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Mapping and Monitoring Methods
Published in Ajai, Rimjhim Bhatnagar, Desertification and Land Degradation, 2022
Estimating vegetation biomass production by the field-based method is very tedious and time-consuming and, thus, may not be feasible for large-area assessment, such as national, regional or global coverage. This is where remote sensing comes into the picture. The typical characteristic of green vegetation is its high reflectance in NIR (near-infrared) wave length (due to multiple reflectances within the leaf structure) and high absorption (low reflectance) in the red wavelength band (due to absorption by chlorophyll pigment present in the green leaves). This property has been widely used to develop many vegetation indices which can be used to estimate vegetation green biomass. Vegetation indices, such as ratio index, orthogonal index, normalized difference vegetation index (NDVI), EVI, etc., derived from the EO satellite data, are usually taken as the surrogate of NPP and hence to land productivity (Dorigo et al. 2007). As the name suggests, ratio indices are usually based on the ratio of NIR and red reflectance while orthogonal indices include methods to reduce soil background effects. These indices are easy to compute and save time and efforts. Out of the several vegetation indices as mentioned above, one of the most commonly used index as a proxy to NPP is NDVI, which represents the vegetation vigour and green biomass. It has been shown to be related to biophysical variables that play a controlling role in vegetation productivity and biophysical fluxes (Hall et al. 2006) such as Leaf-Area Index (LAI) (Myneni et al. 1997), NPP (Alexandrov & Oikawa 1997, Rasmussen 1998), vegetation growth status, etc. NDVI has less influence from sun angle and illumination, as well.
A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains
Published in International Journal of Digital Earth, 2019
Hong Jiang, Sen Wang, Xiaojie Cao, Chenghai Yang, Zhaoming Zhang, Xiaoqin Wang
A growing body of research has been conducted during the past half century to develop various vegetation indices. Some commonly-used vegetation indices include the normalized difference vegetation index (NDVI, Rouse et al. 1974), ratio vegetation index (RVI, Jordan 1969), global environment monitoring index (GEMI, Pinty and Verstraete 1992), three-angle-indices (TAI, Fassnacht, Latifi, and Koch 2012), enhanced vegetation index (EVI) and EVI2 (Huete and Liu 1994; Jiang et al. 2008), atmospherically resistant vegetation index (ARVI) (Kaufman and Tanré 1992) and the green ARVI (Gitelson, Kaufman, and Merzlyak 1996), and soil-adjusted vegetation index (SAVI) and its variations (Huete 1988; Qi et al. 1994; Rondeaux, Steven, and Baret 1996; Gilabert et al. 2002). What is more, the global moderate resolution imaging spectroradiometer (MODIS) NDVI and EVI products were designed to provide consistent spatial and temporal comparisons of vegetation conditions and continuity for time series historical applications (Waring et al. 2006; Qiu et al. 2016).
Object based classification using multisensor data fusion and support vector algorithm
Published in International Journal of Image and Data Fusion, 2018
Remotely sensed sensors are able to capture the red and near-infrared waves reflected by the land surfaces. The wavelengths captured in these ranges can be used to study the reflectance properties of vegetation. Certain mathematical formulations, known as vegetation indices, have been developed which facilitate easy identification of vegetation in an image. A vegetation index is an indicator that describes the greenness in terms of relative density and health of the vegetation. The key benefits of the vegetation indices are that these indices enhance the spectral information of the vegetation and increase the separable conditions for the classes of interest. It, therefore, influences the quality of the information derived from the remotely sensed data. The most known and widely used vegetation index is the Normalised Difference Vegetation Index (NDVI) (DrissHaboudane 2002). Using hyperspectral narrow bands, this index is quantified by the following equation where Rx is the reflectance and x is the given wavelength in infrared (700 nm–1100 nm) and visible range (400 nm–700 nm).
Assessment of drought conditions using HJ-1A/1B data: a case study of Potohar region, Pakistan
Published in Geomatics, Natural Hazards and Risk, 2018
Adnan Aziz, Mudassar Umar, Muhammad Mansha, Mehwish Shafi Khan, Muhammad Naveed Javed, Hailiang Gao, Suhaib Bin Farhan, Imran Iqbal, Shaikh Abdullah
Remote sensing-based drought indices have been widely used for drought monitoring and address the issue of spatial context. Generally, remote sensing based techniques for monitoring droughts are indirect as they depend on image based parameters to represent the moisture status in the soil and vegetation when the soil is obscured by vegetation cover (Nichol and Abbas 2015). The optical remote sensing data in wavelength ranges (0.4–2.5 µm) have been used as input to drought indices (Dalezios et al. 2012). In multispectral data spectral ranges: red, near infrared (NIR) and shortwave infrared (SWIR) are commonly used bands due to their response to vegetation greenness and wetness condition using vegetation indices (VIs) (Hazaymeh and Hassan 2016). VIs such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) are used to represent vegetation condition in drought indices because the state of vegetation condition normally indicates the underlying soil moisture content (MAO et al. 2012; Abbas et al. 2014). VI-based drought indices are mainly characterized into Vegetation Condition Index (VCI) (Kogan 2002), Deviation NDVI (DevNDVI) (Berhan et al. 2011), NDVI Anomaly (NDVIA) (Anyamba et al. 2001), and Standardized Vegetation Index (SVI) (Peters et al. 2002). The VCI is considered to be suitable for monitoring agro-droughts and is highly correlated with crop yield (Salazar et al. 2008).