Mapping Grassland Classes Using Unmanned Aerial Vehicle and MODIS NDVI Data for Temperate Grassland in Inner Mongolia, China

Meng, Baoping; Zhang, Yuzhuo; Yang, Zhigui; Lv, Yunyun; Chen, Jianjun; Li, Meng; Sun, Yi; Zhang, Huifang; Yu, Huilin; Zhang, Jianguo; Lian, Jing; He, Mingzhu; Li, Jinrong; Yu, Hongyan; Chang, Li; Yi, Shuai

doi:10.3390/rs14092094

Cited by 10 publications

(17 citation statements)

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“…The published manuscripts covered a wide range of topics regarding vegetation classification using remote sensing techniques. In 14 research papers, the classifications were performed to identify different types of vegetation [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Different types of crops were analysed most frequently [2][3][4]6,6,9,12].…”

Section: Summary Of Contributionsmentioning

confidence: 99%

An Overview of the Special Issue “Remote Sensing Applications in Vegetation Classification”

2023

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One of the ideas behind vegetation monitoring is the ability to identify different vegetation units, such as species, communities, habitats, or vegetation types. Remote sensing data allow for obtaining such information remotely, which is especially valuable in areas that are difficult to explore (such as mountains or wetlands). At the same time, such techniques allow for limiting field research, which is particularly important in this context. Remote sensing has been utilized for vegetation inventories for many decades, using airborne and spaceborne platforms. Developing newer tools, algorithms and sensors is conducive to more new applications in the vegetation identification field. The Special Issue “Remote Sensing Applications in Vegetation Classification” is an overview of the applications of remote sensing data with different resolutions for the identification of vegetation at different levels of detail. In 14 research papers, the most frequent different types of crops were analysed. In three cases, the authors recognised different types of grasslands, whereas trees were the object of the studies in two papers. The most commonly used sensors were Copernicus Sentinel-1 and Sentinel-2; however, to a lesser extent, MODIS, airborne hyperspectral and multispectral data, as well as LiDAR products, were also utilised. There were articles that tested and compared different combinations of datasets, different terms of data acquisition, or different classifiers in order to achieve the highest classification accuracy. These accuracies were assessed quite satisfactorily in each publication; the overall accuracy (OA) for the best result varied from 72% to 98%. In all of the research papers, at least one of the two commonly used machine learning algorithms, random forest (RF) and support vector machines (SVM), was applied. Additionally, one paper presented software ARTMO’s machine-learning classification algorithms toolbox, which allows for the testing of 13 different classifiers. The studies published in this Special Issue can be used by the vegetation research teams and practitioners to conduct deeper analysis via the utilization of the proposed solutions.

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Section: Summary Of Contributionsmentioning

confidence: 99%

An Overview of the Special Issue “Remote Sensing Applications in Vegetation Classification”

2023

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“…Identifying the grassland classes are crucial for managing and utilizing grassland resources and for reconstructing and restoring the grassland ecological environment (Meng et al, 2022). To further understand the vegetation distribution across a tropical (Camargo et al, 2018).…”

Section: Grassland Classification and Their Relationship With Vegetat...mentioning

confidence: 99%

“…In the context of climate change and rapid transformation of grassland environment due to anthropogenic activities, mainly during the lasted four decades (Fernandes et al, 2020;Buisson et al, 2022), the grassland classification is crucial for its management. The identification of grassland classes provides the basis for the protection of grassland resources and for the reconstruction and restoration of a grassland ecological environment (Meng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Conventional platforms are characterized by lack of spatial detail to solve and classify fine landscape features (individual trees and shrubs), requiring large amounts of cost and resources (Wang et al, 2019a;Meng et al, 2022). In this regard, hyperspatial tools, such as UAS imagery, have been successfully applied in many fine-scale classification studies, allowing the identification of subtle variations in species composition, and capturing detailed vegetation and landscape features, for instance, shadows, stems, and canopy gaps (Laliberte and Rango, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Medeiros

Morellato²,

Silva³

2023

Front. Environ. Sci.

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Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland.

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“…Due to frequent variations in vegetation structure and surface roughness, high-level observations are required [3]. The Convolution neural network approach is used for vegetation mapping through image recognition in buildings [4]. The author utilized the MODIS NDVI dataset in the interior location of Mongolia and China to analyze the grassland using Unmanned Aerial Vehicles (UAV).…”

Section: Introductionmentioning

confidence: 99%

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Vijayalakshmi¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

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Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation area. The novel Deep Meta fusion model (DMFM) is created with a unique fusion framework of residual stacking of convolution layers with Unique covariate features (UCF), Intensity features (IF), and Colour features (CF). The overhead issues in GPU utilization during Convolution neural network (CNN) models are reduced here with a lightweight architecture. The system considers detailing feature areas to improve classification accuracy and reduce processing time. The proposed DMFM model achieved 99% accuracy, with a maximum processing time of 130 s. The training, testing, and validation losses are degraded to a significant level that shows the performance quality with the DMFM model. The system acts as a standard analysis platform for dynamic datasets since all three different features, such as Unique covariate features (UCF), Intensity features (IF), and Colour features (CF), are considered very well.

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Mapping Grassland Classes Using Unmanned Aerial Vehicle and MODIS NDVI Data for Temperate Grassland in Inner Mongolia, China

Cited by 10 publications

References 50 publications

An Overview of the Special Issue “Remote Sensing Applications in Vegetation Classification”

An Overview of the Special Issue “Remote Sensing Applications in Vegetation Classification”

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

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