A Novel Remote Sensing Technology on Land Analysis Use Change Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 10-14
Author(s):  
J. Suresh Babu ◽  
T. Sudha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ground Truth ◽  
Soil Protection ◽  
Land Cover Changes ◽  
Remote Sensing Technology ◽  
Picture Arrangement ◽  
Sensing Technology ◽  
Erdas Imagine ◽  
Extreme Probability

Change location is helpful in numerous applications identified with land use and land cover changes, for example, moving development and scene changes, arrive corruption and desertification. Remotes sensing technology has been utilized for the location of the adjustment in land use arrive cover in upper Rib watershed. The fundamental target of this examination was to identify the land use change utilizing Remotes sensing for manageable land use arranging in Upper Rib watershed. The two satellite pictures for the year 2007 and 2018 were downloaded and utilized for recognizing the land cover changes. Most extreme probability arrangement was utilized in ERDAS Imagine device for characterizing the pictures. Ground truth focuses were gathered and utilized for check of picture arrangement. This was identified with the proceeded with extension of developed and settlement over years in River watershed. The information about the adjustment in land use is so fundamental for the organization and land use arranging exercises in upper Rib watershed. This is so for, the expansion and profitability of Rib repository by decreasing the upland disintegration through powerful land use arranging and soil preservation rehearses. Consequently, this examination uncovered that there is an expansion of horticultural land which needs due consideration towards soil protection for the improvement of the helpful existence of the supply.

scholarly journals Land Use/Land Cover Changes in a Disturbed River Watershed Kenya

2019 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
Zachary Gichuru Mainuri ◽  
John M. Mironga ◽  
Samuel M. Mwonga
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing Image ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Human Needs ◽  
Observation Systems ◽  
Erdas Imagine ◽  
Remote Sensing Image Processing

Drivers of land use change were captured by the use of DPSIR model where Drivers (D) represented human needs, Pressures (P), human activities, State (S), the ecosystem, Impact (I) services from the ecosystem and Response (R), the decisions taken by land users. Land sat MSS and Land sat ETM+ (path 185, row 31) were used in this study. The Land sat ETM+ image (June 1987, May, 2000 and July, 2014) was downloaded from USGS Earth Resources Observation Systems data website. Remote sensing image processing was performed by using ERDAS Imagine 9.1. Two land use/land cover (LULC) classes were established as forest and shrub land. Severe land cover changes was found to have occurred from 1987-2000, where shrub land reduced by -19%, and forestry reduced by -72%. In 2000 – 2014 shrub land reduced by-45%, and forestry reduced by -64%. Forestry and shrub land were found to be consistently reducing.

scholarly journals Perbandingan Quadratic Discriminant Analysis dan Support Vector Machine untuk Klasifikasi Tutupan Lahan di DKI Jakarta

2020 ◽  
Vol 9 (1) ◽  
pp. 12-20
Author(s):  
Kamaluddin Junianto Dimas ◽  
Rahma Anisa ◽  
Itasia Dina Sulvianti
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Land Cover ◽  
Training Data ◽  
Support Vector ◽  
Quadratic Discriminant Analysis ◽  
Remote Sensing Technology ◽  
Sensing Technology

DKI Jakarta is a center of government as well as economy and business of Indonesia, thus development projects in Jakarta continue every year. Therefore, monitoring for land use has to be improved in accordance to DKI Jakarta Spatial Planning. The attempt needs to be supported by continuous data availability regarding land cover condition in Jakarta. The aforementioned data collecting process become easier due to remote sensing technology development. Remote sensing technology can be utilized for analyzing the size of land use area by using classification analysis. It has been found that the level of accuracy depends on the type of classification method and number of training data. This research evaluated the level of overall accuracy, sensitivity, and specificity of Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) along with number of data training used in classifying Jakarta land cover in 2017. The results showed that in both methods, the variance of all the aforementioned criteria were getting smaller along with the increasing number of training data. QDA and SVM had similar performance based on overall accuracy and specificity. However, SVM was better than QDA on sensitivity.

scholarly journals Land Use and Land Cover Changes in the Owabi Reservoir Catchment, Ghana: Implications for Livelihoods and Management

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 286 ◽  
Author(s):  
Philip Antwi-Agyei ◽  
Felix Kpenekuu ◽  
Jonathan N. Hogarh ◽  
Kwasi Obiri-Danso ◽  
Robert C. Abaidoo ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Community Involvement ◽  
Anthropogenic Activities ◽  
Bare Soil ◽  
Land Cover Changes ◽  
Catchment Management ◽  
Forest Clearing ◽  
Local Livelihoods ◽  
Erdas Imagine

Reservoir catchments in Ghana have undergone significant changes in recent years with major implications for socio-economic development and local livelihoods. We studied land use and land cover changes and their impacts on livelihoods in the Owabi reservoir catchment from 1970 to 2014 using Landsat, ERDAS Imagine and Arc Geographic Information System (ArcGIS 10.2) software supplemented with participatory approaches including focus group discussions, key informant interviews and questionnaire surveys with 400 households. Our results showed that, since 1970, 24.6% of high-density forests and 15.8% of sparse forests have disappeared, while the built-up area has increased from 9.8% to 56.6%. Additionally, the proportion of bare soil (areas that do not have vegetation cover due to forest clearing and other anthropogenic activities) has increased, while the areas of waterbodies have declined. We identified urbanisation and lack of community involvement in catchment management as the key factors driving the land cover changes that have adversely affected the livelihoods of the local fringe communities. This study highlights the threats from urbanisation to land cover changes and identifies the key drivers of land use change. For effective and sustainable management of natural resources, the local communities should be more actively involved in the decision-making process regarding the management of their individual catchments.

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

scholarly journals ANALISIS SPASIAL SEBARAN LAHAN KRITIS DI KPHP UNIT XII BATANGHARI PROVINSI JAMBI

2021 ◽  
Vol 4 (2) ◽  
pp. 127
Author(s):  
Eva Achmad ◽  
Agus Kurniawan ◽  
Yunita Lestari
Keyword(s):  
Land Cover ◽  
Critical Level ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Maximum Score ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Forest Function ◽  
Slope Class

Critical land occured as a result of land cover changes from vegetated into non vegetated land or the composition of the vegetation has changed. This study aimed to analyze the distribution of land critical at KPHP Unit XII Batanghari. Critical land analysis was based on the Perdirjen PDASHL Number P.3/PDASHL/SET/KUM.1/7/2018. Land is classified into 5 levels of criticality, namely: non-critical, critical potential, somewhat critical, critical and very critical. The parameters used in determining the level of criticality of the land are: land cover, erosion-prone class, slope class and the presence of land inside or outside the forest function. Spatial analysis used by Geographic Information System (GIS) and remote sensing technology. GIS is able to analyze and represent geographic phenomenon. Landsat 8 imagery was analyzed to obtain land cover clasification. The results of the analysis showed that critical land level of KPHP Unit XI Batanghari consisted of 3,609 ha (4.45%) that classified as very critical and 3,599 ha (4,43%) as critical land. Then, land with a somewhat critical level had the largest area, namely 26,024 ha or 32.07% of the total area of KPHP Unit XII Batanghari. The landcover was the main parameter to determine the level of criticality of the land. The openland cover type had the maximum score 60.

Sign in / Sign up

Export Citation Format

Share Document