Nghe An province” (Nguyen Ngọc Phi, 2009) the most approximate classification method was used to divide into 5 classes of subject The most noteworthy point of this topic is the combined use of various remote sensing images such as Landsat (1992, 2000) and SPOT (2005) to bring forth the interpreted results, while also have a comparison on the accuracy, detail between the image types With a Kappa index of =0,9, the SPOT image data has the post-classification accuracy higher than compared to Landsat (Kapa~0.7),
In their study titled "Application of Remote Sensing and GIS in Establishing the Land Cover Map of the Chan May Area, Phu Loc District, Thua Thien Hue Province," Nguyen Huy Anh and Dinh Thanh Kien utilized the maximum likelihood classification technique to analyze Landsat TM image data at a resolution of 30 meters This method, widely recognized for its precision, enabled the researchers to categorize land cover types within the study area, providing valuable insights into the spatial distribution of various landforms and ecosystems.
10m, combined with field sampling to divide into 13 types of cover with relatively high accuracy
In the research “Usage of MODIS satellite image material in research of crop season, establishment of status quo and convulsion map of the Red River delta cover in the period of 2008 -2010" (Vu Huu Long, Pham Khanh Chi, Tran Hung, 2011), the author used supervised classification with the most approximate classification algorithm The topic classfied 9 types of cover with Kapa index ~0,9 To assess the accuracy, the author used a combination of the survey sample data, field investigation and the status quo map of Land use of the most recent year
In the topic “Research of the impact of shifting agricultural land to non-agricultural land in the vicinity of Hue city, period of 2006 - 2010” (Nguyen Thi Phuong Anh et al, 2012), the author assessed the impact of the shift of agricultural land to non- agricultural land on the economic structure, social life and bring out viable solutions, with the pilot research area being Kim Long ward In this topic, the author only used methods of synth perform research 1 is, analysis, comparison, contrasting, and statistics of data to we data is extracted via tables, without visual output by system of maps
In the topic “Research of change of forest vegetation at the Bach Ma national garden,
‘Thua Thien Hue province” (Dang Ngoc Quoc Hung, 2009), the author built forest vegetation maps of the years 1989, 2001, 2004, 2007 by supervised method of remote se ing image interpretation to extract information from satellite images Erdas software was used to interpret satellite images The change of the forest vegetation in the periods 1989-2001, 2001-2004, 2004-2007 was analyzed and assessed by method of stacking and analysis by Arcview 3.2 software, he application of remote sensi in monitoring the status of land use, although popular in the world, is still not widely applied in Vietnam This may show that, the capability of remote sensing in status monitoring is very good but the performance of this task is still difficult, especially for small areas,
In this research, the author used the method of remote sensing to extract information of land cover on the surface of the research area, combined with field survey and
‘other materials determining the status of land use
Tad cover map THỂ cover map and cover change map 1999-2015,
Fvaluaiag the resus of and cover ng
Figure I 1 Overview of the research
‘The GIS geographical information system is used to analyze, assess the convulsion of land resources.
‘The processing of remote sensing image for analysis of land cover for the research area adhers to the following procedure
Remote sensing image analysis plays a crucial role in land cover classification Popular methods include unsupervised and supervised classification Landsat 7 (1999) and Landsat 8 (2015) data were employed for image analysis to establish land cover maps for both periods Unsupervised classification considers pixel values without prior knowledge, while supervised classification utilizes training samples to assign classes These methods allow for efficient land cover classification, providing valuable insights into changes over time.
Each method of classification uses certain algorithms The algorithms have limitation and application in different situations (Shrestha and Alfted, 2001) The popularly used algorithms are the Minimum Distance, Parallelepiped and Maximum
Likelihood (Richards, 1994) Among these, the Maximum Likelihoood algorithm is used the most by classifiers in works of land cover research (Keuchel et al, 2003 Shrestha and Alfred, 2001; Swain and Davis, 1978; Este etal, 1983; Schowengerdi, 1983; Sabins, 1986; Lillesand and Kiefer, 2000; Jensen, 1996) The Minimum Distance algorithm is often applied in the unsupervised classification method, while the two algorithms Maximum Likelihood and Parallelepiped are usually applied in the supervised classification method In addition, people also use several methods to highlight the vegetation factor such as the vegetation index analysis method ~ NDVI
In this research, the author uses the supervised classification method and the Maximum Likelihood algorithm to analyze the land cover of 2 periods on the research area,
“The calculation of land cover changes of two periods is done by the CROSSTAB tool (Cross-Classification) in the IDRISI 17 software This method is the popular
9 approach to calculation of land cover changes in land cover researches around the world,
1.3.1 The remote sensing is used for monitoring land cover changes
Remote sensing is a science of technology that helps to identify, measure or analyze the properties of objects or phenomena from a distance without direct contact with the abject.
