scholarly journals DETERMINING VEGETATION COVER BASED ON FIELD DATA AND MULTI-SCALE REMOTELY SENSED DATA

2007 ◽  
Vol 31 (5) ◽  
pp. 842-849 ◽  
Author(s):  
ZHANG Yun-Xia ◽  
◽  
◽  
◽  
LI Xiao-Bing ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Field Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Multi Scale

scholarly journals Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabelo Nick Dlamini ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
Environmental Factors ◽  
Spatial Resolution ◽  
Malaria Vector ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Breeding Sites ◽  
Multi Scale ◽  
The Relationship

Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude, on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites.

Grazing Gradients in Central Australian Rangelands: Ground Verification of Remote Sensing-Based Approaches.

1993 ◽  
Vol 15 (2) ◽  
pp. 217 ◽  
Author(s):  
GN Bastin ◽  
AD Sparrow ◽  
G Pearce
Keyword(s):  
Satellite Data ◽  
Vegetation Cover ◽  
Field Data ◽  
Graphical Representation ◽  
Woody Species ◽  
Soil Surface ◽  
Grazing Pressure ◽  
Remotely Sensed Data ◽  
Suitable Technique ◽  
Grazing Behaviour

Remotely-sensed data collected by satellites have been proposed for investigating grazing effects across the large paddocks of arid Australia. These data are used to compute indices of vegetation cover which are then analysed with reference to patterns of grazing behaviour around watering points. Grazing pressure typically increases as water is approached, resulting in a decrease in herbage cover. This pattern of cover change is called a grazing gradient. The change in these gradients from a dry to wet period forms the basis for assessing land degradation as described in an accompanying paper. This study demonstrates that grazing gradients do exist, that they can be detected with field-based methods of data collection, and that there is close correspondence between ground data and indices of vegetation cover obtained from contemporary Landsat Multispectral Scanner satellite data. Field data representing aerial cover of the herbage and woody species layers were collected along transects radiating away from water at two sites grazed by cattle in central Australia. Graphical representation of the litter and herbage components demonstrate that gradients of decreasing cover attributable to increasing grazing pressure occur along all, or sections, of each transect. Highly significant correlations exist between the field data and satellite indices of vegetation cover. Localised shrub increase and patches of recent erosion obscured trends of increasing cover with distance from water on parts of some transects. Soil surface state (describing past erosion) was a significant covariate of cover change at one site. Our ability to characterise gradients of increasing vegetation cover with distance from water using both field and satellite data should mean that the grazing gradient method, when used with satellite data, is a suitable technique for assessing the extent of landscape recovery following good rainfall.

A combined approach for estimating vegetation cover in urban/suburban environments from remotely sensed data

2006 ◽  
Vol 32 (9) ◽  
pp. 1299-1309 ◽  
Author(s):  
Chen Yunhao ◽  
Shi Peijun ◽  
Li Xiaobing ◽  
Chen Jin ◽  
Li Jing
Keyword(s):  
Vegetation Cover ◽  
Remotely Sensed ◽  
Combined Approach ◽  
Remotely Sensed Data

scholarly journals A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone

2021 ◽  
Vol 13 (19) ◽  
pp. 3806
Author(s):  
Angela Cotugno ◽  
Virginia Smith ◽  
Tracy Baker ◽  
Raghavan Srinivasan
Keyword(s):  
Sierra Leone ◽  
Urban Areas ◽  
Field Data ◽  
Curve Number ◽  
Remotely Sensed ◽  
Peak Discharge ◽  
Urban Rivers ◽  
Remotely Sensed Data ◽  
Precipitation Events

As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work contains a case study for seven urban rivers in Freetown, Sierra Leone.

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