scholarly journals Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

2019 ◽  
Vol 11 (3) ◽  
pp. 244 ◽  
Author(s):  
Gaofei Yin ◽  
Aleixandre Verger ◽  
Yonghua Qu ◽  
Wei Zhao ◽  
Baodong Xu ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Network ◽  
Growth And Yield ◽  
Wireless Sensor ◽  
Observation System ◽  
Spatiotemporal Resolution ◽  
Area Index ◽  
High Spatiotemporal Resolution

Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.

scholarly journals Validation of Leaf Area Index Measurement System Based on Wireless Sensor Network

2021 ◽  
Author(s):  
Rongjin Yang ◽  
Lu Liu ◽  
Qiang Liu ◽  
Xiuhong Li ◽  
Lizeyan Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wireless Sensor Network ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Network ◽  
Measurement System ◽  
Wireless Sensor ◽  
Area Index ◽  
The Real ◽  
Real Value

Abstract Accurate measurement of leaf area index (LAI) is important for agricultural analysis such as the estimation of crop yield, which makes its measurement work important. There are mainly two ways to obtain LAI: ground station measurement and remote sensing satellite monitoring. Recently, reliable progress has been made in long-term automatic LAI observation using wireless sensor network (WSN) technology under certain conditions. We developed and designed an LAI measurement system (LAIS) based on a wireless sensor network to select and improve the appropriate algorithm according to the image collected by the sensor, to get a more realistic leaf area index. The corn LAI was continuously observed from May 30 to July 16, 2015. Research on hardware has been published, this paper focuses on improved system algorithm and data verification. By improving the finite length average algorithm, the data validation results are as follows: 1. The slope of the fitting line between LAIS measurement data and the real value is 0.944, and the root means square error (RMSE) is 0.264 (absolute error ~ 0-0.6), which has high consistency with the real value. 2. The measurement error of LAIS is less than LAI2000, although the result of our measurement method will be higher than the actual value, it is due to the influence of weeds on the ground. 3. LAIS data can be used to support the retrieval of remote sensing products. We find a suitable application situation of our LAIS system data, and get our application value as ground monitoring data by the verification with remote sensing product data, which supports its application and promotion in similar research in the future.

scholarly journals Growth and yield of wheat at different dates of sowing under chrland ecosystem of Bangladesh

2017 ◽  
Vol 14 (2) ◽  
pp. 147-154 ◽  
Author(s):  
MM Kamrozzaman ◽  
MAH Khan ◽  
S Ahmed ◽  
N Sultana
Keyword(s):  
Growth Rate ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Matter ◽  
Block Design ◽  
Grain Filling ◽  
Crop Growth ◽  
Growth And Yield ◽  
Area Index ◽  
Crop Growth Rate

An experiment was conducted at Sadipur charland under Farming System Research and Development Site, Hatgobindapur, Faridpur, during rabi season of 2012-13 and 2013-14 to study the growth and yield performance of cv. BARI Gom-24 as affected by different dates of sowing under Agro-ecological Zone-12 (AEZ-12) of Bangladesh. The experiment was laid out in randomized complete block design with six replications, comprising five different dates of sowing viz. November 5, November 15, November 25, December 5 and December 15. Results reveal that the tallest plant, leaf area index, total dry matter, and crop growth rate were observed in November 25 sown crop and leaf area index, total dry matter and crop growth rate were higher at booting, grain filling, and tillering stages of the crop. Maximum effective tillers hill-1 (3.49), spikes m-2, (311), number of grains spike-1 (42.20) and 1000-grain weight (52.10 g) were produced by November 25 sown crop exhibited the highest grain (4.30 t ha-1) and straw yield (4.94 t ha-1) as well as harvest index (46.88%) of the crop. Lowest performance was observed both in early (November 5) and late sown crop (December 15). The overall results indicated that November 25 sown crop showed better performance in respect of growth and yield of wheat under charland ecosystem of Bangladesh.J. Bangladesh Agril. Univ. 14(2): 147-154, December 2016

scholarly journals Extracting Leaf Area Index Using Viewing Geometry Effects - A New Perspective on High-Resolution Unmanned Aerial System Photography.

2020 ◽  
Author(s):  
Lukas Roth ◽  
Helge Aasen ◽  
Achim Walter ◽  
Frank Liebisch
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Ecology ◽  
Growth And Yield ◽  
Unmanned Aerial System ◽  
Area Index ◽  
Handheld Device ◽  
Gap Fraction ◽  
Rgb Images ◽  
New Perspective

Abstract Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI 14 prediction accuracy was comparable with the one of a gap fraction-based handheld device (R2 of 0.92, RMSE of 0.42 m2 m2) and correlated well with destructive LAI measurements (R2 of 0.89, RMSE of 0.41 m2 m2). These results indicate that, if respecting the range (LAI ≤3) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science.

Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

2006 ◽  
Vol 82 (2) ◽  
pp. 159-176 ◽  
Author(s):  
R J Hall ◽  
F. Raulier ◽  
D T Price ◽  
E. Arsenault ◽  
P Y Bernier ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Forest Productivity ◽  
Process Models ◽  
Growth And Yield ◽  
Area Index ◽  
Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

Field Competition between Ivyleaf Morningglory (Ipomea hederacea) and Soybeans (Glycine max)

Weed Science ◽  
1984 ◽  
Vol 32 (3) ◽  
pp. 364-370 ◽  
Author(s):  
Ronald C. Cordes ◽  
Thomas T. Bauman
Keyword(s):  
Soil Moisture ◽  
Glycine Max ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Weight ◽  
Growth And Yield ◽  
Yield Reduction ◽  
Area Index ◽  
Soybean Yield ◽  
Competition Effects

Detrimental effects on growth and yield of soybeans [Glycine max(L.) Merr. ‘Amsoy 77′] from density and duration of competition by ivyleaf morningglory [Ipomea hederacea(L.) Jacq. ♯3IPOHE] was evaluated in 1981 and 1982 near West Lafayette, IN. Ivyleaf morningglory was planted at densities of 1 plant per 90, 60, 30, and 15 cm of row in 1981 and 1 plant per 60, 30, 15, and 7.5 cm of row in 1982. Each density of ivyleaf morningglory competed for 22 to 46 days after emergence and the full season in 1981, and for 29 to 60 days after emergence and the full season in 1982. The best indicators of competition effects were leaf area index, plant dry weight, and yield of soybeans. Ivyleaf morningglory was more competitive during the reproductive stage of soybean growth. Photosynthetic irradiance and soil moisture measurements indicated that ivyleaf morningglory does not effectively compete for light or soil moisture. All densities of ivyleaf morningglory could compete with soybeans for 46 and 60 days after emergence in 1981 and 1982, respectively, without reducing soybean yield. Full-season competition from densities of 1 ivyleaf morningglory plant per 15 cm of row significantly reduced soybean yield by 36% in 1981 and 13% in 1982. The magnitude of soybean growth and yield reduction caused by a given density of ivyleaf morningglory was greater when warm, early season temperatures favored rapid weed development.

scholarly journals Impact of Vermicompost, Poultry Manure and Jeevaamrit on Growth Parameters of Kiwifruit (Actinidia deliciosa) cv. Allison

2020 ◽  
pp. 31-40
Author(s):  
Ashok K. Garg ◽  
Rajesh Kaushal ◽  
Vishal S. Rana
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Photosynthetically Active Radiation ◽  
Poultry Manure ◽  
Growth And Yield ◽  
Recommended Dose ◽  
Inorganic Fertilizers ◽  
Area Index ◽  
Active Radiation ◽  
Chlorophyll Index

The present investigation was conducted on 6 years old kiwifruit vines cultivar ‘Allison’ at a spacing of 4.0 m × 6.0 m for two consecutive years 2018-19 and 2019-20 at experimental block of Department of Fruit Science, Dr YS Parmar University of Horticulture and Forestry, Nauni, Solan (HP). The experiment was laid out in triplicate in Randomized Block Design with 8 treatments under three farming systems viz., Inorganic Fertilizer Based System (IFBS), Organic Farming Based System (OFBS) and Subhash Palekar’s Natural Farming System (SPNFS). The maximum leaf area (158.1 cm2), leaf area index (4.36), chlorophyll index (51.2), comparative photosynthetically active radiation (612 µ mol quanta m-2 s-1) was found in the treatment (T8) receiving 30 liters of jeevaamrit (JM) + 3 kg ghana jeevaamrit and 40 kg FYM per vine under SPNFS. Among OFBS, the treatment T2 (100% recommended dose of nitrogen (RDN) through vermicompost and poultry manure on 50:50 basis) observed maximum leaf area (151.8 cm2), leaf area index (4.35), comparative photosynthetically active radiation (642 µ mol quanta m-2 s-1) but lower significantly lower chlorophyll index (51.2) over T1 (Recommended dose of inorganic fertilizers + FYM) treatment of IFBS. Hence application of 30 litres jeevaamrit and 3 kg ghana jeevaamrit (both in 3 equal splits first in end of January, second in February and third in the month of April) along with 40 kg FYM per vine or alternatively substitution of 100% RDN through vermicompost and poultry manure on 50:50 basis along with 40 kg FYM were found to be best and alternate different option in place of inorganic fertilizers to ‘Allison’ cultivar of kiwifruit under mid-hill conditions of Himachal Pradesh, India. Furthermore, the research emphases mainly on improving soil health without compromising growth and yield of kiwifruits in the region. By using alternative sources of nutrients, farmers can obtain the comparable growth and yield of kiwifruits.

Sign in / Sign up

Export Citation Format

Share Document