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Generating land use/land cover change index for satelite images data verification (case study: West Java Province)
a Faculty of Earth Sciences and Technology, Geodesy and Geomatics Engineering, Institute of Technology Bandung (ITB), Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 by Ceser Publications.The majority of land use/landcover (LULC) data were derive from satellite image data of varying resolution. These images were interpreted to classify the LULC in a particular area at a certain period. However, the process of visual interpretation might produce a pattern of land class change that does not match actual condition because it always has the potential to create different classification outcomes if compared using image data of higher resolution. Thus, it becomes highly important to verify satellite image data of the study area by generating an index of LULC change. In this research, proportional stratified random sampling method was used. In this method, each sample of recorded LULC change was verified using a high-resolution image. The verification results were then tested using one-tailed t-test with 90% confidence level. The product was an index of LULC Change where 1 (the number one) signified that change did/might happen and 0 (the number zero) signified that change did/might not happen. From this research, LULC Change index with a resolution of 30 meters for the area of West Java Province was obtained. Cellular Autamata-Markov Chain model was used to test the application of this index to predict land cover in 2015. The most accurate result (accurate value = 40.3797%) was obtained from using Von Neuman nearest neighbors method with 7×7 size. This value was 0.9509 higher than the value resulted from LULC prediction without using the index.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]LULC,LULC Change index,One tailed t-test,Proportionate stratified random sampling[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]