Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

Abstract

Abstract:The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future.

Spatiotemporal nexus between the pattern of land degradation and land cover dynamics in Iran

“…Cultivated land has been converted to built‐up areas due to the increase of migration to urban areas and population growth. This transition is mainly manifested in developing countries (Shafizadeh‐Moghadam & Helbich, ), and Iran is no exception, as reported by local LCC studies in Iran (Minaei & Kainz, ; Mosammam, Tavakoli Nia, Khani, Teymouri, & Kazemi, ; Zanganeh Shahraki et al, ). However, converting wetlands to the built‐up class is not common or expected.…”

“…Due to the progress in remote sensing technology, monitoring the Earth's surface dynamics has been facilitated in an unprecedented way. The intensity, type, and size of LCC have critical importance in our understanding of the Earth's dynamics because LCC has direct and indirect influences on other components of the Earth such as climate, biodiversity, and environmental and natural resources (e.g., Kalnay & Cai, ; Minaei & Kainz, ; Reidsma, Tekelenburg, Van den Berg, & Alkemade, ; Rutten et al, ; Tayyebi, Tayyebi, Jokar Arsanjani, Moghadam, & Omrani, ). Therefore, monitoring LCC and the net LCC of the Earth's surface is a fundamental prerequisite for the study of global change.…”

Improving Identification of Areas for Ecological Restoration for Conservation by Integrating USLE and MCDA in a GIS-Environment: A Pilot Study in a Priority Region Northern Mexico

“…It is important to mention that, the quality of results obtained is often limited by the level of detail of the available data sources i.e., spatial and temporal. In our case, there was only availability of Landsat spectral data at 30 m spatial resolution [66], so the level of detail achieved can be improved; thus, further research can be addressed to use higher spatial resolution sensors i.e., Geoeye, Worldview or Quickbird, in order to find, more accurately, the bare soil land that urgently requires being ecologically restored. However, the large size of study area and the cloudy environment make difficult to build a seamless dataset with high spatial resolution imagery that cover all the study area.…”

Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods

“…In recent years, geographic object-based image analysis (GEOBIA) has become an extensively used classification algorithm, especially after the widespread use of very high-resolution satellite images. The use of this method in the classification of moderate spatial resolution data, such as Landsat, with high accuracy has also become widespread [22][23][24][25]. The main advantage gained by the object-based classification is the segmentation of pixels to form objects, which reduces the within-object heterogeneity problem faced in pixel-based classification methods due to spectral variations in pixels that constitute a single object [26].…”