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1.
Vet Rec ; 190(1): 13, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34994442
2.
Environ Monit Assess ; 194(2): 92, 2022 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-35028760

RESUMO

Monitoring and determining the amount of water in reservoirs is of great importance in terms of water planning and management. This study proposes a geographic information system (GIS)-based methodology to estimate the water volume changes in water reservoirs. Two specific methods are proposed using Australian National University's Digital Elevation Model (ANUDEM) raster surface and Triangulated Irregular Network (TIN) surface models, both utilizing normalized difference water index (NDWI) of Sentinel 2A satellite images for water-covered area and coastline and digital elevation model (DEM) for 3D modelling of the reservoir. The most crucial part of this study is the comprehensive evaluation of the model findings considering hydrological, meteorological and anthropogenic factors, simultaneously. Application of the proposed methods is provided for the analysis of the multi-temporal water volume changes of Bayramiç Dam Lake (Çanakkale, Turkey) in two hydrological periods covering the 2015-2016 and 2016-2017 water years. The results indicate that the TINS model produced water volume values much closer to the in situ Turkish General Directorate of State Hydraulic Works (DSI) values than the ANUDEM model. The performance of these methods was also assessed by the temporal dynamics of surface hydrological processes. Regarding the water storage dynamics, hydro-meteorological factors influence the water input, while anthropogenic factors strongly influence the water output. Water consumption for irrigation and electricity generation was found to be the most important water budget components of the total water consumption.


Assuntos
Lagos , Tecnologia de Sensoriamento Remoto , Austrália , Monitoramento Ambiental , Humanos , Água
3.
J Environ Manage ; 302(Pt B): 114067, 2022 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-34781051

RESUMO

Worldwide mining activities are one of the major anthropogenic activities that have caused high forest cover loss (FCL). In this study, we have quantified FCL in Odisha State due to mining activities analyzing Hansen Global Forest Change (HGFC) time series data for the period of 2001-2019 in Google Earth Engine platform. Our analysis suggests that Nabarangpur, Puri, Kendrapara, and Kalahandi districts lost more than 20% of their forest cover during this period. Rayagada and Koraput were the top two districts that recorded the highest FCL with mean change rates of 13.81 km2/year and 7.17 km2/year, respectively. The results point out that mining operations have grown in recent years in Odisha State, and the increase in these activities has contributed to the increase in FCL. This study offers a cost-effective methodology to monitor FCL in mining areas which will eventually contribute to the protection of forest biodiversity and forest dwelling tribal population.


Assuntos
Conservação dos Recursos Naturais , Tecnologia de Sensoriamento Remoto , Monitoramento Ambiental , Florestas , Índia
4.
IEEE Trans Image Process ; 31: 99-109, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34793302

RESUMO

Remote sensing scene classification (RSSC) is a hotspot and play very important role in the field of remote sensing image interpretation in recent years. With the recent development of the convolutional neural networks, a significant breakthrough has been made in the classification of remote sensing scenes. Many objects form complex and diverse scenes through spatial combination and association, which makes it difficult to classify remote sensing image scenes. The problem of insufficient differentiation of feature representations extracted by Convolutional Neural Networks (CNNs) still exists, which is mainly due to the characteristics of similarity for inter-class images and diversity for intra-class images. In this paper, we propose a remote sensing image scene classification method via Multi-Branch Local Attention Network (MBLANet), where Convolutional Local Attention Module (CLAM) is embedded into all down-sampling blocks and residual blocks of ResNet backbone. CLAM contains two submodules, Convolutional Channel Attention Module (CCAM) and Local Spatial Attention Module (LSAM). The two submodules are placed in parallel to obtain both channel and spatial attentions, which helps to emphasize the main target in the complex background and improve the ability of feature representation. Extensive experiments on three benchmark datasets show that our method is better than state-of-the-art methods.


