scholarly journals Photogrammetry for assessment of pasture biomass

2019 ◽  
pp. 33-40 ◽  
Author(s):  
Kathryn Wigley ◽  
Jennifer L. Owens ◽  
Matthias Westerschulte ◽  
Paul Riding ◽  
Jaco Fourie ◽  
...  

New tools are required to provide estimates of pasture biomass as current methods are time consuming and labour intensive. This proof-of-concept study tested the suitability of photogrammetry to estimate pasture height in a grazed dairy pasture. Images were obtained using a digital camera from one site on two separate occasions (May and June 2017). Photogrammetry-derived pasture height was estimated from digital surface models created using the photos. Pasture indices were also measured using two currently available methods: a Rising Plate Meter (RPM), and Normalised Difference Vegetation Index (NDVI). Empirical pasture biomass measurements were taken using destructive sampling after all other measurements were made, and were used to evaluate the accuracy of the estimates from each method. There was a strong linear relationship between photogrammetry-derived plant height and actual biomass (R2=0.92May and 0.78June) and between RPM and actual biomass (R2=0.91May and 0.78June). The relationship between NDVI and actual biomass was relatively weaker (R2=0.65May and 0.66June). Photogrammetry could be an efficient way to measure pasture biomass with an accuracy comparable to that of the RPM but further work is required to confirm these preliminary findings.

Author(s):  
M. Possoch ◽  
S. Bieker ◽  
D. Hoffmeister ◽  
A. Bolten ◽  
J. Schellberg ◽  
...  

Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).


2018 ◽  
Vol 36 (3) ◽  
pp. 266-273
Author(s):  
Euseppe Ortiz ◽  
Enrique A. Torres

The use of remote sensing to determine water needs has been successfully applied by several authors to different crops, maintaining, as an important basis, the relationship between the normalized difference vegetation index (NDVI) and biophysical variables, such as the fraction of coverage (fc) and the basal crop coefficient (Kcb). Therefore, this study quantified the water needs of two varieties of coriander (UNAPAL Laurena CL and UNAPAL Precoso CP) based on the response of fc and Kcb, using remote sensors and a water balance according to the FAO-56 methodology. A Campbell Scientific meteorological station, a commercial digital camera and a portable spectro radiometer were used to obtain information on the environmental conditions and the crop. By means of remote sensing associated with a water balance, it was found that the water demand was 156 mm for CL and 151 mm for CP until the foliage harvest (41 d after sowing); additionally, the initial Kcb was 0.14, the mean Kcb was 1.16 (approximately) and the final Kcb was 0.71 (approximately).


2009 ◽  
Vol 60 (9) ◽  
pp. 844 ◽  
Author(s):  
P. D. Fisher ◽  
M. Abuzar ◽  
M. A. Rab ◽  
F. Best ◽  
S. Chandra

Despite considerable interest by Australian farmers in precision agriculture (PA), its uptake has been low. Analysis of the possible financial benefits of alternative management options that are based on the underlying patterns of observed spatial and temporal yield variability in a paddock could increase farmer confidence in adopting PA. The cost and difficulty in collecting harvester yield maps have meant that spatial yield data are generally not available in Australia. This study proposes a simple, economical and easy to use approach to generate simulated yield maps by using paddock-specific relationships between satellite normalised difference vegetation index (NDVI) and the farmer’s average paddock yield records. The concept behind the approach is illustrated using a limited dataset. For each of 12 paddocks in a property where a farmer’s paddock-level yield data were available for 3–5 years, the paddock-level yields showed a close to linear relationship with paddock-level NDVI across seasons. This estimated linear relationship for each paddock was used to simulate mean yields for the paddock at the subpaddock level at which NDVI data were available. For one paddock of 167 ha, for which 4 years of harvester yield data and 6 years of NDVI data were available, the map of simulated mean yield was compared with the map of harvester mean yield. The difference between the two maps, expressed as percentage deviation from the observed mean yield, was <20% for 63% of the paddock and <40% for 78% of the paddock area. For 3 seasons when there were both harvester yield data and NDVI data, the individual season simulated yields were within 30% of the observed yields for over 70% of the paddock area in 2 of the seasons, which is comparable with spatial crop modelling results reported elsewhere. For the third season, simulated yields were within 30% of the observed yield in only 22% of the paddock, but poor seasonal conditions meant that 40% of the paddock yielded <100 kg/ha. To illustrate the type of financial analysis of alternative management options that could be undertaken using the simulated yield data, a simple economic analysis comparing uniform v. variable rate nitrogen fertiliser is reported. This indicated that the benefits of using variable rate technology varied considerably between paddocks, depending on the degree of spatial yield variability. The proposed simulated yield mapping requires greater validation with larger datasets and a wider range of sites, but potentially offers growers and land managers a rapid and cost-effective tool for the initial estimation of subpaddock yield variability. Such maps could provide growers with the information necessary to carry out on-farm testing of the potential benefits of using variable applications of agronomic inputs, and to evaluate the financial benefits of greater investment in PA technology.


