Review of Remote Sensing Applications in Grassland Monitoring

Remote Sensing, Год журнала: 2022, Номер 14(12), С. 2903 - 2903

Опубликована: Июнь 17, 2022

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, the overall advantage convenience, efficiency, cost effectiveness, especially over large areas. This paper provides a comprehensive review latest estimation methods some critical parameters, including above-ground biomass, primary productivity, fractional vegetation cover, leaf area index. Then, applications are also reviewed from perspective their use these parameters other data. In detail, degradation evaluated. addition, disaster carbon cycle included. Overall, most studies have used empirical models statistical regression models, while number machine learning approaches an increasing trend. specialized methods, such as light efficiency productivity mixed pixel decomposition coverage, widely improved. However, all above certain limitations. For future work, it is recommended that should adopt advanced rather than simple models. particular, potential deep processing high-dimensional data fitting non-linear relationships be further explored. Meanwhile, important to explore new indices based on spectral characteristics specific under study. Finally, fusion multi-source images considered address deficiencies information resolution acquired by single sensor or satellite.

Язык: Английский

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A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion

The Science of The Total Environment, Год журнала: 2021, Номер 804, С. 150187 - 150187

Опубликована: Сен. 8, 2021

Язык: Английский

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Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data

Geoderma, Год журнала: 2022, Номер 411, С. 115695 - 115695

Опубликована: Янв. 10, 2022

Язык: Английский

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Synergetic use of multi-temporal Sentinel-1, Sentinel-2, NDVI, and topographic factors for estimating soil organic carbon

CATENA, Год журнала: 2022, Номер 212, С. 106077 - 106077

Опубликована: Фев. 9, 2022

Язык: Английский

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Evaluation of the Carbon Sink Capacity of the Proposed Kunlun Mountain National Park

International Journal of Environmental Research and Public Health, Год журнала: 2022, Номер 19(16), С. 9887 - 9887

Опубликована: Авг. 11, 2022

National parks, as an important type of nature protected areas, are the cornerstone that can effectively maintain biodiversity and mitigate global climate change. At present, China is making every effort to build a nature-protection system, with national parks main body, this approach considers China′s urgent goals obtaining carbon neutrality mitigating It great significance carbon-neutralization strategy accurately predict sink capacity park ecosystems under background To evaluate dynamics change different management measures, we combined remote-sensing observations, model simulations scenario analyses simulate in proposed Kunlun Mountain Park ecosystem over past two decades (2000–2020) zoning controls various scenarios from 2020 2060. Our results show area increasing. Simultaneously, will be improved implementation control measures; which increased by 2.04% 2.13% 2060 research multiple scenarios. The provide scientific basis for establishment final boundary determination Park.

Язык: Английский

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Modelling soil organic carbon stock distribution across different land-uses in South Africa: A remote sensing and deep learning approach

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2022, Номер 188, С. 351 - 362

Опубликована: Май 4, 2022

Язык: Английский

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Space-time mapping of soil organic carbon stock and its local drivers: Potential for use in carbon accounting

Geoderma, Год журнала: 2024, Номер 441, С. 116771 - 116771

Опубликована: Янв. 1, 2024

The measurement and tracking of changes in soil organic carbon (SOC) stocks its underpinning drivers are important greenhouse gas accounting trading schemes. While repeated direct a location over time is the 'gold standard' for SOC stock change estimation, this currently unfeasible at scale due to sample collection analysis costs. Here, we used data-driven, space–time digital mapping modelling approach (ST-DSM) coupled with an interpretive machine learning (IML) technique both predict quantify relative contributions covariates annual their uncertainty 0–30 cm depth 90 m spatial resolution Australia between 1990 2018. A quantile regression forest model was calibrated using data from datacube comprising values collected prediction years suite dynamic static environmental covariates. evaluation ST-DSM on test set satisfactory. Long-term mean (Mg C/ha), total (Pg C) topsoil were 37.6 (12.3 – 101.1, % PI) 28.3 (9.25 83.1, respectively. rate Australian continent 0.11 (±0.22) Mg C/ha yr−1 net 23.5 Tg C yr−1, indicating Australia's soils sink 29-year period. However, declined montane ecoregion, great concern given fragility high stock. long-term fraction photosynthetic vegetation most individual local contributor many parts Australia. aggregated effect based forming factors indicates as principal driver across continent, followed by climate. our results against independent, temporally paired field estimates nationally accepted process-based yielded promising results. Our demonstrates use readily available inexpensive estimate changes, well underlying larger area but suitable decision making. This presents new monitoring verification possibility projects national especially growing number existing profile observations databases.