Assuntos
Algoritmos , Tecnologia de Sensoriamento Remoto , Redes Neurais de Computação
5.
Sci Total Environ ; 806(Pt 1): 150496, 2022 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-34844326

RESUMO

A large number of studies have shown that the existence of wetlands may influence arsenic concentrations in adjacent shallow groundwater. However, little is known about the linkage between wetland evolution and arsenic enrichment in shallow groundwater. This study investigated wetland evolutions from 1973 to 2015 in two arid-semiarid inland basins along the Yellow River catchment (i.e., the Yinchuan Basin and the Hetao Basin) based on remote sensing data, and their association with arsenic distributions based on arsenic concentrations of 244 and 570 shallow groundwater samples, respectively. The long-term Landsat images reveal that the covering area of wetlands exhibited increasing trends in both the Yinchuan Basin and the Hetao Basin. Wetlands in the Yinchuan Basin and the Yellow River water-irrigation area in the Hetao Basin varied with precipitation before 2000, but exhibited increasing trends because of wetland restoration policies since 2000. Wetlands in groundwater-irrigation area in the Hetao Basin decreased due to increasing exploitation of shallow groundwater. Wetlands with long existence time were mainly distributed along the Yellow River and drainage channels and in large lakes in the northern Yinchuan Basin and the Hetao Basin, where high­arsenic (>10 µg/L) groundwater occurred. The probability of high­arsenic groundwater distribution increased with the proportion of wetland existence time to the entire studied period (42 years), which can be best explained by a BiDoseResp growth curve. Longer existence of wetlands may cause greater probability of high­arsenic groundwater. This was likely related to long-term introduction of biodegradable organic matter into shallow aquifers and thereafter enhancement of arsenic mobility and/or arsenic being released beneath wetlands and transported into shallow aquifers under continuing wetland water recharge. We therefore suggest that mapping wetland evolutions could probably serve as a good indicator for predicting high arsenic groundwater distributions in shallow aquifers.


Assuntos
Arsênio , Água Subterrânea , Poluentes Químicos da Água , Arsênio/análise , Monitoramento Ambiental , Lagos , Tecnologia de Sensoriamento Remoto , Poluentes Químicos da Água/análise , Áreas Alagadas
6.
Environ Pollut ; 292(Pt B): 118397, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34688724

RESUMO

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0-20 cm) in a 2574 km2 agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (-0.26 > r > -0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.


Assuntos
Poluentes do Solo , Solo , Agricultura , Brasil , Monitoramento Ambiental , Humanos , Tecnologia de Sensoriamento Remoto , Poluentes do Solo/análise
7.
Sci Total Environ ; 805: 150423, 2022 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-34818810

RESUMO

Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m-3. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.


Assuntos
Cianobactérias , Ecossistema , Algoritmos , Clorofila/análise , Clorofila A , Monitoramento Ambiental , Imageamento Hiperespectral , Lagos , Tecnologia de Sensoriamento Remoto
8.
Opt Lett ; 47(1): 18-21, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34951872

RESUMO

Photoacoustic microscopy (PAM) is a unique tool for biomedical applications because it can visualize optical absorption contrast in vivo. Recently, non-contact PAM based on non-interferometric photoacoustic remote sensing (PARS), termed PARS microscopy, has shown promise for selected imaging applications. A variety of superluminescent diodes (SLDs) have been employed in the PARS microscopy system as the interrogation light source. Here, we investigate the use of a low-cost laser diode (LD) as the interrogation light source in PARS microscopy, termed PARS-LD. A side-by-side comparison of PARS-LD and a PARS microscopy system using an SLD was conducted that showed comparable performance in terms of resolution and signal-to-noise ratio. More importantly, for the first time to our knowledge, in vivo PAM imaging of mouse brain vessels was conducted in a non-contact manner, and the results show that PARS-LD provides great performance.


