Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model

Drisya, J.; D, Sathish Kumar; Roshni, Thendiyath

doi:10.1016/b978-0-12-812056-9.00027-0

Cited by 36 publications

(18 citation statements)

References 21 publications

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“…Environmental and topographic variables were chosen after performing a literature review that indicated their relevance for the occurrence of both malaria and vectors [12,17,31]. These included the Normalized Difference Vegetation Index (NDVI), which determines how much near‐infrared light is reflected compared to visible red and helps to evaluate vegetation conditions or to differentiate bare soil from grass or forest [32]; the Normalized Difference Water Index (NDWI) used for assessing the presence of moisture in vegetation cover; changes in NDWI values reflect either sufficient vegetation water content or water stress [33], and the Topographic Wetness Index (TWI), which predicts relative surface wetness; it is an indicator of places where water will tend to accumulate [34]. The variables were obtained from various databases (Table 1), processed in raster format of 1 km 2 resolution and filtered with a mask for the Pacific region.…”

Section: Methodsmentioning

confidence: 99%

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Piedrahita

Altamiranda‐Saavedra

Correa

2020

Tropical Med Int Health

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Objective To categorise and map, at high resolution, the risk of malaria incidence in the Pacific region, the main malaria‐endemic region of Colombia. Methods The relationship between the environmental variables Normalized Difference Vegetation Index Normalized Difference Water Index, Topographic Wetness Index, precipitation and temperature with the observed Annual Parasitic Index was evaluated using a generalised linear model. An incidence risk map at a resolution of 1 km2 was constructed and projected to the entire endemic region. Associations of malaria risk categories with both presence records and co‐occurrence of the three main malaria vectors were determined. Results A significant correlation was found for the incidence of malaria with precipitation and Normalized Difference Vegetation Index (R2 = 0.98, P < 0.05), whereas there was no significant correlation with the remaining environmental and topographic variables. Moderate‐ to high‐risk areas were located mainly in central Chocó Department along the San Juan and Atrato rivers and in areas west of the Cauca River and Pacific lowlands of the Andes Mountains. There was a statistically significant relationship for the presence of the two main vectors Anopheles darlingi and Anopheles nuneztovari with the high malaria risk category. Furthermore, malaria risk was directly proportional to the number of co‐occurring vector species. Conclusions The map obtained provides useful information on the risk of malaria in particular places of the Colombian Pacific region. The data can be used by public entities to optimise the allocation of economic resources for vector control interventions and surveillance.

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Section: Methodsmentioning

confidence: 99%

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Piedrahita

Altamiranda‐Saavedra

Correa

2020

Tropical Med Int Health

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“…We rank each built environment variable in turn and we keep the 10% highest (90th percentile) and 10% lowest (10th percentile) values (see Figure 1). and dense vegetation while lower values indicate sparse vegetation [48]; negative values indicate non-biomass areas such as water. The higher the index, the greener the area [2].…”

Section: Methodsmentioning

confidence: 99%

“…The NDVI measures the density of greenness in each neighborhood and is used as an aesthetic built-environment variable proxy. The NDVI is a typical remote sensing index and ranges between −1 to 1; higher values indicate healthy and dense vegetation while lower values indicate sparse vegetation [48]; negative values indicate non-biomass areas such as water. The higher the index, the greener the area [2].…”

Section: Datamentioning

confidence: 99%

Do Neighborhoods with Highly Diverse Built Environment Exhibit Different Socio-Economic Profiles as Well? Evidence from Shanghai

2021

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The link between the built environment and residential segregation has long been of interest to the discussion for sustainable and socially resilient cities. However, direct assessments on how extensively diverse built environments affect the social landscapes of cities at the neighborhood level are rare. Here, we investigate whether neighborhoods with a diverse built environment also exhibit different socio-economic profiles. Through a geodemographic approach, we scrutinize the socio-economic composition of Shanghai’s neighborhoods. We statistically compare the top 10% (very high values) to the bottom 10% (very low values) of the following built environment variables: density, land use mix, land use balance, and greenness. We show that high-density areas have three times the percentage of divorced residents than low-density areas. Neighborhoods with a high level of greenness have median values of 30% more residents aged between 25–44 years old and five times the percentage of houses between 60 to 119 m2 than low-greenness areas. In high land-use mix areas, the share of people that live on a pension is 30% more than the low land-use mix areas. The findings of this study can be used to improve the designs of modern, sustainable cities at the neighborhood level, significantly improving quality of life.

