scholarly journals Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR

2021 ◽  
Vol 13 (4) ◽  
pp. 710
Author(s):  
Jordan Steven Bates ◽  
Carsten Montzka ◽  
Marius Schmidt ◽  
François Jonard
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Forest Cover ◽  
Growing Season ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Potential Yield ◽  
Area Index ◽  
Canopy Density ◽  
Plant Area Index

Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.

UAS mounted LiDAR for Estimating LAI Type Metrics for Winter Wheat

2021 ◽  
Author(s):  
Jordan Bates ◽  
Carsten Montzka ◽  
Marius Schmidt ◽  
François Jonard
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Pearson Correlation ◽  
Growing Season ◽  
Unmanned Aircraft ◽  
Area Index ◽  
Canopy Density ◽  
Wheat Field ◽  
Plant Area Index ◽  
Pass Through

<p> </p><p>Metrics such as Leaf Area Index (LAI) are key factors in agricultural monitoring to understand the health and predictive yield of crops. Knowing the spatial distribution and variability in more detail increases the precision of fertilizer and irrigation practices. Unmanned Aircraft Systems (UAS) provide a means to carry sensors at a low altitude below the clouds providing a much higher spatial and temporal resolution than previously seen with satellite remote sensing while also providing more spatially complete data as compared to ground methods. Being that LiDAR is an active sensor and does not depend on solar reflectance and its corresponding zenith angle like commonly used passive optical sensors, it can further improve upon these UAS characteristics. It can also sense further into the canopy as the laser signals can pass through small gaps and are not affected by the shadowing of plant features created by the canopy itself. Evaluating the penetration of these signals and investigating the gap fraction (GF) that relates to canopy density, we are able to retrieve LAI. However, as LiDAR is sensing all above-ground plant elements it may present the ability to estimate Plant Area Index (PAI) rather than LAI when monitoring an entire growing season for a cereal crop like winter wheat that begins browning during senescence. This study investigates the feasibility of using LiDAR to estimate LAI or similar crop canopy density metrics. As LiDAR sensors for UAS are just becoming more accessible, studies related to this topic are scarcely seen.</p><p>In this study, a winter wheat field in Selhausen, Germany (~10 ha in size) was monitored throughout the growing season using the following methods: [1] air campaigns with a DJI Matrice 600 UAS with a YellowScan Surveyor LiDAR system, [2] a DJI Matrice 600 UAS with a Micasense RedEdge-M (five band) multispectral sensor, and [3] ground measurements using a SS1 SunScan ceptometer. The resulting LAI type metrics of the UAS LiDAR methods used were compared to methods commonly used with multispectral (MS) and ground instruments to assess the proposed method’s potential. Additionally, because both products are spatially complete unlike the ground measurements, the LiDAR and multispectral methods were compared for similarities in spatial patterns.</p><p>The results showed promise in using UAS LiDAR to estimate metrics that relate to LAI. Pearson correlation coefficient between the LiDAR and multispectral methods were moderate to high (R= 0.39 – 0.66) over the growing season. The comparison of UAS LiDAR towards the ground reference was within a 3% difference at times before senescence. Later in the growing season, the discrepancy increased between LiDAR and MS sensor retrievals mainly because of plant browning related to changes in plant chlorophyll content. This study covers the benefits of using UAS mounted LiDAR for LAI related measurements and its potential for improving crop health monitoring for precision farming.</p>

Spatial patterns in winter wheat development related to soil properties and historic management. A case study from central Belgium.

2020 ◽  
Author(s):  
Dimitri Goffart ◽  
Klara Dvorakova ◽  
Yannick Curnel ◽  
Quentin Limbourg ◽  
Giacomo Crucil ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Soil Properties ◽  
Spatial Patterns ◽  
Crop Yields ◽  
Growing Season ◽  
Conditional Inference ◽  
Physical Factors ◽  
Field Variation ◽  
Area Index ◽  
Plant Area Index

<p>Intra-field heterogeneity of soil properties is function of complex interaction between biological, physical factors and historic agricultural management. Quantifying the spatial patterns of soil properties such as soil organic carbon (SOC), nitrogen (N), phosphorous, exchangeable cations, pH, soil texture will contribute to an optimization of fertilizer application and crop yields. We tested the capacity of the multispectral Micasense rededge camera mounted on a UAV in order to map the development of winter wheat and related the Red-Edge NDVI (RENDVI) from the sensor to the Plant Area Index (PAI) measured in the field. The geo-referenced grain yield of the winter wheat was measured by a combine harvester and the top soil characteristics analysed by a grid based sampling. The spatial patterns in RENDVI at three phenological stages were mapped together with the yields. For each of these images conditional inference trees were used to derive the soil properties that significantly influenced these spatial patterns. Within-field variation in PAI (cv 41 % in March, 27% in April and 9 % in May) and yield (cv 4%) can be observed. The spatial patterns of RENDVI are rather constant and their correlation with yields is highest in March and April (r=0.64). Soil properties explain between 67 to 79 % of the variance in vegetation index throughout the growing season as well as 67 % of the variance in yield.  Legacy effects of land consolidation two years earlier reflected in the field lay-out, pH and exchangeable K are significant factors explaining around 12-18 % of the variance in crop yield each. The SOC contents were overall low (8-15 g kg-1). Hence, the N supply resulting from SOC mineralization throughout the growing season covered less than 10% of the crop needs and the yield patterns did not reflect the variation in SOC contents.</p>

scholarly journals Biomass accumulation and radiation use efficiency of winter wheat under deficit irrigation regimes