Assuntos
Microscopia , Técnicas Fotoacústicas , Animais , Lasers Semicondutores , Camundongos , Tecnologia de Sensoriamento Remoto , Análise Espectral
9.
IEEE Trans Image Process ; 31: 678-690, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34914588

RESUMO

Building extraction in VHR RSIs remains a challenging task due to occlusion and boundary ambiguity problems. Although conventional convolutional neural networks (CNNs) based methods are capable of exploiting local texture and context information, they fail to capture the shape patterns of buildings, which is a necessary constraint in the human recognition. To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation. In the proposed ASLNet, we introduce the adversarial learning strategy to explicitly model the shape constraints, as well as a CNN shape regularizer to strengthen the embedding of shape features. To assess the geometric accuracy of building segmentation results, we introduced several object-based quality assessment metrics. Experiments on two open benchmark datasets show that the proposed ASLNet improves both the pixel-based accuracy and the object-based quality measurements by a large margin. The code is available at: https://github.com/ggsDing/ASLNet.


Assuntos
Processamento de Imagem Assistida por Computador , Tecnologia de Sensoriamento Remoto , Humanos , Redes Neurais de Computação
10.
Sci Total Environ ; 802: 149928, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34464806

RESUMO

Climate change in recent decades led to the remarkable expansions for most lakes in endorheic basins of the Tibetan Plateau (TP). Enlarged lake inundation areas may pose adverse effects and potential threats on the local human living environment, especially for high-risk villages adjacent to rapidly expanding lakes. Taking a rapidly expanding lake, Angzi Co in the central TP as a study case, we investigated the flooding risk of lake growth on the local living environment and proposed an optimized solution of village relocation selection on the basis of satellite and unmanned aerial vehicle (UAV) remote sensing. The detection of spatiotemporal variations of Angzi Co using optical and altimetric satellite observations revealed a significant area and water level increase by 81.28 km2 and 5.78 m, respectively, from 2000 to 2020. We also assessed the vertical accuracy of multi-source digital elevation model (DEM) products using Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) altimetry data and further examined the flooding risk and potential influences of lake expansion on adjacent settlements (Guozha Village). Results indicated that UAV-DEM achieves excellent advantages in depicting details of lake shoreline variations and simulating potential submergence regions, followed by Advanced Land Observing Satellite World 3D DEM (AW3D DEM). Moreover, assuming that Angzi Co maintains the water level at a growth rate of 0.29 m/a (the average change rate during 2000-2020), the village will be submerged in approximate 10 years based on our assessment. Furthermore, we designed an optimal relocation site southwest of Guozha Village and approximately 3 km away based on the GIS-MVDA method and field investigations. An initial remote sensing-based approach for assessing the flooding risk from dramatic lake expansions in the TP and optimizing the village relocation site was proposed in this study to provide an essential scientific reference for formulating risk mitigation solutions under future climate change scenarios.


Assuntos
Lagos , Tecnologia de Sensoriamento Remoto , Mudança Climática , Inundações , Humanos , Tibet
11.
Sci Total Environ ; 803: 149805, 2022 Jan 10.
Artigo em Inglês | MEDLINE | ID: mdl-34492494

RESUMO

Accurate, high spatial and temporal resolution water quality monitoring in inland waters is vital for environmental management. However, water quality monitoring in inland waters by satellite remote sensing remains challenging due to low signal-to-noise ratios (SNRs) and instrumental resolution limitations. We propose the concept of proximal remote sensing for monitoring water quality. The proximal hyperspectral imager, developed by Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (CAS) and Hikvision Digital Technology, Ltd., is a high spatial, temporal and spectral resolution (1 nm) sensor for continuous observation, allowing for effective and practical long-term monitoring of inland water quality. In this study, machine learning and empirical algorithms were developed and validated using in situ total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD) concentrations and spectral reflectance from Lake Taihu (N = 171), the Liangxi River (N = 94) and the Fuchunjiang Reservoir (N = 109) covering different water quality. Our dataset includes a large range for three key water quality parameters of TN from 0.93 to 6.46 mg/L, TP from 0.04 to 0.62 mg/L, and COD from 1.32 to 15.41 mg/L. Overall, the back-propagation (BP) neural network model had an accuracy of over 80% for TN (R2 = 0.84, RMSE = 0.33 mg/L, and MRE = 11.4%) and over 90% for TP (R2 = 0.93, RMSE = 0.02 mg/L, and MRE = 12.4%) and COD (R2 = 0.91, RMSE = 0.66 mg/L, and MRE = 9.3%). Our results show that proximal remote sensing combined with machine learning algorithms has great potential for monitoring water quality in inland waters.


