No abstract

Monitoring of land use and land cover change using remote sensing is important to evaluate the impacts of anthropogenic activities on the environment. Digital change detection using post-classification can help to elucidate dynamics of landscape change. This study illustrates the effectiveness of object-oriented classification compared to pixel-oriented classification in generating land cover information and its temporal changes. Spatio-temporal dynamics of land cover types in Vientiane area, Lao PDR were analyzed using Landsat images in two-time series (1990 and 2015). We used the topdown approach to classify the Landsat images in iterative steps with three hierarchical scale levels. Scale levels of 25, 10 and 5 with different weighting parameters were used to map the land cover type of Vientiane in 1990 and 2015. With object-oriented classification, overall accuracy and Kappa statistic were improved by 13.44% and 0.16 for land cover classification (LCC) 1990. For LCC 2015, the improvements in overall accuracy and Kappa statistic were 28.71% and 0.25. Based on the LCC 1990 and 2015, we observed an significant growth of plantation areas over the 25 years in the study area. Instead of traditional agricultural activity, the plantation seemed to be the new driver in the rural areas of Lao PDR. The object-oriented classification approach can be applied in other areas of Lao PDR to generate accurate information on land cover changes for better land resource management.

No abstract

The present study aims to exploit the thermal inertia model to assess the shallow groundwater level distribution, as an attempt to address agricultural water shortage and ecological degradation in arid and semi-arid regions. Apparent thermal inertia (ATI) model is one of the most commonly used and successfully implemented models, whereas in some cases, the scales of the results achieved on different dates are inconsistent, which poses a serious difficulty when monitoring soil moisture over time. To address this restriction, we introduce a soil moisture indicator, called RTI, which derives an approximation of soil thermal inertia, from flexible multi-temporal MODIS observations covering a 2-year period. Taking the Ejina Basin in arid area as an example, to more accurately assess the shallow groundwater level distribution, ATI and RTI models were adopted based on the long time series MODIS data to assess the multi-temporal soil moisture content in the study area. The data of groundwater level measured by observation well and thermal inertia were employed to generate scatter diagrams. Subsequently, a nonlinear equation was developed. Based on the thermal inertia, the temporal and spatial distribution map of the shallow groundwater level in the whole study area was generated with the inverse calculation of the nonlinear equation. It was found that the shallow groundwater level distribution in Ejina Basin tended to be shallower from south to north, as well as from summer to winter. As revealed from the results, both ATI and RTI models can effectively assess the shallow groundwater level in arid areas. The RTI model could better reflect the seasonal changes of soil moisture content and groundwater level.