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“…The NDVI values can help determine the green density of an area observing the unique hues of the reflected visible and nearinfrared light from plants. The higher the NDVI, the healthier and denser the vegetation while the lower the NDVI, the less amount of vegetation present (Drisya et al, 2018). Bands 8 (Near Infrared) and 4 (Red) from the Sentinel-2 data are the bands used in order to calculate the NDVI values per pixel and create an equivalent NDVI map.…”

Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

“…To extract NDVI values from multi-spectral data, the difference of Near Infrared (NIR) and Red (RED) bands is divided by the sum of the two as seen in Equation 1. Values of NDVI range from -1 to +1 (Drisya et al, 2018).…”

Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

Analyzing the Status of Mango Trees in Brgy. Cantipay, Carmen, Cebu Using Ndvi and Time Series Clustering

Navaja¹,

Campomanes²,

Patiño³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The Department of Agriculture – Region VII reports that many mango orchards in Cebu province are dying because of the absence of required post-harvest attention. Lacklustre yields and erratic pest infestations have driven some farmers and growers to abandon mango orchards. To help revive low-yielding mango orchards, there is a need to distinguish actively bearing mango trees from those that remain dormant throughout the year. Using remote sensing techniques, mango trees from separate orchards in Brgy. Cantipay, Carmen, Cebu were mapped and studied using multi-temporal Sentinel-2 data (from January 2018 through May 2019). Prior to that, a field visit was conducted to survey the area using UAVs and field observation, and in the process, was able to identify an abandoned mango orchard. Pixel-based Normal Difference Vegetation Index (NDVI) values were extracted from each of the 822 geotagged mango trees with an average of 16 trees among 53 divisions. Time series were derived from the average of the NDVI values from each division and plotted per month of extraction from oldest to latest. Clustering was applied to the time series data using Hierarchical Clustering with Ward’s Minimum Variance as an algorithm to determine the divisions with the closest time series. Using the resulting dendrogram as basis, two major clusters were selected based on the value of their distances with each other: Cluster 1 containing 29 Divisions, and Cluster 2 containing 24 Divisions. Cluster 1 contains most of the Divisions in and around the biggest active mango orchard. In contrast, Cluster 2 contains most of the Divisions that are in and around the previously identified abandoned mango orchard. An alternative dendrogram was also created by using Complete Linkage algorithm in Hierarchical Clustering, after which 3 relevant clusters were selected. The second dendrogram highlights the stark difference between Division 1, contained in Cluster 3, from the rest of the other clustered divisions at 2.17 units from the next closest one. Notably, Division 1 is located smack in the middle of the abandoned orchard The remaining clusters, Cluster 2 with 21 divisions containing most of the divisions in the abandoned orchard, is 2.46 distance units away from Cluster 1, which has 31 and hosting most of the divisions in the active mango orchards. Two major clusters emerged from using the two algorithms. Divisions with higher and more variant NDVI values seemed to come from the mango trees which were more active during the fruiting cycle. Divisions from the abandoned mango orchards were observed to have lower and less varied NDVI values because of minimal activity in the trees. Other Divisions clustered under the abandoned orchard could have been juveniles based on their size.

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Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model

Cited by 36 publications

References 21 publications

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Spatial fine‐resolution model of malaria risk for the Colombian Pacific region

Do Neighborhoods with Highly Diverse Built Environment Exhibit Different Socio-Economic Profiles as Well? Evidence from Shanghai

Analyzing the Status of Mango Trees in Brgy. Cantipay, Carmen, Cebu Using Ndvi and Time Series Clustering

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