Upscaling Aboveground Biomass Estimation at Low-land Royal Belum Forest Reserve Using Unmanned Aerial Vehicle Image

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

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APPLICATION OF UNMANNED AERIAL VEHICLE IN THE MANAGEMENT OF AKURE FOREST RESERVE IN SOUTHWESTERN NIGERIA

Interdisciplinary Environmental Review ◽

10.1504/ier.2020.10034021 ◽

2020 ◽

Vol 20 (1) ◽

pp. 1

Author(s):

Oladimeji Samuel Popoola ◽

Isaac Adelakun Gbiri ◽

Nathaniel Adeoye

Keyword(s):

Unmanned Aerial Vehicle ◽

Southwestern Nigeria ◽

Forest Reserve ◽

Aerial Vehicle

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Above-ground vegetation indices and yield attributes of rice crop using unmanned aerial vehicle combined with ground truth measurements

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha48412134 ◽

2020 ◽

Vol 48 (4) ◽

pp. 2385-2398

Author(s):

Piyanan PIPATSITEE ◽

Apisit EIUMNOH ◽

Rujira TISARUM ◽

Kanyarat TAOTA ◽

Sumaid KONGPUGDEE ◽

...

Keyword(s):

Plant Height ◽

Unmanned Aerial Vehicle ◽

Aboveground Biomass ◽

Vegetation Indices ◽

Ground Truth ◽

Grain Weight ◽

Yield Traits ◽

Aerial Vehicle ◽

Ground Truthing ◽

Non Destructive

Rice is an important economic and staple crop in several developing countries. Indica rice cultivars, ‘KDML105’ and ‘RD6’ are clear favourites, popular throughout world for their cooking quality, aroma, flavour, long grain, and soft texture, thus consequently dominate major plantation area in Northeastern region of Thailand. The objective of present study was to validate UAV (unmanned aerial vehicle)-derived information of rice crop traits with ground truthing non-destructive measurements in these rice varieties throughout whole life span under field environment. Plant height of cv. ‘KDML105’ was more than cv. ‘RD6’ for each respective stage. Whereas, number of tillers per clump in ‘KDML105’ exhibited stability at each developmental stage, which was in contrast to ‘RD6’ (increased continuously). Moreover, 1,000 grain weight, total grain weight and aboveground biomass were higher in ‘KDML105’ than in ‘RD6’ by 1.20, 1.82 and 3.82 folds. Four vegetative indices, ExG, EVI2, NDVI and NDRE derived from UAV platform proved out to be excellent parameters to compare KDML105 and RD6, especially in the late vegetative and reproductive developmental stages. Positive relationships between NDVI and NDRE, NDRE and total yield traits, as well as NDVI and aboveground biomass were demonstrated. In contrast, total chlorophyll pigment in cv. ‘RD6’ was higher than in cv. ‘KDML105’ leading to negative correlation with NDVI. ‘KDML105’ reflected rapid adaptation to Northeastern environments, leading to maintenance of plant height and yield components. Vegetation indices derived from UAV platform and ground truth non-destructive data exhibited high correlation. ‘KDML105’ was rapidly adapted to NE environments when compared with ‘RD6’, leading to maintenance of physiological parameters (detecting by UAV), the overall growth performances and yield traits (measuring by ground truth method). This study advocates harnessing and adopting the approach of UAV platform along with ground truthing non-destructive measurements of assessing a species/cultivars performance at broad land-use scale.

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