Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

Fang, Wenjing; Zhu, Hongfen; Li, Shuai; Ding, Haoxi; Bi, Rutian

doi:10.3390/s23020659

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…To determine the input data, the NSP index was proposed to maximize the differentiation between daylilies and corn. The use of this single index performed well with the RF classifier, which aligns with findings from previous studies [38,39]. When comparing different input data, Sentinel-1 and Sentinel-2 were found to complement each other effectively, leading to improved classification accuracy, and this was also supported by previous research [10].…”

Section: Advantages and Limitations Of Classification And Identificationsupporting

confidence: 88%

Spatial Distribution and Influencing Factors of Daylily Cultivation in the Farming–Pastoral Ecotone of Northern China

Peng,

Li,

et al. 2024

Land

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The farming–pastoral ecotone in northern China is an ecologically vulnerable area with low-quality arable land, and cash crops are an important economic source for local farmers. Although local governments have introduced supportive policies, there are still several factors that hinder the implementation of the policies: there is a lack of sufficient research on the distribution of specialty crops, and the driving factors for agricultural planting structure adjustment are not yet clear. In this study, the specialty cash crop of the daylily planting industry in Yunzhou District, in the Farming–Pastoral Ecotone in northern China, was selected as the research object. Field surveys were conducted to collect sample points and village-level survey data, which were further combined with Sentinel-1 and Sentinel-2 data, and vegetation indices. Support vector machine (SVM) and random forest (RF) classifiers were utilized to identify daylilies and compare the accuracy using different combinations of input data. Furthermore, the classification results were counted by village, and spatial autocorrelation was used to analyze the spatial distribution pattern of daylilies. Finally, in conjunction with the village-level survey data, Spearman correlation analysis, multiple regression trees (MRT), and random forests were employed to explore the driving factors of daylily cultivation. The results indicate that using an RF classification tree of 300 resulted in the optimal method, as it achieved the highest accuracy for crop classification. The overall accuracy and daylily classification accuracy were 94.6% and 94.75%, respectively. Daylily distributions were mainly concentrated near the Sanggan River, urban areas, and the tourism industry. The distribution area of daylilies in each village was concentrated in 13.4–38.8 hm2. Spatial clustering showed more aggregation of low–low and high–high types. Labor force and daylily yield were identified as the most significant influencing factors. Further analysis of the different regions revealed the importance of industry support policies and technical training. This study provides data to support the distribution of specialty crops in Yunzhou District and a technical basis for adjusting agricultural planting structures.

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Section: Advantages and Limitations Of Classification And Identificationsupporting

confidence: 88%

Spatial Distribution and Influencing Factors of Daylily Cultivation in the Farming–Pastoral Ecotone of Northern China

Peng,

Li,

et al. 2024

Land

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“…At the same time the NIR wave beam is reflected very well by green vegetation. This difference in the ability to absorb light rays makes it possible to conduct research on the health and greenness of vegetation [14][15][16].…”

Section: Ndvimentioning

confidence: 99%

Analysis of impact of the 2021 eruption of the Semeru Volcano in Lumajang Regency, East Java Province, Indonesia

Permatasari,

Wiguna,

Kurnia

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Indonesia is a country located on the Pacific Ring of Fire which causes Indonesia to have the largest number of active volcanoes in the world, resulting in frequent volcanic eruptions. As happened in 2021, the eruption of Semeru Volcano caused casualties and damage to landuse. This study aims to determine the impact of the 2021 Semeru Volcano Eruption and to map the impact of Semeru Volcano erupton to landuse in Lumajang Regency using Normalized Difference Vegetation Index (NDVI). This research includes three stages. In the first stage, data collection was carried out by downloading pre-eruption and post-eruption Landsat 8 OLI satellite images of Semeru Volcano. Second, analyze data, reports and documents to obtain data and information regarding the impact of the Semeru Volcano eruption. The third phase focuses on data analysis and mapping of impact caused by the eruption. The most affected area with lahar flood were bareland with 2330 ha or 45.56% of affected area, followed by forest with 1921 ha (37.56%), dryland agriculture with 713 ha (13.94%) and rice fields with 134 ha (2.62%). The whole area that affected by Semeru Volcano eruption in Lumajang Regency is 5114 ha or 2.8% from total area of Lumajang Regency. Pronojiwo District is the district with the largest area affected by the eruption with total of 20.34% of its area affected by Semeru Volcano eruption.

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“…Spectral indices (SIs) are robust indicators of vegetation conditions that allow for the amplification of the spectral differences among soils, vegetation, etc. [29]. Extracting canopy SIs from UAV hyperspectral images can effectively reduce data multicollinearity.…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

Fang,

Man,

Liu

et al. 2023

Remote Sensing

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The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R2 of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R2 value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora.

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Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

Cited by 3 publications

References 49 publications

Spatial Distribution and Influencing Factors of Daylily Cultivation in the Farming–Pastoral Ecotone of Northern China

Spatial Distribution and Influencing Factors of Daylily Cultivation in the Farming–Pastoral Ecotone of Northern China

Analysis of impact of the 2021 eruption of the Semeru Volcano in Lumajang Regency, East Java Province, Indonesia

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

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