Язык: Английский

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Mapping the distribution, trends, and drivers of soil organic carbon in China from 1982 to 2019

Geoderma, Год журнала: 2022, Номер 429, С. 116232 - 116232

Опубликована: Ноя. 2, 2022

The evaluation of soil organic carbon (SOC) dynamics and its driving factors is important for developing sustainable C sequestration practices. A key challenge the lack a time series spatially explicit SOC estimates; most previous estimates are static. In this study, we applied digital mapping framework to model relationship between environmental based on combined dataset 4695 samples collected in China from early 1980 s during 2000–2014 period. We predicted annual distribution at depth 0–100 cm 1 km resolution 1982 2019. mapped trends change identified hotspots. To obtain pixel-level association factors, used linear function quantify contribution individual predictors change. results show that accuracy prediction acceptable, with mean R2 value 0.58 validation. storage was estimated range 89.03 97.41 Pg showed slight increase (slope = 0.024 C/yr) Specifically, two distinct phases were as follows: (1) 2004, considerable decline occurred, which mainly driven by increased temperature; (2) 2004 2019, slowly, resulting greening sustaining soils under warming conditions. Approximately 17 % all areas have been characterized significant decrease since 1982, predominantly central western China. Environmental controls exhibited large spatial variation, where approximately 69 controlled climate factors. For grasslands, losses northern largely temperature precipitation, while primarily attributed decreased inputs grassland degradation Tibetan Plateau. Policy-driven land use has led an some cropland (e.g., northeast east China). implementation ecological restoration shown positive influence sequestration. This study highlights diverse patterns heterogeneity changes their controls. Given differentiation, assessments formulation regional soil-based mitigation strategies guidance effective interventions geographic location.

Язык: Английский

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Storage, patterns, and environmental controls of soil organic carbon stocks in the permafrost regions of the Northern Hemisphere

The Science of The Total Environment, Год журнала: 2022, Номер 828, С. 154464 - 154464

Опубликована: Март 9, 2022

Язык: Английский

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Applications and challenges of digital soil mapping in Africa

Geoderma, Год журнала: 2024, Номер 449, С. 117007 - 117007

Опубликована: Авг. 26, 2024

The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting units locally and for the continent. In past two decades, there has been growing interest alternatives generating soil through digital (DSM) techniques. There are, however, numerous challenges pertaining implementation DSM Africa, such as unavailability appropriate covariates, age positional error measurements, low sampling density, spatial clustering data used fit validate models. This review aims investigate current state identify specific implementing ways it solved literature. found that nearly half African countries had an existing map covering either local or national area, most studies were performed extent. Soil carbon was common property under study, whereas hydraulic variables seldom reported. Nearly all topsoil up 30 cm calculated validation statistics using datasets but without collecting post-mapping probability sample. Few (i.e., 11%) reported estimate uncertainty. Half mind downstream application (e.g., fertility assessment) generation. further correlated area study density strong negative relationship. About 30% relied on legacy lack sufficient coverage their study. From this review, we highlight some research opportunities suggest improvements methodologies. Future should focus capacity building DSM, new collection, rescue. New initiatives, be initiated led from within continent, could support long-term monitoring updating information systems while ensuring contextualised usability. pairs with better delivery stakeholders generation value-added proposition governmental institutions.

Язык: Английский

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