scholarly journals Impacts of inhomogeneous landscapes in oasis interior on the oasis self-maintaining mechanism by integrating numerical model with satellite data

2012 ◽  
Vol 9 (2) ◽  
pp. 1979-2004
Author(s):  
X. Meng ◽  
S. Lu ◽  
T. Zhang ◽  
Y. Ao ◽  
S. Li ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Layer ◽  
Soil Moisture ◽  
Land Surface ◽  
Negative Impact ◽  
Remote Sensing Data ◽  
Vegetation Fraction ◽  
First Case ◽  
Land Use Types ◽  
Meteorological Modeling

Abstract. Mesoscale meteorological modeling is an important tool to help understand the energy budget of the oasis. While basic dynamic and thermodynamic processes for oasis self-maintaining in the desert environment is well investigated, influence of heterogeneous landscapes of oasis interior on the processes are still important and remain to be investigated. In this study, two simulations are designed for investigating the influence of inhomogeneity. In the first case, land surface parameters including land-use types, vegetation cover fraction, and surface layer soil moisture are derived by satellite remote sensing data from EOS/MODIS, and then be used specify the respective options in the MM5 model, to describe a real inhomogeneity for the oasis interior. In the other run, land use types are set to MM5 default, in which landscapes in the oasis interior is relative uniform, and then surface layer soil moisture and vegetation fraction is set to be averages of the first case for the respective oasis and desert surface lying, to represent a relative homogeneity. Results show that the inhomogeneity leads to a weaker oasis "cold-wet island" effect and a stronger turbulence over the oasis interior, both of which will reduce the oasis-desert secondary circulation and increase the evaporation over the oasis, resulting in a negative impact on the oasis self-protecting mechanism. The simulation of homogeneity indicates that the oasis may be more stable even with relative lower soil moisture if landscapes in the oasis interior are comparatively uniform.

Estimating the Effects of DEM and Land Use Types on Soil Moisture Using HJ-1A CCD/IRS Images: A Case Study in Minhou County

2013 ◽  
Vol 726-731 ◽  
pp. 4572-4576 ◽  
Author(s):  
Yu Qin Liu ◽  
Jin Ming Sha ◽  
De Sheng Wang
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indexes ◽  
Land Use Types ◽  
Research Findings ◽  
Soil Moisture Status

Soil moisture is of great significance for regional resources and environments. The combination of land surface temperature (Ts) and vegetation index (VI) is appropriate for monitoring the regional surface soil moisture status. In this study, we employed HJ-1B CCD/IRS images,DEMand land use types to obtain the information about soil moisture for Minhou county in FuZhou. Firstly,TVDIreflected the soil moisture status was analyzed with in-situ soil moisture measurements based on two kinds of different vegetation indexes (NDVI/EVI). Secondly, the relationship betweenTVDIandDEMwas analyzed. Finally, the soil moisture status of each land use type was explored combined with the main land use types of study area. Research findings indicate that: (1)TVDIcan effectively reflect the spatial pattern of soil moisture andTs/EVIhas a higher accuracy thanTs/NDVI; (2) the spatial distribution of soil moisture is obviously affected by the altitude; (3) there exists correlationship between soil moisture and land use types in study area.

scholarly journals Contrasting roles of interception and transpiration in the hydrological cycle – Part 1: Simple Terrestrial Evaporation to Atmosphere Model

2014 ◽  
Vol 5 (1) ◽  
pp. 203-279 ◽  
Author(s):  
L. Wang-Erlandsson ◽  
R. J. van der Ent ◽  
L. J. Gordon ◽  
H. H. G. Savenije
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Land Use Change ◽  
Time Scale ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Open Water ◽  
Climate Conditions ◽  
Land Use Types ◽  
Atmosphere Model

Abstract. Terrestrial evaporation consists of biophysical (i.e., transpiration) and physical fluxes (i.e., interception, soil moisture, and open water). The partitioning between them depends on both climate and the land surface, and determines the time scale of evaporation. However, few land-surface models have analysed and evaluated evaporative partitioning based on land use, and no studies have examined their subsequent paths in the atmosphere. This paper constitutes the first of two companion papers that investigate the contrasting effects of interception and transpiration in the hydrological cycle. Here, we present STEAM (Simple Terrestrial Evaporation to Atmosphere Model) used to produce partitioned evaporation and analyse the characteristics of different evaporation fluxes on land. STEAM represents 19 land-use types (including irrigated land) at sub-grid level with a limited set of parameters, and includes phenology and stress functions to respond to changes in climate conditions. Using ERA-Interim reanalysis forcing for the years 1999–2008, STEAM estimates a mean global terrestrial evaporation of 73 800 km3 year−1, with a transpiration ratio of 59%. We show that the terrestrial residence time scale of transpiration (days to months) has larger inter-seasonal variation and is substantially longer than that of interception (hours). Furthermore, results from an offline land-use change experiment illustrate that land-use change may lead to significant changes in evaporative partitioning even when total evaporation remains similar. In agreement with previous research, our simulations suggest that the vegetation's ability to transpire by retaining and accessing soil moisture at greater depth is critical for sustained evaporation during the dry season. Despite a relatively simple model structure, validation shows that STEAM produces realistic evaporative partitioning and hydrological fluxes that compare well with other global estimates over different locations, seasons and land-use types. We conclude that the simulated evaporation partitioning by STEAM is useful for understanding the links between land use and water resources, and can with benefit be employed for atmospheric moisture tracking.

