Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

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

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102373 ◽

2021 ◽

Vol 102 ◽

pp. 102373

Author(s):

Bo Sun ◽

Chufeng Wang ◽

Chenghai Yang ◽

Baodong Xu ◽

Guangsheng Zhou ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Canopy Coverage ◽

Uav Images

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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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Small Scale Rainfall Partitioning in a European Beech Forest Ecosystem Reveals Heterogeneity of Leaf Area Index and Its Connectivity to Hydro-and Atmosphere

Geosciences ◽

10.3390/geosciences9090393 ◽

2019 ◽

Vol 9 (9) ◽

pp. 393 ◽

Cited By ~ 3

Author(s):

Nico Frischbier ◽

Katharina Tiebel ◽

Alexander Tischer ◽

Sven Wagner

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Forest Ecosystem ◽

Spatial Scales ◽

European Beech ◽

Beech Forest ◽

Small Scale ◽

Litter Production ◽

Area Index ◽

Average Value

(1) Background: Leaf area index (LAI) is an essential structural property of plant canopies and is functionally related to fluxes of energy, water, carbon, and light in ecosystems; coupling the biosphere to the geo-, hydro-, and atmosphere. There is an increasing need for more accurate and traceable measurements among several spatial scales of investigation and modelling. We hypothesize that the spatial variability of LAI at the scale of crown sections of a single European beech (Fagus sylvatica L.) tree in a highly structured, mixed European beech-Norway spruce stand can be determined by simultaneous records of precipitation; (2) Methods: Spatially explicit measurements of throughfall were conducted repeatedly below beech and in forest gaps for rain events in leafed and in leafless periods. Subsequent analysis with a new regression approach resulted in estimating leaf and twig water storage capacities (SCleaf/twig) at point level independent of within-crown lateral flow mechanisms. Inverse modelling was used to estimate spatial litterfall (n = 99) distribution and litter production (mass, area, numbers) for single trees, as a function of diameter at breast height; (3) Results: As revealed by a linear mixed-effects model, SCleaf at the center of a beech canopies amounts to 4.9 mm in average and significantly decreases in the direction of the crown edges to an average value of 1.1 mm. Based on diameter-sensitive prediction of litter production, specific leaf area wetting capacity amounts to 0.260 l·m−2. A linear within-canopy dynamic of LAI was found with a mean of 17.6 m2·m−2 in the center and 4.0 m2·m−2 at the edges; and (4) Conclusions: The application of the method provided plausible results and can be extended to further throughfall datasets and tree species. Unravelling the causes and magnitude of spatial- and temporal heterogeneity of forest ecosystem properties contribute to overall progress in geosciences by improving the understanding how the biosphere relates to the hydro- and atmosphere.

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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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