Remotely sensed vegetation productivity predicts breeding activity and drought refuges for a threatened bird in semi‐arid Australia

2022 ◽  
Author(s):  
A. R. Young ◽  
K. E. Selwood ◽  
J. Benshemesh ◽  
J. Wright ◽  
D. Southwell
Keyword(s):  
Remotely Sensed ◽  
Vegetation Productivity ◽  
Breeding Activity ◽  
Semi Arid

Using Remotely Sensed Data To Monitor Land Surface Climatology Variations In A Semi-arid Grassland

2005 ◽  
Author(s):  
L.F. Johnson ◽  
N.A. Bryant ◽  
A.J. BrazeI ◽  
C.F. Hutchinson ◽  
R.C. Balling
Keyword(s):  
Land Surface ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Semi Arid

Assessing the performance of remotely sensed landscape function indices in semi-arid rangelands of Iran

2017 ◽  
Vol 39 (3) ◽  
pp. 253 ◽  
Author(s):  
F. Jafari ◽  
R. Jafari ◽  
H. Bashari
Keyword(s):  
Structural Properties ◽  
Vegetation Cover ◽  
Function Analysis ◽  
Remotely Sensed ◽  
Rangeland Management ◽  
Landscape Function ◽  
Structural And Functional Properties ◽  
Basin Scale ◽  
Arid Rangelands ◽  
Semi Arid

Appropriate rangeland management requires rangeland function analysis at broad scales. This study aimed to examine the potential of remotely sensed function indices extracted from Landsat data to evaluate the function of semi-arid rangelands in central Iran at the sub-basin scale. Three replicate 30-m transects were randomly placed in the dominant slope direction of 14 selected sub-basins. Various structural properties of vegetation (e.g. number and size of vegetation patches and interpatch lengths) and soil surface were scored based on the landscape function analysis (LFA) procedure. The obtained structural and function indices of the LFA, as well as field percent vegetation cover, were compared with the perpendicular distance vegetation index and remotely sensed function indices including proximity, lacunarity, leakiness index, and weighted mean patch size (WMPS). Remotely sensed function indices were found to be capable of discriminating rangeland landscapes with different conditions. Results showed that the structural properties of vegetation considered in the LFA could also be obtained through WMPS and proximity indices (R >0.76; P < 0.01). All indices, except for lacunarity, had significant correlations with percent vegetation cover and the strongest correlation was observed between WMPS and proximity. Our findings highlight the usefulness and efficiency of function indices derived from satellite data in the estimation of structural and functional properties of rangeland landscapes at the sub-basin scale.

Alluvial groundwater recharge estimation in semi-arid environment using remotely sensed data

2017 ◽  
Vol 548 ◽  
pp. 1-15 ◽  
Author(s):  
Victor Hugo R. Coelho ◽  
Suzana Montenegro ◽  
Cristiano N. Almeida ◽  
Bernardo B. Silva ◽  
Leidjane M. Oliveira ◽  
...  
Keyword(s):  
Groundwater Recharge ◽  
Arid Environment ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Alluvial Groundwater ◽  
Recharge Estimation ◽  
Semi Arid

scholarly journals Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

2015 ◽  
Vol XL-7/W3 ◽  
pp. 27-30 ◽  
Author(s):  
H. H. Jaafar ◽  
F. A. Ahmad
Keyword(s):  
Primary Production ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Early Assessment ◽  
Food Shortages ◽  
Semi Arid ◽  
Summer Crop

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

From hillslope to catchment scale hydrologic prediction in a semi-arid region with in-situ observations, satellite soil moisture products, and a distributed catchment model

2020 ◽  
Author(s):  
In-Young Yeo ◽  
Ali Binesh ◽  
Garry Willgoose ◽  
Greg Hancock ◽  
Omer Yeteman
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Limited Region ◽  
Semi Arid Region ◽  
Catchment Model ◽  
Semi Arid

&lt;p&gt;The water-limited region frequently experiences extreme climate variability.&amp;#160; This region, however, has relatively little hydrological information to characterize the catchment dynamics and its feedback to the climate system. This study assesses the relative benefits of using remotely sensed soil moisture, in addition to sparsely available in-situ soil moisture and stream flow observations, to improve the hydrologic understanding and prediction.&amp;#160; We propose a multi-variable approach to calibrate a hydrologic model, Soil and Water Assessment Tool (SWAT), a semi-distributed, continuous catchment model, with observed streamflow and in-situ soil moisture.&amp;#160; The satellite&lt;span&gt; soil moisture products (~ 5 cm top soil) from the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) are then used to evaluate the model estimates of soil moisture over the spatial scales through time.&amp;#160; The results show the model calibrated against streamflow only could provide misleading prediction for soil moisture. &amp;#160;Long term in-situ soil moisture observations, albeit limited availability, are crucial to constrain model parameters leading to improved soil moisture prediction at the given site.&amp;#160; &lt;/span&gt;&lt;span&gt;Satellite soil moisture products &lt;/span&gt;&lt;span&gt;provide useful information to assess simulated soil moisture results across the spatial domains, filling the gap on the soil moisture information at landscape scales.&lt;/span&gt; &lt;span&gt;The preliminary results from this study suggest the potential to produce robust soil moisture and streamflow estimates across scales for a semi-arid region, using a distributed catchment model with in-situ soil network and remotely sensed observations and enhance the overall water budget estimations for multiple hydrologic variables across scales.&amp;#160; &lt;/span&gt;This research is conducted on Merriwa catchment, a semi-arid region located in the Upper Hunter Region of NSW, Australia.&lt;/p&gt;

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