2009 ◽  
Vol 55 (No. 2) ◽  
pp. 85-91 ◽  
Author(s):  
Q. Li ◽  
M. Liu ◽  
J. Zhang ◽  
B. Dong ◽  
Q. Bai
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Deficit Irrigation ◽  
Biomass Accumulation ◽  
Growing Season ◽  
Radiation Use Efficiency ◽  
Area Index ◽  
Irrigation Regimes ◽  
Use Efficiency ◽  
Radiation Use

To better understand the potential for improving biomass accumulation and radiation use efficiency (RUE) of winter wheat under deficit irrigation regimes, in 2006–2007 and 2007–2008, an experiment was conducted at the Luancheng Experimental Station of Chinese Academy of Science to study the effects of deficit irrigation regimes on the photosynthetic active radiation (PAR), biomass accumulation, grain yield, and RUE of winter wheat. In this experiment, field experiment involving winter wheat with 1, 2 and 3 irrigation applications at sowing, jointing, or heading stages was conducted, and total irrigation water was all controlled at 120 mm. The results indicate that irrigation 2 or 3 times could help to increase the PAR capture ratio in the later growing season of winter wheat; this result was mainly due to the changes in the vertical distributions of leaf area index (LAI) and a significant increase of the LAI at 0–20 cm above the ground surface (LSD, <i>P</i> < 0.05). Compared with irrigation only once during the growing season of winter wheat, irrigation 2 times significantly (LSD, <i>P</i> < 0.05) increased aboveground dry matter at maturity; irrigation at sowing and heading or jointing and heading stages significantly (LSD, <i>P</i> < 0.05) improved the grain yield, and irrigation at jointing and heading stages provided the highest RUE (0.56 g/mol). Combining the grain yield and RUE, it can be concluded that irrigation at jointing and heading stages has higher grain yield and RUE, which will offer a sound measurement for developing deficit irrigation regimes in North China.

Effect of Deficit Irrigation on Root-Shoot Development and Grain Yield of Winter Wheat

2014 ◽  
Vol 936 ◽  
pp. 2389-2395
Author(s):  
Bin Hu ◽  
Min Zhang
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Leaf Area Index ◽  
Deficit Irrigation ◽  
North China Plain ◽  
North China ◽  
Ground Surface ◽  
Growing Season ◽  
Area Index ◽  
Growing Seasons

In order to investigate the optimal water-saving and high-efficient irrigation patterns of winter wheat in North China Plain, during 2010－2011 and 2011－2012 winter wheat growing seasons, 3 irrigation treatments were conducted, i.e., irrigated 120 mm only at jointing stage (T1), irrigated 120 mm only at heading stages (T2), and irrigated 60 mm each at jointing and heading stages (T3), respectively, to study the effect of deficit irrigation on root-shoot development and grain yield of winter wheat in North China Plain. The results showed that under the condition of irrigated 120 mm during the winter wheat growing season, the treatment which irrigated 60 mm each at jointing and heading stages, the leaf area index significantly (LSD, P<0.05) increased at milky stage, which was mainly due to increase the leaf area index at 0－20 and more than 60 cm above the ground surface. The 2 growing season results revealed that dry matter accumulation at maturity stage in T3 was significantly (LSD, P<0.05) higher than those in T1 and T2. Compared with T2, the root length density in T1 and T3 were significantly (LSD, P<0.05) higher below the ground surface 50 cm. The results indicated that irrigated 60 mm each at jointing and heading stages during the winter wheat growing seasons, grain yield was the highest, which could be attributed to significantly (LSD, P<0.05) increase the spike numbers. Under the condition of irrigated 120 mm during the winter wheat growing seasons in North China Plain, it is suggests that winter wheat should be irrigated 60 mm each at jointing and heading stages, to achieve reasonable water use efficiency and grain yield.

scholarly journals Airborne and Spaceborne Lidar Reveal Trends and Patterns of Functional Diversity in a Semi-Arid Ecosystem

2021 ◽  
Vol 2 ◽  
Author(s):  
Nayani Ilangakoon ◽  
Nancy F. Glenn ◽  
Fabian D. Schneider ◽  
Hamid Dashti ◽  
Steven Hancock ◽  
...  
Keyword(s):  
Functional Diversity ◽  
Spatial Scales ◽  
Ecosystem Processes ◽  
Airborne Lidar ◽  
Arid Ecosystems ◽  
Arid Ecosystem ◽  
Area Index ◽  
Plant Area Index ◽  
Abiotic Controls ◽  
Semi Arid