Assuntos
Tecnologia de Sensoriamento Remoto , Qualidade da Água , Monitoramento Ambiental , Lagos , Rios
12.
Sci Total Environ ; 807(Pt 1): 150635, 2022 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-34606871

RESUMO

Accurate and timely estimates of groundwater storage changes are critical to the sustainable management of aquifers worldwide, but are hindered by the lack of in-situ groundwater measurements in most regions. Hydrologic remote sensing measurements provide a potential pathway to quantify groundwater storage changes by closing the water balance, but the degree to which remote sensing data can accurately estimate groundwater storage changes is unclear. In this study, we quantified groundwater storage changes in California's Central Valley at two spatial scales for the period 2002 through 2020 using remote sensing data and an ensemble water balance method. To evaluate performance, we compared estimates of groundwater storage changes to three independent estimates: GRACE satellite data, groundwater wells and a groundwater flow model. Results suggest evapotranspiration has the highest uncertainty among water balance components, while precipitation has the lowest. We found that remote sensing-based groundwater storage estimates correlated well with independent estimates; annual trends during droughts fall within 15% of trends calculated using wells and groundwater models within the Central Valley. Remote sensing-based estimates also reliably estimated the long-term trend, seasonality, and rate of groundwater depletion during major drought events. Additionally, our study suggests that the proposed method estimate changes in groundwater at sub-annual latencies, which is not currently possible using other methods. The findings have implications for improving the understanding of aquifer dynamics and can inform regional water managers about the status of groundwater systems during droughts.


Assuntos
Água Subterrânea , Tecnologia de Sensoriamento Remoto , Secas , Hidrologia , Água
13.
Sci Total Environ ; 807(Pt 1): 150772, 2022 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-34619207

RESUMO

The flood storage of lakes and reservoirs plays an important role in flood regulation and control in floodplains. However, the flood storage capacity of lakes and reservoirs is ineffectively quantified at the basin scale due to the limited access to in-situ data and poor quality of optical satellite images in flooding seasons. To address this, taking a typical floodplain basin (the Poyang Lake basin) in the Yangtze as a study case, radar satellite data combined with measured bathymetry and digital elevation model data were utilized to reconstruct the time series of the water inundation area and water storage change of all lakes and reservoirs larger than 1 km2 during the once-in-a-generation flood event that occurred in 2020 (termed as the 2020 flood event hereafter). Results show that the flood storage capacity of Poyang Lake can reach the maximum at 12.18 Gt, and that for other lakes and reservoirs within the basin is approximately 2.95 Gt. It indicates a total flood-storage capacity of 15.13 Gt for the basin-scale lakes and reservoirs, approximately accounting for 45.02% of the terrestrial water storage change of the basin. The storage capacity of Poyang Lake was approximately four times larger than the entirety of other lakes and reservoirs in the basin despite that its maximum water inundation area is in the proportion of 2.58 times other water bodies. This finding indicates that the Poyang Lake provided the dominant contribution to flood storage among all the lakes and reservoirs in the basin. This study introduced a remote sensing approach to quantify the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions during the flood event, which could fill the insufficiently-quantified knowledge about dynamics of lakes and reservoirs in areas lacking full-covered in-situ data records. This study also helps to offer a quantitative basis to improve flood forecasting and control for the public authority, stakeholders, and decision-makers.


