Spatial and Temporal Changes in Vegetation Cover in the Three North Protection Forest Project Area Supported by GEE Cloud Platform

Li, Xusheng; Hai, Quansheng; Zhu, Zhenchang; Zhang, Donghui; Shao, Yakui; Zhao, Yingjun; Li, Hu; Vandansambuu, Battsengel; Xiao-li, Ning; Chen, Donghua; Liu, Yufeng

doi:10.3390/f14020295

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…The correlation coefficient is a special covariance after standardization, which is calculated by dividing the covariance by the standard deviation. Then, the correlation coefficient between mangrove nutrient elements and LAI is calculated (Table 5) [55]. The covariance range is from positive infinity to negative infinity, and the correlation coefficient can only change between ±1 [49].…”

Section: Distribution Patterns Of Nutrient Content In Mangrove Along ...mentioning

confidence: 99%

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

Chen¹,

Yang

Zhang

et al. 2023

Forests

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Mangrove is the key vegetation in the transitional zone between land and sea, and its health assessment can indicate the deep-level ecological information. The LAI and six key nutrients of mangrove were selected as quantitative evaluation indicators, and the decisive evaluation method of mangrove growth was expected. The mangrove reserve of Dongzhai Port National Nature Reserve in Hainan Province, China, was selected as the study area, with an area of 17.71 km2. The study area was divided into adjacent urban areas, aquaculture areas, and agricultural production areas, and key indicators are extracted from satellite hyperspectral data. The extraction process includes spectral data preprocessing, spectral transformation, spectral combination, spectral modeling, and precision inspection. The spatial distribution of LAI and six key nutrient components of mangrove in the study area were obtained. LAI and Chla need to calculate the index after high-order differentiation of the spectrum; MSTR and Chlb need to calculate the envelope after the second-order differential of the spectrum; TN and TP are directly changed by original or exponential spectrum; the spectral transformation method adopted by TK was homogenization after first-order differential. The results of the strength of nutrient content along the three regions show that there was no significant difference in the retrieval index of mangroves in the three regions, and the overall health level of mangroves was consistent. Chla was the key identification component of mangrove growth and health. The contents of nutrient elements with correlation coefficient exceeding 0.80 include MSTR and TK (0.98), Chla and TP (0.96), Chla and TK (0.87), MSTR and Chla (0.86), MSTR and TK (0.83), and MSTR and TP (0.81). The study quantifies the relationship between different LAI and nutrient content of mangrove leaves from the perspectives of water, leaf biology, and chemical elements, which improved our understanding of the relationship between key components during mangrove growth for the first time.

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Section: Distribution Patterns Of Nutrient Content In Mangrove Along ...mentioning

confidence: 99%

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

Chen¹,

Yang

Zhang

et al. 2023

Forests

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“…Globally, forest ecosystems that are rich in biodiversity face the risk of land use change due to forest destruction from anthropogenic activities. Damage to protected forests due to anthropogenic activities affects the composition of forest vegetation structures [21]. This section may be divided into subheadings.…”

Section: Anthropogenic Activitymentioning

confidence: 99%

Anthropogenic Damage, Land Cover Change and Mitigation of Mount Hamiding Protected Forest Area in North Halmahera District, North Maluku Province-Wallace Zone, Indonesia

Kailola¹,

Purwanto²,

Sumardi³

et al. 2023

Preprint

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Mount Hamiding Protected Forest is located in North Halmahera Regency, North Maluku Province, the Wallace zone has an important role in providing protection functions, life support systems and regulating water management, preventing floods, preventing seawater intrusion, and controlling soil fertility. The protected forest area in the Wallace Zone is globally renowned for its endemic characteristics of flora and fauna. The condition of the protected forest of Mount Hamiding HMPF is currently experiencing anthropogenic damage as a result of forest encroachment, illegal logging, shifting cultivation, grazing and poaching to meet social needs. Anthropogenic damage has been in the spotlight for decades and has become a global issue. This study aims to determine damage to protected forests, changes in land cover, and mitigation of protected forest areas. This research uses quantitative and descriptive qualitative methods. Determination of research locations by purposive sampling. Parameters measured in this study were damage to protected forests, changes in land cover and area mitigation strategies. To identify forest damage, snowball sampling was carried out, and land cover changes through land cover data processing were obtained from the Ministry of Environment and Forestry of the Republic of Indonesia. Then using Imagery 7 ETM+ and 8 OLI to calculate changes in forested land to non-forested from 2006 -2021, analysis of land cover change using supervised classification, developing a strategy based on identity verification of forest damage and analysis of land cover change. The results showed that damage to protected forests was caused by anthropogenic disturbances in the form of forest encroachment, shifting cultivation, illegal logging, grazing and hunting of wild animals. Changes in land cover experienced the greatest damage in 2006-2021 amounting to 1,796.54 ha (16.17%). Strategic efforts are carried out using forest engineering, namely the agroforestry system and social engineering through community empowerment, namely training and outreach.

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“…For example, vegetation cover can be seen as the weight assigned to the vegetation component based on its area relative to the total image area. (Kalisa et al, 2019;Li et al, 2023). Its calculation formula is…”

Section: Image Element Dichotomous Modelmentioning

confidence: 99%

Quantifying vegetation change and driving mechanism analysis in Sichuan from 2000 to 2020

Ning,

Peng,

et al. 2023

Front. Environ. Sci.

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Vegetation cover is a crucial indicator of biodiversity and ecological processes, but there are still uncertainties about the factors driving changes in vegetation. In this study, we conducted a comprehensive analysis of vegetation cover changes in Sichuan Province from 2000 to 2020 using Formation Vegetation Cover (FVC) derived from MODIS13Q1 data. Our results revealed a consistent increase in vegetation FVC, rising from 0.506 to 0.624 over the 21-year period, with an annual growth rate of 0.0028. The turning point in this growth occurred in 2006. Of significance, the expansion of vegetation covered a substantial portion, accounting for 84.76%, while the decrease constituted 13%. Elevation proved to be an effective explanatory factor, with a coefficient of 0.417, indicating its role in explaining vegetation cover changes. It is important to note that FVC trends and averages exhibited distinct patterns concerning elevation, land use, population density, topography, and soil type, while their correlation with meteorological factors was relatively weak. Concurrently, the increase in construction and urban development had a negative impact on vegetation cover.

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Spatial and Temporal Changes in Vegetation Cover in the Three North Protection Forest Project Area Supported by GEE Cloud Platform

Cited by 12 publications

References 12 publications

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

Anthropogenic Damage, Land Cover Change and Mitigation of Mount Hamiding Protected Forest Area in North Halmahera District, North Maluku Province-Wallace Zone, Indonesia

Quantifying vegetation change and driving mechanism analysis in Sichuan from 2000 to 2020

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