scholarly journals Automatic classification of land cover change associated with the Brazilian sugarcane expansion over the last decade

2012 ◽  
Author(s):  
Marcio Pupin Mello ◽  
Marcos Adami ◽  
Daniel Alves Aguiar ◽  
Bernardo Friedrich Theodor Rudorff
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Automatic Classification

scholarly journals The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China)

2019 ◽  
Vol 19 (10) ◽  
pp. 2207-2228 ◽  
Author(s):  
Lixia Chen ◽  
Zizheng Guo ◽  
Kunlong Yin ◽  
Dhruba Pikha Shrestha ◽  
Shikuan Jin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Aerial Photographs ◽  
Time Interval ◽  
Human Engineering ◽  
Classification Of Images

Abstract. Land use and land cover change can increase or decrease landslide susceptibility (LS) in the mountainous areas. In the hilly and mountainous part of southwestern China, land use and land cover change (LUCC) has taken place in the last decades due to infrastructure development and rapid economic activities. This development and activities can worsen the slope susceptible to sliding due to mostly the cutting of slopes. This study, taking Zhushan Town, Xuan'en County, as the study area, aims to evaluate the influence of land use and land cover change on landslide susceptibility at a regional scale. Spatial distribution of landslides was determined in terms of visual interpretation of aerial photographs and remote sensing images, supported by field surveys. Two types of land use and land cover (LUC) maps, with a time interval covering 21 years (1992–2013), were prepared: the first was obtained by the neural net classification of images acquired in 1992 and the second by the object-oriented classification of images in 2002 and 2013. Landslide-susceptible areas were analyzed using the logistic regression model (LRM) in which six influencing factors were chosen as the landslide susceptibility indices. In addition, the hydrologic analysis method was applied to optimize the partitioning of the terrain. The results indicated that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land, which is increased by 34.3 %. An increase of 1.9 % is shown in the area where human engineering activities concentrate. The comparison of landslide susceptibility maps among different periods revealed that human engineering activities were the most important factor in increasing LS in this region. Such results emphasize the requirement of a reasonable land use planning activity process.

scholarly journals The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuanen County (Hubei, China)

2019 ◽  
Author(s):  
Lixia Chen ◽  
Zizheng Guo ◽  
Kunlong Yin ◽  
Dhruba Pikha Shrestha ◽  
Shikuan Jin
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Landslide Susceptibility ◽  
Aerial Photographs ◽  
Time Interval ◽  
Human Engineering ◽  
Classification Of Images

Abstract. Land use and land cover change can have effect on the land by increasing/decreasing landslide susceptibility (LS) in the mountainous areas. In the southwestern hilly and mountainous part of China, land use and land cover change (LUCC) has been taking place in the recent past due to infrastructure development and increase in economic activities. These development activities can also bring negative effects: the sloping area may become susceptible to landsliding due to undercutting of slopes. The study aims at evaluating the influence of land use and land cover change on landslide susceptibility at regional scale, based on the application of Geographic Information System (GIS) and Remote Sensing (RS) technologies. Specific objective is to answer the question: which land cover/land use change poses the highest risk so that mitigation measures can be implemented in time? The Zhushan Town, Xuanen County in the southwest of China was taken as the study area and the spatial distribution of landslides was determined from visual interpretation of aerial photographs and remote sensing images, as well as field survey. Two types of land use/land cover (LUC) maps, with a time interval covering 21 years (1992–2013), were prepared: the first was obtained through the neural net classification of images acquired in 1992, the second through the object-oriented classification of images in 2002 and 2013. Landslide susceptible areas were analyzed using logistic regression models. In this process, six landslide influencing factors were chosen as the landslide susceptibility indices. Moreover, we applied a hydrologic analysis method achieving slope unit (SU) delineation to optimize the partitioning of the terrain. The results indicate that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land and the human engineering activities land (HEAL). The areas of these two kind of LUC increased by 34.3 % and 1.9 %, respectively. The comparison of landslide susceptibility maps in various periods revealed that human engineering activities was the most important factor to increase LS in this region. Such results underline that a more reasonable land use planning in the urbanization process is necessary.

scholarly journals ANALISIS PERUBAHAN TUTUPAN LAHAN HUTAN DI IUPHHK-HA PT. GEMA HUTAN LESTARI KABUPATEN BURU PROVINSI MALUKU

2019 ◽  
Vol 3 (2) ◽  
pp. 127-135
Author(s):  
Ibnu R Pattilouw ◽  
G Mardiatmoko ◽  
Ferad Puturuhu
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Large Change ◽  
Forest Degradation ◽  
Landsat 8 ◽  
Open Land ◽  
Management Policies ◽  
Forest Land Cover ◽  
Remote Sensing Methods

The objectives of this study are: identifying land cover, analyzing land cover change, calculating the rate of forest degradation and deforestation, and determining forest management policies. This research was conducted using remote sensing methods and GIS for obtaining land cover change data for several years and conducting mapping. From the research conducted, the results of the classification of forest land cover using Landsat 8 OLI / TIRS imagery in 2013, 2016 and 2018 consisted of forests, shrubs, reeds, swamps, rivers and open land. The highest land cover change that occurred in 2013 up to 2016 covered forests turned into open land amounting to 12261.75 ha. Shrubs into open land also experienced a large change of 12912.25 ha. Forests that have been degraded since 2013-2016 covering an area of ​​17703.07 ha,

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