Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs

Coy, André; Rankine, Dale; Taylor, Michael A.; Nielsen, D. G.; Cohen, Jane E.

doi:10.3390/rs8070474

Cited by 39 publications

(37 citation statements)

References 40 publications

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“…In overgrowth, all pixels corresponding to leaves, stems, and branches were classified as cover, and the rest of the pixels were classified as sky. As mentioned in [11], visual classification is considered as the real values of cover in the image and those are compared with the automated threshold proposed in this research.…”

Section: Accuracy In Cover Classificationmentioning

confidence: 99%

“…The plots represented different land uses. The accuracy of the implemented classifier (AC) was evaluated using the following formula [11].…”

Section: Accuracy In Cover Classificationmentioning

confidence: 99%

“…The automation of vegetative cover classification is a process that avoids the error of the human component and ensures the possibility of reproducing the results [11]. The efforts to automate image classification have focused on programs such as MATLAB [12,15,39], WinScanopy [40], or Photoshop [41], among others.…”

Section: Automated Classification Of Imagesmentioning

confidence: 99%

“…There are many methods to extract the vegetation cover fraction [11,12,43] so the degree of accuracy is an important factor in the efficiency of field measurements. For example, supervised classification has a high accuracy but low efficiency, while unsupervised classification has a high efficiency but low precision due to commission and omission errors [1].…”

Section: Understory Covermentioning

confidence: 99%

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Estimation of Vegetation Cover Using Digital Photography in a Regional Survey of Central Mexico

Aguilar¹,

Sánchez-Sánchez

Rojas-García

et al. 2017

Forests

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Abstract:The methods for measuring vegetation cover in Mexican forest surveys are subjective and imprecise. The objectives of this research were to compare the sampling designs used to measure the vegetation cover and estimate the over and understory cover in different land uses, using digital photography. The study was carried out in 754 circular sampling sites in central Mexico. Four spatial sampling designs were evaluated in three spatial distribution patterns of the trees. The sampling designs with photographic captures in diagonal form had lower values of mean absolute error (MAE < 0.12) and less variation in random and grouped patterns. The Carbon and Biomass Sampling Plot (CBSP) design was chosen due to its smaller error in the different spatial tree patterns. The image processing was performed using threshold segmentation techniques and was automated through an application developed in the Python language. The two proposed methods to estimate vegetation cover through digital photographs were robust and replicable in all sampling plots with different land uses and different illumination conditions. The automation of the process avoided human estimation errors and ensured the reproducibility of the results. This method is working for regional surveys and could be used in national surveys due to its functionality.

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Section: Accuracy In Cover Classificationmentioning

confidence: 99%

“…The plots represented different land uses. The accuracy of the implemented classifier (AC) was evaluated using the following formula [11].…”

Section: Accuracy In Cover Classificationmentioning

confidence: 99%

Section: Automated Classification Of Imagesmentioning

confidence: 99%

Section: Understory Covermentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Vegetation Cover Using Digital Photography in a Regional Survey of Central Mexico

Aguilar¹,

Sánchez-Sánchez

Rojas-García

et al. 2017

Forests

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show abstract

“…However, the assumption of LSU methods for decomposition of endmembers in mixed pixels is often not true because of multiple scattering from neighboring objects and interactions among the endmembers [19,20]. Moreover, decomposition of endmembers in mixed pixels is complex and depends on many factors, including landscape complexity, spatial resolution of images, purity of endmembers, or training samples selected and relationship of PVC with spectral variables derived from images [5,6,9,11,17,19,20]. Therefore, LSU methods do not work well in many cases.…”

mentioning

confidence: 99%

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

et al. 2019

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China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.

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Segmentation of vegetation and microplots in aerial agriculture images: A survey

Mardanisamani

Eramian

2022

The Plant Phenome Journal

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Because of the increasing global population, changing climate, and consumer demands for safe, environmentally friendly, and high‐quality food, plant breeders strive for higher yield cultivars by monitoring specific plant phenotypes. Developing new crop cultivars and monitoring through current methods is time‐consuming, sometimes subjective, and based on subsampling of microplots. High‐throughput phenotyping using unmanned aerial vehicle‐acquired aerial orthomosaic images of breeding trials improves and simplifies this labor‐intensive process. To perform per‐microplot phenotype analysis from such imagery, it is necessary to identify and localize individual microplots in the orthomosaics. This paper reviews the key concepts of recent studies and possible future developments regarding vegetation segmentation and microplot segmentation. The studies are presented in two main categories: (a) general vegetation segmentation using vegetation‐index‐based thresholding, learning‐based, and deep‐learning‐based methods; and (b) microplot segmentation based on machine learning and image processing methods. In this study, we performed a literature review to extract the algorithms that have been developed in vegetation and microplots segmentation studies. Based on our search criteria, we retrieved 92 relevant studies from five electronic databases. We investigated these selected studies carefully, summarized the methods, and provided some suggestions for future research.

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Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs

Cited by 39 publications

References 40 publications

Estimation of Vegetation Cover Using Digital Photography in a Regional Survey of Central Mexico

Estimation of Vegetation Cover Using Digital Photography in a Regional Survey of Central Mexico

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

Segmentation of vegetation and microplots in aerial agriculture images: A survey

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