ฉันรักการแปลDigital change detectio

ฉันรักการแปลDigital change detection techniques by using multi-temporal satellite imagery helps in understanding landscape dynamics. The present study illustrates the spatio-temporal dynamics of land use/cover of Hawalbagh block of district Almora, Uttarakhand, India. Landsat satellite imageries of two different time periods, i.e., Landsat Thematic Mapper (TM) of 1990 and 2010 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site and quantify the changes in the Hawalbagh block from 1990 to 2010 over a period of 20 years. Supervised classification methodology has been employed using maximum likelihood technique in ERDAS 9.3 Software. The images of the study area were categorized into five different classes namely vegetation, agriculture, barren, built-up and water body. The results indicate that during the last two decades, vegetation and built-up land have been increased by 3.51% (9.39 km2) and 3.55% (9.48 km2) while agriculture, barren land and water body have decreased by 1.52% (4.06 km2), 5.46% (14.59 km2) and 0.08% (0.22 km2), respectively. The paper highlights the importance of digital change detection techniques for nature and location of change of the Hawalbagh block.

ผลลัพธ์ (อังกฤษ) 1:

I love the Digital change detection techniques to interpret by using multi-temporal satellite imagery helps in understanding The present study illustrates landscape dynamics. the dynamics of land use/cover spatio-temporal of Almora district of Uttarakhand, block Hawalbagh, India of two different Landsat satellite imageries. time periods, i.e., Landsat Thematic Mapper (TM) of 1990 and 2010 were acquired by Global Land Cover Facility (GLCF) earth explorer Site and quantify the changes in the site and block Hawalbagh from 1990 to 2010, a period of over 20 years Supervised classification methodology has been employed. using maximum likelihood technique in The images of ERDAS Software 9.3. the study area were categorized into five different classes, namely agriculture, barren vegetation, water and built-up body. The results indicate that during the last two decades have been built-up land and vegetation, increased by 3.51% and 3.55% (9.39 km2) (9.48 km2) is land and agriculture, while barren water body have decreased by 1.52% (4.06 km2), and 5.46% (14.59 km2) (0.22 km2) 0.08%, respectively. The paper highlights the importance of digital techniques for change detection and location change of the nature of Hawalbagh block.

ผลลัพธ์ (อังกฤษ) 2:

I love to translate Digital change detection techniques by using multi-temporal satellite imagery helps in understanding landscape dynamics. The present study illustrates the spatio-temporal dynamics of land use / cover of Hawalbagh block of district Almora, Uttarakhand, India. Landsat satellite imageries of. two different time periods, ie, Landsat Thematic Mapper (TM) of 1990 and 2010 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site and quantify the changes in the Hawalbagh block from 1990 to 2010 over a period of 20 years. . Supervised classification methodology has been employed using maximum likelihood technique in ERDAS 9.3 Software. The images of the study area were categorized into five different classes namely vegetation, agriculture, barren, built-up and water body. The results indicate that during the last two. decades, vegetation and built-up land have been increased by 3.51% (9.39 km2) and 3.55% (9.48 km2) while agriculture, barren land and water body have decreased by 1.52% (4.06 km2), 5.46% (14.59 km2) and. 0.08% (0.22 km2), respectively. The paper highlights the importance of digital change detection techniques for nature and location of change of the Hawalbagh block.

ผลลัพธ์ (อังกฤษ) 3:

I love Digital translation change detection techniques by using multi-temporal satellite imagery helps in understanding landscape. Dynamics. The present study illustrates the spatio-temporal dynamics of land use / cover of Hawalbagh block of, district Almora. Uttarakhand India. Landsat, satellite imageries of two different, time periods i.e, Landsat Thematic Mapper (TM) of 1990 and 2010 were. Acquired by Global Land Cover Facility Site (GLCF) and Earth Explorer site and quantify the changes in the Hawalbagh block. From 1990 to 2010 over a period of 20 years. Supervised classification methodology has been employed using maximum likelihood. Technique in ERDAS 9.3 Software. The images of the study area were categorized into five different classes, namely vegetation. ,, agriculture barren built-up and water body. The results indicate that during the last two decades vegetation and, built-up. Land have been increased by 3.51% (9.39 km2) and 3.55% (9.48 km2), while agriculture barren land and water body have decreased. By 1.52% (4.06 km2), 5.46% (14.59 km2) and 0.08% (0.22 km2), respectively. The paper highlights the importance of digital. Change detection techniques for nature and location of change of the Hawalbagh block.