Assessing functional diversity and its abiotic controls at continuous spatial scales are crucial to understanding changes in ecosystem processes and services. Semi-arid ecosystems cover large portions of the global terrestrial surface and provide carbon cycling, habitat, and biodiversity, among other important ecosystem processes and services. Yet, the spatial trends and patterns of functional diversity in semi-arid ecosystems and their abiotic controls are unclear. The objectives of this study are two-fold. We evaluated the spatial pattern of functional diversity as estimated from small footprint airborne lidar (ALS) with respect to abiotic controls and fire in a semi-arid ecosystem. Secondly, we used our results to understand the capabilities of large footprint spaceborne lidar (GEDI) for future applications to semi-arid ecosystems. Overall, our findings revealed that functional diversity in this ecosystem is mainly governed by elevation, soil, and water availability. In burned areas, the ALS data show a trend of functional recovery with time since fire. With 16 months of data (April 2019-August 2020), GEDI predicted functional traits showed a moderate correlation (r = 41–61%) with the ALS predicted traits except for the plant area index (PAI) (r = 11%) of low height vegetation (&lt;5 m). We found that the number of GEDI footprints relative to the size of the fire-disturbed areas (=&lt; 2 km2) limited the ability to estimate the full effects of fire disturbance. However, the consistency of diversity trends between ALS and GEDI across our study area demonstrates GEDI’s potential of capturing functional diversity in similar semi-arid ecosystems. The capability of spaceborne lidar to map trends and patterns of functional diversity in this semi-arid ecosystem demonstrates its exciting potential to identify critical biophysical and ecological shifts. Furthermore, opportunities to fuse GEDI with complementary spaceborne data such as ICESat-2 or the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR), and fine scale airborne data will allow us to fill gaps across space and time. For the first time, we have the potential to monitor carbon cycle dynamics, habitats and biodiversity across the globe in semi-arid ecosystems at fine vertical scales.

scholarly journals Robust processing of airborne laser scans to plant area density profiles

2020 ◽  
Vol 17 (23) ◽  
pp. 5939-5952
Author(s):  
Johan Arnqvist ◽  
Julia Freier ◽  
Ebba Dellwik
Keyword(s):  
Beech Forest ◽  
Airborne Lidar ◽  
Small Scale ◽  
Average Intensity ◽  
Forest Site ◽  
Area Index ◽  
Area Density ◽  
Intensity Information ◽  
Plant Area Index ◽  
Plant Area

Abstract. We present a new algorithm for the estimation of the plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods do not use the intensity information at all, use only average intensity values, or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new algorithm to three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full-leaf) and winter (bare-tree) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites. Specific weak points regarding the estimation of the PAD from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms. We further show that low-resolution gridding of the PAD will lead to a negative bias in the resulting estimate according to Jensen's inequality for convex functions and that the severity of this bias is method dependent. As a result, the PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for the PAD gridding of airborne lidar scans.

Paleovegetation Simulations of Lowland Amazonia and Implications for Neotropical Allopatry and Speciation

2001 ◽  
Vol 55 (2) ◽  
pp. 140-149 ◽  
Author(s):  
Sharon A. Cowling ◽  
Mark A. Maslin ◽  
Martin T. Sykes
Keyword(s):  
Amazon Basin ◽  
Forest Cover ◽  
Vegetation Type ◽  
Relative Importance ◽  
Physiological Mechanisms ◽  
Area Index ◽  
Canopy Density ◽  
Last Glaciation ◽  
Highly Sensitive ◽  
Ecosystem Level

AbstractPaleovegetation modeling simulations of the lowland Amazon basin were made to assess the relative importance of glacial climate and atmospheric CO2 for altering vegetation type and structure, as well as to explore the potential physiological mechanisms underlying these ecosystem-level responses. Modeling results support the view that widespread invasion of grasslands into the Amazon lowlands during the last glaciation was not likely. Glacial cooling was probably responsible for maintaining glacial forest cover via its effects in reducing photorespiration and decreasing evapotranspiration, which collectively improve plant carbon and water relations. Modeling results confirm that leaf area index (LAI), a common proxy for canopy density, is highly sensitive to independent and interactive changes in climate and low concentration of atmospheric CO2, and the results show considerable region-to-region variation during the last glaciation. Heterogeneous variations in glacial vegetation LAI may have promoted allopatric speciation by geographically isolating species (called vicariance) in the forest (sub)canopy. The proposed vicariance hypothesis incorporating spatial variations in canopy density conforms to many of the essential tenets addressed by previous neotropical speciation models, but also helps to overcome some of their inconsistencies.

scholarly journals Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat

2018 ◽  
Vol 10 (10) ◽  
pp. 1637 ◽  
Author(s):  
Thomas Meyer ◽  
Lutz Weihermüller ◽  
Harry Vereecken ◽  
François Jonard
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Optical Depth ◽  
Incidence Angle ◽  
Soil Surface ◽  
Growing Season ◽  
Area Index ◽  
Vegetation Height ◽  
L Band

L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (TB) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from TB observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured TB.

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