2021 ◽  
pp. 79-85
Author(s):  
Kazuma Watanabe ◽  
Nami Kumagai ◽  
Masayuki U. Saito

We evaluated the environment types of raccoon dog latrine sites in the hilly areas of north-eastern Japan. We conducted a route census in the spring and autumn of 2020 to record the latrine sites and analysed the relationship between the presence or absence of latrine sites and environmental factors, namely, topographic position index (TPI), slope, normalised difference vegetation index (NDVI), and vegetation type for each season. To investigate the space use of raccoon dogs, we also conducted camera trapping from July to November 2020 along the spring survey route. We analysed the relationship between the occurrence frequency of raccoon dogs and TPI, slope angle, NDVI, and vegetation type. The analysis showed that latrine sites tended to be located at sites with a high TPI (topography closer to the ridge) in both seasons. However, the occurrence of latrine sites in broadleaf forests was significantly higher in autumn. The frequency of raccoon dogs, based on camera-trap footage, was significantly higher at sites with gentle slopes; although the environment and space used by raccoon dogs at these sites differed. Raccoon dogs possibly select visually and olfactorily conspicuous sites on the ridge as latrine sites to facilitate odour dispersal. In addition, broadleaf forests in autumn are considered important feeding grounds for raccoon dogs, suggesting that the latrine sites were formed near foraging sites.


Author(s):  
M. Possoch ◽  
S. Bieker ◽  
D. Hoffmeister ◽  
A. Bolten ◽  
J. Schellberg ◽  
...  

Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).


2021 ◽  
Author(s):  
Fathielrahaman. H. Ajloon ◽  
Dong Xie ◽  
Shao Junxue ◽  
Zhang RuiTing ◽  
Aniefiok Ini Inayng

Abstract Vegetation cover has an essential role in wetland habitats in controlling avian populations throughout the world. The vegetation cover structure in grassland systems varies dramatically among seasons on the same sites. Variation in vegetation cover-abundance richness and diversity has been studied through one hundred forty-seven quadrate samples during summer and autumn, 2019, winter, and spring 2020. Avian spe cies richness and diversity were recorded during the same period. Meanwhile, correlation analysis results confirmed that: (1) there was no apparent seasonal difference in the abundance of vegetation cover while avian abundance was statistically different. (2) Plant abundance in summer was positively correlated with the number of avian, while in autumn it was negatively correlated. Plant and avian abundance at the genus level showed a positive correlation while maintaining a negative correlation at the speci es level (p < 0.05). However, during summer and autumn, a strong linear relationship exists between vegetation coverage and avian. The Shannon diversity index and Simpson diversity index have a positive linear relationship between vegetation coverage and a vian families and genera. Therefore, we conclude that vegetation coverage and richness significantly impact avian communities. We suggest further research into the relationship between other biological communities and farming practices in the wetlands.


2020 ◽  
Vol 39 (3) ◽  
pp. 87-109 ◽  
Author(s):  
Alfred S. Alademomi ◽  
Chukwuma J. Okolie ◽  
Olagoke E. Daramola ◽  
Raphael O. Agboola ◽  
Tosin J. Salami

AbstractThe Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson's correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.


2021 ◽  
Author(s):  
Fathielrahaman H Ajloon ◽  
Dong Xie ◽  
Shao Junxue ◽  
Zhang RuiTing ◽  
Aniefiok Ini Inayng

Abstract Vegetation cover has an essential role in wetland habitats in controlling avian populations throughout the world. The vegetation cover structure in grassland systems varies dramatically among seasons on the same sites. Variation in vegetation cover-abundance richness and diversity has been studied through one hundred forty-seven quadrate samples during summer and autumn, 2019, winter, and spring 2020. Avian species richness and diversity were recorded during the same period. Meanwhile, correlation analysis results confirmed that: (1) there was no apparent seasonal difference in the abundance of vegetation cover while avian abundance was statistically different. (2) Plant abundance in summer was positively correlated with the number of avian, while in autumn it was negatively correlated. Plant and avian abundance at the genus level showed a positive correlation while maintaining a negative correlation at the species level (p < 0.05). However, during summer and autumn, a strong linear relationship exists between vegetation coverage and avian. The Shannon diversity index and Simpson diversity index have a positive linear relationship between vegetation coverage and avian families and genera. Therefore, we conclude that vegetation coverage and richness significantly impact avian communities. We suggest further research into the relationship between other biological communities and farming practices in the wetlands


2021 ◽  
Author(s):  
Fathielrahaman. H. Ajloon ◽  
Dong Xie ◽  
Shao Junxue ◽  
Zhang RuiTing ◽  
Aniefiok Ini Inayng

Abstract Background: Vegetation cover has an essential role in wetland habitats in controlling avian populations throughout the world. The vegetation cover structure in grassland systems varies dramatically among seasons on the same sites. Variation in vegetation cover-abundance richness and diversity has been studied through one hundred forty-seven quadrate samples during summer and autumn, 2019, winter, and spring 2020. Avian species richness and diversity were recorded during the same period. Results: The correlation analysis results confirmed that: (1) there was no apparent seasonal difference in the abundance of vegetation cover while avian abundance was statistically different. (2) Plant abundance in summer was positively correlated with the number of avian, while in autumn it was negatively correlated. Plant and avian abundance at the genus level showed a positive correlation while maintaining a negative correlation at the species level (p < 0.05). However, during summer and autumn, a strong linear relationship exists between vegetation coverage and avian. The Shannon diversity index and Simpson diversity index have a positive linear relationship between vegetation coverage and avian families and genera. Conclusions: We conclude that vegetation coverage richness significantly impact avian communities. We suggest further research into the relationship between other biological communities and farming practices in the wetlands.


Climate ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 109
Author(s):  
Nikul Kumari ◽  
Ankur Srivastava ◽  
Umesh Chandra Dumka

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.


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