Assuntos
Inundações , Lagos , China , Monitoramento Ambiental , Tecnologia de Sensoriamento Remoto , Estações do Ano , Água
14.
Sci Total Environ ; 806(Pt 4): 150807, 2022 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-34626624

RESUMO

The West Coast of Ireland hosts many of the few populations of Freshwater Peal Mussels (FPM) left in Europe. The decline of this keystone species is strongly related to deteriorating hydrological conditions, specifically to the threat of low flows during dry summers. Populations still capable of reproducing require a minimum discharge and flow velocity to support juvenile mussels, or else stress builds up and an entire generation may be lost. Monitoring environmental and hydrological conditions in small and remote FPM catchments is difficult due to the lack of infrastructure. Indices derived from remote sensing imagery can be used to assess hydrological variables at the catchment scale. Here, five indices are tested as possible surrogates for soil moisture and evapotranspiration, based on two relevant land-cover types: open peat habitats (OPH) and forestry. Selected indices are then assessed in their ability to reproduce seasonal patterns and in their response to a severe drought event. The moisture stress index (MSI) and normalized difference vegetation index (NDVI) were found to be the best surrogates for soil moisture and evapotranspiration respectively. Both indices showed seasonality patterns in the two land-cover types, although the variability of MSI was significantly higher. During the 2018 drought, MSI visibly increased only in OPH, while NDVI rose only for forestry. The results suggest that OPH enhances the long-term hydrological resilience of a catchment by conserving water in the peat substrate, while industrial forestry plantations exacerbate the pressure on water during drier periods. This has consequences for river discharge, freshwater biodiversity and specifically for FPM. Implementing these surrogates have the potential to identify land-use management strategies that reduce and even avert the effects of drought on FPM. Such strategies are increasingly necessary in a climate change context, as recurring summer droughts are expected in most of Europe.


Assuntos
Bivalves , Tecnologia de Sensoriamento Remoto , Animais , Secas , Água Doce , Hidrologia
15.
J Environ Manage ; 302(Pt A): 113957, 2022 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-34673457

RESUMO

Coastal wetlands are the most valuable ecosystems on the earth but facing severe degradation and losses owing to climate change and anthropogenic activities. Many ecological engineering projects (EEP) have been conducted to mitigate the degradation of coastal wetlands. However, the geomorphological impacts of EEP on coastal wetlands have not been well documented. In this study, a method employed a process-based hydrodynamic model and remote sensing (RS) was developed to evaluate the impacts of EEP on the geomorphological change of a prototype Ramsar site. Results demonstrated that RS can improve the quality of bathymetry data for the numerical model with a decrease of RMSE of bathymetry data from 0.52 m to 0.3 m. RS data also showed good capacity in trend detection of geomorphological change spatially. Results showed the Chongming Dongtan wetland experienced erosion with an annual rate of -0.035 m/yr from 2013 to 2016 after the implementation of EEP. The deposition rate changed significantly in the area within 200 m of the EEP. It is found that the EEP modified the composition of vegetation, sediment transportation, as well as substrate stability, affecting the geomorphological change of coastal wetlands. The study suggested that the EEP is a direct and effective way to restore the coastal habitats for waterbirds from moderate anthropogenic disturbance. However, the modification of the coastal wetland ecosystem by EEP will potentially increase the vulnerability to global climate change. Therefore, Future studies are needed to further evaluate the advantages and disadvantages of EEP and identify a more sustainable approach for coastal management.


