Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations (1988–2016)

Zhu, Fangyan; Wang, Heng; Li, Mingshi; Diao, Jiaojiao; Shen, Wenjuan; Zhang, Yali; Wu, Hongji

doi:10.1007/s11707-020-0820-6

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…Additionally, certain combinations of vegetation indices boost some crops' spectral traits while inhibiting others [16]. The wide use of RS imagery for monitoring land and environmental changes was proofed for soil sealing [17][18][19], human or natural factors that cause the loss of forests [20][21][22], effects of global warming [23,24], a wildfire's damage [25,26], and additional humanmade and natural dynamics. Particularly for SD investigations, which are frequently thought of as being over a lengthy period of time, data acquisition costs and time of coverage are the main limits of observation data such as SPOT [27,28] and Sentinel-2 [29,30].…”

Section: Introductionmentioning

confidence: 99%

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

AbdelRahman

Metwalli

Gao

et al. 2023

Land

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In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation’s indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis.

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Section: Introductionmentioning

confidence: 99%

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

AbdelRahman

Metwalli

Gao

et al. 2023

Land

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“…Remote sensing and satellite imagery have been widely utilized for environmental studies, including urban expansion (Dou et al, 2017;Chai et al, 2018;Zhang et al, 2020;Oñate-Valdivieso, et al, 2022), deforestation (Souza et al, 2013;Hamunyela et al, 2016;Schultz et al, 2016), climate change impacts (Mo et al, 2019;Zhu et al, 2020), wildfire damage (Meddens et al, 2016;Hislop et al, 2018) and other natural and anthropogenic dynamics.…”

Section: Introductionmentioning

confidence: 99%

Soil degradation in andean watersheds: a case study using remote sensing

Oñate-Valdivieso,

Oñate-Paladines,

Díaz

2024

Front. Earth Sci.

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Soil degradation is one of the greatest challenges faced by arid and semi-arid zones, as it causes a progressive loss of the capacity of soils to sustain life and human activities. In the present study, five spectral indices, individually associated with erosion, desertification, salinity, and soil sealing, were evaluated in a semi-arid region of the Ecuadorian Andes to study of soil degradation. The results were validated through field observations and samples processed in the laboratory. The spectral indices presented correlation coefficients between 0.5278 and 0.8809 with field observations. These indices provided useful information to understand the current state of the soils and their potential deterioration. The study area showed greater vulnerability to erosion caused by the combination of scarce vegetation, irregular topography, and significant seasonal rainfall. The advance of urbanization, mechanized agriculture, and overgrazing had a high impact by impermeabilizing considerable surfaces, while the advance of desertification and soil salinization showed little incidence in the study area. The systematic application of these indices would become a valuable means for monitoring soil degradation in Andean watersheds.

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“…Nature reserve area mainly includes the core zone and buffer zone. in the nature reserve protection (Griffiths et al, 2014;Pericak et al, 2018;Zhu et al, 2020;Hua et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Temporal-Based Forest Disturbance Monitoring Analysis: A Case Study of Nature Reserves of Hainan Island of China From 1987 to 2020

Xiao

Zhang²,

Yan³

et al. 2022

Front. Environ. Sci.

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Forest disturbance monitoring can provide scientific data for the decision making and management of nature reserves. LandTrendr algorithm has been applied to identify forest disturbances on a long-time scale through appropriate segmentation and linear fitting. In this study, 23 nature reserves were detected using LandTrendr during 1987–2020, and the vegetation loss was quantified by years and pixel numbers. The results illustrated that (1) most disturbances occurred in the 1990s and early 21st century. (2) From the spatial distribution of forest loss, the area of forest vegetation disturbance in the coastal zone was larger than the protected area in the internal Hainan Island, the area disturbed in the coastal zone protected area was 97.12 km2, and the area disturbed in the internal area of Hainan Island protected area was 63.02 km2. (3) In terms of different levels of nature reserves, the disturbed area of national nature reserves was 28.39 km2 and the total disturbed area of provincial nature reserves was 131.75 km2. (4) In terms of different types of nature reserves, forest ecological nature reserves had the largest disturbed area of 102.96 km2, followed by marine coastal nature reserves with a disturbed area of 36.99 km2, wildlife nature reserves with a disturbed area of 10.22 km2, and wild plant nature reserves with the smallest disturbed area of 9.96 km2. The results are hoped to provide scientific support and data for the management and planning of nature reserves in Hainan Island.

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Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations (1988–2016)

Cited by 12 publications

References 43 publications

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

Soil degradation in andean watersheds: a case study using remote sensing

The Temporal-Based Forest Disturbance Monitoring Analysis: A Case Study of Nature Reserves of Hainan Island of China From 1987 to 2020

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