Measuring pedestrian volume by land use mix: Presenting a new entropy-based index by weighting walking generation units

2019 ◽  
Vol 47 (7) ◽  
pp. 1219-1236 ◽  
Author(s):  
Ha Na Im ◽  
Chang Gyu Choi
Keyword(s):  
Land Use ◽  
Urban Studies ◽  
Negative Impact ◽  
Explanatory Power ◽  
Built Environments ◽  
Estimation Model ◽  
Land Use Types ◽  
Land Use Mix ◽  
The Relationship ◽  
The Empirical Analysis

This study proposes an alternative to the conventional entropy-based land use mix index, which is generally used to measure the diversity of land use. Pedestrian volume was selected as the dependent variable as it represents the vitality of districts, which many recent urban studies now consider important. The study investigates an entropy-based weighted land use mix index, which is weighted by different land use types. For the index, different areas are needed to generate a unit of pedestrian volume, whose measure is m2/person/day. The study demonstrates that this alternative is more effective than the existing conventionally used entropy-based land use mix index for explaining pedestrian volume. The research confirms that the conventionally used entropy-based land use mix index can have a positive or negative impact depending on the land use characteristics of the survey points because the conventionally used entropy-based land use mix index has a non-linear relationship with pedestrian volume. By analysing 9727 surveyed locations of pedestrian volume in Seoul, Korea, the study demonstrates that the weighted land use mix index, rather than the conventionally used entropy-based land use mix index, can improve the explanatory power of the estimation model for the relationship between pedestrian volume and built environments, showing consistent results throughout the empirical analysis. In future built-environment studies, the utility of the weighted land use mix index is expected to improve if studies include how to find the accurate weighting of the land use in estimating the pedestrian volume.

scholarly journals Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3223
Author(s):  
Hamed Adab ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Mahmoud Moradian ◽  
Gholam Abbas Fallah Ghalhari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Machine Learning Methods ◽  
Land Use Types ◽  
Semi Arid Region ◽  
Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

scholarly journals The InVEST Habitat Quality Model Associated with Land Use/Cover Changes: A Qualitative Case Study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia

2020 ◽  
Vol 12 (7) ◽  
pp. 1103 ◽  
Author(s):  
Abreham Berta Aneseyee ◽  
Tomasz Noszczyk ◽  
Teshome Soromessa ◽  
Eyasu Elias
Keyword(s):  
Land Use ◽  
Habitat Quality ◽  
Global Economy ◽  
Habitat Degradation ◽  
Remote Sensing Data ◽  
Impact Factors ◽  
Conservation Strategies ◽  
Anthropogenic Pressure ◽  
Quality Model ◽  
Land Use Types

The contribution of biodiversity to the global economy, human survival, and welfare has been increasing significantly, but the anthropogenic pressure as a threat to the pristine habitat has followed. This study aims to identify habitat suitability, analyze the change in habitat quality from 1988 to 2018, and to investigate the correlation between impact factors and habitat quality. The InVEST habitat quality model was used to analyze the spatiotemporal change in habitat quality in individual land-use types in the Winike watershed. Remote sensing data were used to analyze the land use/land cover changes. Nine threat sources, their maximum distance of impact, mode of decay, and sensitivity to threats were also estimated for each land-use cover type. The analysis illustrates that habitat degradation in the watershed was continuously increasing over the last three decades (1988 to 2018). Each threat impact factor and habitat sensitivity have increased for the last 30 years. The most contributing factor of habitat degradation was the 25.41% agricultural expansion in 2018. Population density, land-use intensity, elevation, and slope were significantly correlated with the distribution of habitat quality. Habitat quality degradation in the watershed during the past three decades suggested that the conservation strategies applied in the watershed ecosystem were not effective. Therefore, this study helps decision makers, particularly regarding the lack of data on biodiversity. It further looks into the conflict between economic development and conservation of biodiversity.

The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

2011 ◽  
Vol 42 (2-3) ◽  
pp. 95-112 ◽  
Author(s):  
Venkat Lakshmi ◽  
Seungbum Hong ◽  
Eric E. Small ◽  
Fei Chen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Area Index ◽  
Vegetation Fraction ◽  
Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

Sign in / Sign up

Export Citation Format

Share Document