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Analyzing Land-Use/Land-Cover Changes and Its Impact on Land Surface Temperature in Berhampore Municipality, West Bengal, India
Published in Uday Chatterjee, Arindam Biswas, Jenia Mukherjee, Dinabandhu Mahata, Sustainable Urbanism in Developing Countries, 2022
Hassan Momin, Chandrakala Tamang, Rubia Biswas, Abdul Odud
NDVI (Normalized Difference Vegetation Index) is used to calculate the area of vegetation health. NDVI varies from −1 to 1, where the value −1 indicates low vegetation coverage and 1 indicates high vegetation coverage. Rouse et al. (1974) used the following equation: NDVI=NearIR–R/NearIR+R where Near IR = near infrared band; R = red band.
Small Unmanned Aerial Systems (sUAS) and Structure from Motion for Identifying, Documenting, and Monitoring Cultural and Natural Resources
Published in J.B. Sharma, Applications of Small Unmanned Aircraft Systems, 2019
Marguerite Madden, Thomas Jordan, Sergio Bernardes, Cari Goetcheus, Kristen Olson, David Cotten
Following image acquisition and processing, a variety of metrics can be computed and analyzed in conjunction with other data collected in the field. Multispectral systems may have a few or many spectral bands. A variety of works describing vegetation have incorporated systems that are sensitive to the visible part of the electromagnetic spectrum (red, green, and blue spectral bands) and include bands involving longer wavelengths, such as red-edge and near-infrared (Jensen 2007). Observations in the red-edge and near-infrared regions of the electromagnetic spectrum provide insights into plant stress and structure and a variety of indices can be derived from these bands, including those descriptive of vegetation status, such as the Normalized Difference Vegetation Index (NDVI) = (near infrared band − red band)/(near infrared band + red band). Values of NDVI range from −1 to +1 and green, healthy vegetation tends to show high NDVI values. Individual bands or derived indices can be used in the creation of mosaics that show the distribution of vegetation quantities and/or status over an area.
Mapping and Monitoring of Mangrove Forests of the World Using Remote Sensing
Published in Lisamarie Windham-Myers, Stephen Crooks, Tiffany G. Troxler, A Blue Carbon Primer, 2018
Similarly, Landsat imagery was used to map mangrove damage caused by Typhoon Haiyan in November 8, 2013, in the Philippines (Long et al. manuscript underdevelopment). The Normalized Difference Vegetation Index (NDVI) was used as a standardized measure. NDVI is one of the most widely used vegetation indexes (Tucker 1979) to measure and monitor plant growth, vegetation cover, and biomass production. NDVI values range from 0.1 to 1.0 with dense vegetated areas (e.g., closed canopy tropical forest) generally yielding high NDVI values (0.6–0.8), sparsely vegetated areas (e.g., open shrub and grasslands) yielding moderate values (0.2–0.3), and non-vegetated (e.g., rock, sand, and snow) yielding low NDVI values (0.1 and below). Numerous studies have employed repeated measures of NDVI to monitor mangrove vegetation response from varying disturbances (Giri et al. 2011a), but few studies have applied this approach to monitor mangrove disturbance from typhoons (Wang 2012) and still fewer at a 30-m spatial resolution.
GIS-based evolution and comparisons of landslide susceptibility mapping of the East Sikkim Himalaya
Published in Annals of GIS, 2022
Neha Gupta, Sanjit Kumar Pal, Josodhir Das
NDVI was used to identify the vegetation on the surface and was calculated using the formula NDVI = (IR-R)/(IR+R), where IR represents the infrared band and R represents the red band (Ba et al. 2018; Behling et al. 2014; Mishra and Sarkar 2020). The NDVI map was prepared using Landsat 8 OLI (Figure 4(k)). The NDVI value varies from −1 to +1, where the negative value indicates the area of non-vegetation and the positive value shows the vegetation area. The LULC map was prepared from Landsat 8 OLI of the 2019 satellite image based on supervised classification techniques in ERDAS Imagine software, which was also verified with BHUVAN and Google Earth data. All the classes were digitized with the help of visual interpretation and NDVI of Landsat 8. The LULC map was classified into six classes, i.e. settlement, agriculture land, barren land, medium forest, thick forest and spare forest (Figure 4(l)). Classification accuracy indicated that the kappa coefficient was 80.5%, which predicted good accuracy. The classified map showed the settlement of 14.021%, an agriculture land of 3.003%, a medium forest of 28.45%, a thick forest of 27.43%, a spare forest of 12.47% and a barren land of 14.81%
Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt
Published in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2021
Sara M. Abuzied, Biswajeet Pradhan
The land use factor (S7) was used to estimate the effect of vegetation cover on erosion intensity. The land use factor was created based on the percentage of plant canopy at each land use class (Pc) using Equations (9–11). The percentage of plant canopy (Pc) could be obtained (Equation (10)) from the relation with Normalised Difference Vegetation Index (NDVI). The NDVI could be created using remotely sensed data such as LANDSAT satellites. The NDVI is essentially reliant on the area of vegetation which was generated based on the near-infrared band (NIR) and red band showing the maximum and minimum reflection of electromagnetic energy (Equation (11)). Generally, the NDVI varies theoretically between −1 and 1 which the highest estimate is allocated to cultivated lands (Figure 11).
Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach
Published in Geomatics, Natural Hazards and Risk, 2021
Jean Moussa Kourouma, Emmanuel Eze, Emnet Negash, Darius Phiri, Royd Vinya, Atkilt Girma, Amanuel Zenebe
The positive correlations between anomalies of NDVI and values of rainfall portray the positive response of photosynthetic activity to rainfall. A positively-inclined linear relationship between NDVI and rainfall is reported in earlier studies (Lamchin 2004; Li et al. 2004; Richard and Poccard 2010). In another twist, a decrease in vegetation cover led to a decrease in the rainfall pattern (Batool et al. 2015). Thus, agricultural drought is inversely related to the amount of precipitation received by an area, which in turn, influences the vegetation health condition and translates to the productivity of crops directly (Martiny et al. 2006). Moreover, NDVI measures the state and health of crops and has a high correlation with crop yield, which means it can be used as a tool for measuring crop productivity and predicting future yield.