‘These natural objects absorb and reflect electromagnetic waves with intensity and in different ways, known as spectral characteristics These characteristics conta important information that allows the grouping of natural formations into objects of the same spectral reflectance This is useful for the interpretation of satellite imagery so the spectral reflectance characteristics of natural objects play a very important role in exploiting and applying effectively the collected information,
Spectral reflectance properties of natural objects are influenced by numerous factors, including lighting conditions, the atmospheric environment, and the object's surface The unique characteristics of each object (e.g., moisture, surface roughness, vegetation, humus) result in distinct spectral reflection and absorption patterns Remote sensing relies on these principles to identify and detect objects and phenomena in nature Understanding the spectral characteristics of natural objects guides image processing method selection to optimize the extraction of information about the object of study This is the foundation for analyzing and classifying objects based on their spectral properties.
Figure 1 3: Reflective spectral chayacteristics of vegetation cover
RESEARCH ON THE APPLICATION OF REMOSTE
RESEARCH ON THE APPLICATION OF REMOSTE SENSING AND GIS IN ESTABLISHMENT OF LAND COVER CHANGE MAP
OF SON LA PROVINCE 2.1, Overview of study area
2.1.1 Natural and socio-economic conditions
Son La is a mountainous province in Northwest Vietnam, with an area of 14,12 ket, accounting for 4.27% of Vietnam's total area, ranking third among 63 provinces and cities Geographic coordinates: 20°39 '- 2202 North latitude and 10311 "= 10502" East longitude The border: bordered Yen Bai, Dien Bien, Lai Chau in the north; Phu Tho and Hoa Binh to the east; bordered Dien Bien province in the west bordered Thanh Hoa province and Huaphanh province (Laos) in the south; and Luangprabang Province (Laos) in the southwest, Son La has a national boundary of
250 km, the border with other provinces is 628 km The province has 12 administrative units (I city, 11 districts) with 12 ethnic groups.
Son La has subtropical moist montane climate, cold dry non-tropical winter, hot and humid summer, heavy rain, Due to the deep and strong geographical divided terrain, many sub-climates are formed, allowing for the development of a rich agro-forestry production, Moe Chau plateau is suitable for temperate plants and animals.The area along the Da River is suitable for tropical evergreen forests, Statistics show that the annual average temperature of Son La tends to increase over the past 20 years with an increase of 0.5 ° C - 06° C, the average annual temperature in Son La is at 21.1 ° ©, Yen Chau 23 ° C; Average annual rainfall tends to decrease (city is currently at 1,402 mm, Moc Chau 1,563 mm); Average annual humidity also declines The
Calibration Untilities to conduct radiant calibration for remote sensing images.
ENVI provides powerful tools for enhancing the visibility of image information Enhance and Filter To perform these functions we do the following:
From window opened, choose Enhance, a drop-down list for selecting the enhanced area of the Image, Zoom, or Scroll window by the hinha method,
Itmage] Gaussian ental (image) Equalization el [image Square Root
(Zoom) Linear 0-255, (Zoom) Linear 2% eo] Gausian (oom Equalization [zoom Square Root {Serol Lnear
(serl Gaueian Scol) Eualztion [scroll Square Root Histogram Matching.
- Linear: Use the smallest and largest value of the image to perform linear stretch
This method applies to images with little value
- Linear 0-255; This method will display the pixel actual value of the image according to the display value of the image from 0 to 255.
~ Linear 2%: Linear enhancement method will cut 2% of the 2 data points to increase image visibility.
- Gaussian: Image enhancement method uses an average gray value of 127 and a standard deviation of 3 for enhancement.
- Equalization - This method will stetch graph balance of the displayed data,
- Square Root: This method calculates the square root of the first graph and then performs linear stretching.
Image before enhanced Image after enhanced (Linear 2%)
Figure 2 5 Image before and after being enhanced image quality
ENVI also enables to enhance images based on an enhanced image using the Histogram Matching function or allowing users self-enhance based on graphs and