Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

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Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

Applied Engineering in Agriculture ◽

10.13031/aea13875 ◽

2020 ◽

Vol 36 (4) ◽

pp. 557-564

Author(s):

LingHan Cai ◽

Yuan Zhao ◽

Zhuojue Huang ◽

Yang Gao ◽

Han Li ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Color Space ◽

List Type ◽

Area Index ◽

Average Value ◽

Rapid Measurement ◽

Canopy Coverage ◽

Lab Color Space ◽

Extraction Model

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

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Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Sensors ◽

10.3390/s19051027 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1027 ◽

Cited By ~ 6

Author(s):

Theodora Lendzioch ◽

Jakub Langhammer ◽

Michal Jenicek

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Snow Depth ◽

Control Point ◽

Winter Season ◽

Snow Accumulation ◽

Plant Canopy ◽

Coverage Area ◽

Area Index ◽

Uav Images

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

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Multi-temporal monitoring of leaf area index of rice under different nitrogen treatments using UAV images

International Journal of Precision Agricultural Aviation ◽

10.33440/j.ijpaa.20200301.57 ◽

2018 ◽

Vol 1 (1) ◽

pp. 7-12

Author(s):

Xiaoyue Du ◽

Liang Wan ◽

Haiyan Cen ◽

Shuobo Chen ◽

Jiangpeng Zhu ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Multi Temporal ◽

Uav Images ◽

Nitrogen Treatments

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Analisis Tutupan Mangrove Taman Nasional Berbak–Sembilang melalui Citra Landsat-8 dan Pemantauan LAI

Jurnal Kelautan Tropis ◽

10.14710/jkt.v24i3.12278 ◽

2021 ◽

Vol 24 (3) ◽

pp. 393-401

Author(s):

Tengku Zia Ulqodry ◽

Andreas Eko Aprianto ◽

Andi Agussalim ◽

Riris Aryawati ◽

Afan Absori

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Landsat 8 ◽

Area Index ◽

Sensing Data ◽

Canopy Coverage

Berbak Sembilang National Park of South Sumatra Region (BSNP South Sumatera) is the largest mangrove ecosystem in the western part of Indonesia. Monitoring of mangrove coverage in BSNP South Sumatera carried out using Landsat-8 imagery data based on NDVI values (Normalized Difference Vegetation Index) integrated with mangrove LAI (Leaf Area Index) data. The research purpose was to analyze the mangrove coverage and mapping the density of the mangrove vegetation canopy with the integration of remote sensing data and LAI. This research conducted field survey with LAI measurement of mangrove canopy coverage and integrated with remote sensing data to validate map. The determination and correlation coefficient of NDVI and LAI value of canopy coverage was high (R2 = 0.69 ; r = 83.07).The results of research indicated that the overall distribution of the mangrove area was 94,622.05 ha. The NDVI image integration map with LAI resulted in 4 mangrove canopy density classes consisted of rare canopy (688.80 ha ; 0.73%), moderately dense canopy (1,139.55 ha ; 1.2%), dense canopy (35,003.46 ha ; 37%), and very dense canopy (57,790.20 ha ; 61.07%). Taman Nasional Berbak Sembilang wilayah Sumatera Selatan (TNBS Sumsel) merupakan kawasan ekosistem mangrove terluas di wilayah Indonesia bagian barat. Pemantauan kerapatan kanopi vegetasi mangrove di TNBS Sumsel dilakukan menggunakan data Citra Landsat-8 berdasarkan nilai NDVI (Normalized Difference Vegetation Index) yang diintegrasikan dengan data LAI (Leaf Area Index) mangrove di lapangan. Penelitian ini bertujuan untuk menganalisis tutupan vegetasi mangrove dan memetakan sebaran kerapatan kanopi mangrove dengan integrasi data penginderaan jauh dan LAI. Penelitian ini menggunakan metode pengolahan data survei lapangan dan hasil pengolahan citra satelit. Nilai koefisien determinasi dan korelasi antara nilai NDVI dengan nilai LAI tutupan Kanopi di Lapangan dikategorikan tinggi (R2 = 0,69 ; r = 83,07). Hasil penelitian menunjukkan tutupan mangrove secara keseluruhan seluas 94.622,05 ha. Peta integrasi citra NDVI dengan LAI mangrove di lapangan menghasilkan 4 kelas kerapatan kanopi mangrove yakni kanopi jarang seluas 688,80 ha (0,73%), kanopi sedang seluas 1.139,55 ha (1,2%), kanopi lebat seluas 35.003,46 ha (37%), dan kanopi sangat lebat seluas 57.790,20 ha (61,07%).

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The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images

International Journal of Remote Sensing ◽

10.1080/01431161.2018.1455244 ◽

2018 ◽

Vol 39 (15-16) ◽

pp. 5415-5431 ◽

Cited By ~ 12

Author(s):

Anita Simic Milas ◽

Matthew Romanko ◽

Patrick Reil ◽

Tharindu Abeysinghe ◽

Anuruddha Marambe

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Chlorophyll Content ◽

Area Index ◽

Uav Images

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Effects of Topography on Planted Trees in a Headwater Catchment on the Chinese Loess Plateau

Forests ◽

10.3390/f12060792 ◽

2021 ◽

Vol 12 (6) ◽

pp. 792

Author(s):

Da Luo ◽

Zhao Jin ◽

Yunlong Yu ◽

Yiping Chen

Keyword(s):

High Resolution ◽

Leaf Area Index ◽

Leaf Area ◽

Transition Zone ◽

Chinese Loess Plateau ◽

Area Index ◽

Chinese Loess ◽

Canopy Coverage ◽

Headwater Catchment ◽

The Chinese Loess Plateau

The Chinese Loess Plateau (CLP) is known for its complex topography of hills and gullies, and lots of human land-use management activities have been put into practice to sustain the soil, water and other natural resources. Afforestation has been widely applied on the CLP and it’s important to understand the effects of topography on these planted trees. However, the coarse spatial resolution of remote sensing data makes it insensitive to local topography, and the traditional in-situ measurements would consume vast amounts of time and resources. In this study, a small headwater catchment of the CLP was selected to study the effects of topography on the planted trees. Low altitude unmanned aerial vehicle based light detection and ranging (UAV-based LiDAR) technology was utilized to obtain high-resolution topography and vegetation structure data. Results showed that the middle transition zone (mid-transition, slope > 45°) was an important boundary of topography in the gully area of the CLP. In the forested catchment, the area of the mid-transition zone had the lowest of tree density, canopy coverage and leaf area index due to steep slope gradient. The tall trees ten to twenty meters high were concentrated in the downhill area, which had the highest canopy coverage and leaf area index. Elevation had significant linear relationships with canopy coverage and leaf area index (p < 0.001), which revealed the impact of topography on the forest indexes of the afforestation catchment. We concluded that the high-resolution LiDAR technology facilitated the research of topography and forest interactions in land surface.

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