Assuntos
Ecossistema , Áreas Alagadas , Conservação dos Recursos Naturais , Tecnologia de Sensoriamento Remoto
16.
Sci Total Environ ; 806(Pt 3): 151335, 2022 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-34743818

RESUMO

A fundamental challenge in verifying urban CO2 emissions reductions is estimating the biological influence that can confound emission source attribution across heterogeneous and diverse landscapes. Recent work using atmospheric radiocarbon revealed a substantial seasonal influence of the managed urban biosphere on regional carbon budgets in the Los Angeles megacity, but lacked spatially explicit attribution of the diverse biological influences needed for flux quantification and decision making. New high-resolution maps of land cover (0.6 m) and irrigation (30 m) derived from optical and thermal sensors can simultaneously resolve landscape influences related to vegetation type (tree, grass, shrub), land use, and fragmentation needed to accurately quantify biological influences on CO2 exchange in complex urban environments. We integrate these maps with the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) to quantify spatial and seasonal variability in gross primary production (GPP) across urban and non-urban regions of Southern California Air Basin (SoCAB). Results show that land use and landscape fragmentation have a significant influence on urban GPP and canopy temperature within the water-limited Mediterranean SoCAB climate. Irrigated vegetation accounts for 31% of urban GPP, driven by turfgrass, and is more productive (1.7 vs 0.9 µmol m-2 s-1) and cooler (2.2 ± 0.5 K) than non-irrigated vegetation during hot dry summer months. Fragmented landscapes, representing mostly vegetated urban greenspaces, account for 50% of urban GPP. Cooling from irrigation alleviates strong warming along greenspace edges within 100 m of impervious surfaces, and increases GPP by a factor of two, compared to non-irrigated edges. Finally, we note that non-irrigated shrubs are typically more productive than non-irrigated trees and grass, and equally productive as irrigated vegetation. These results imply a potential water savings benefit of urban shrubs, but more work is needed to understand carbon vs water usage tradeoffs of managed vs unmanaged vegetation.


Assuntos
Carbono , Temperatura Alta , Ciclo do Carbono , Clima , Tecnologia de Sensoriamento Remoto
17.
Sci Total Environ ; 811: 152339, 2022 Mar 10.
Artigo em Inglês | MEDLINE | ID: mdl-34914985

RESUMO

Coastal wetlands are of great ecological and economic value but face significant degradation and losses because of human activities. Nevertheless, the changes in spatiotemporal landscape patterns, which have occurred as a result of coastal wetland losses, have not been well documented under the rapid urbanization in coastal zones. In this study, an algorithm based on periodic tidal inundations and full time-series indices was developed to map the detailed status and trends in the coastal wetlands in Fujian Province from 1994 to 2018 by using more than archived 5000 Landsat images. The results showed that in 2018, there were 1136.56 km2 of coastal wetlands along the coast of Fujian with an overall accuracy of 95.63%, which were mainly distributed in estuaries and bays. These coastal wetlands consisted of tidal flats, low marshes, and high marshes with proportions of 84.91%, 13.05%, and 2.04%, respectively. An unprecedented loss of coastal wetlands has occurred in Fujian Province, with an annual rate of 15.44 km2/a from 1994 to 2018. Many coastal wetlands were reclaimed, dredged, and converted into inland areas for aquaculture ponds, ports, and built-up areas in different urbanization periods, which has led to a great loss of coastal spaces with an area of 476.87 km2. The interplay between the loss of coastal wetlands and seaward urbanization will lead to severe fragmentation and squeezing effects in the coastal zone and will weaken the coastal protection from marine disasters that is provided by coastal wetlands. Therefore, we conceived two conceptional frameworks for sustainable coastal protection based on the current situations of the coastal communities to provide a trade-off between economic development and the protection of coastal developing countries in the world.


Assuntos
Tecnologia de Sensoriamento Remoto , Áreas Alagadas , Conservação dos Recursos Naturais , Monitoramento Ambiental , Atividades Humanas , Humanos
18.
Sci Total Environ ; 811: 152480, 2022 Mar 10.
Artigo em Inglês | MEDLINE | ID: mdl-34923008

RESUMO

Forest plays an important role in reducing pressure on the natural environment, weaking the influence of greenhouse effects, and sequestrating atmospheric carbon dioxide. So far, due to the lack of complete understanding of forest ecosystem processes and the limitations on the scope of application of evaluation methods, there are still great uncertainties in the researches on carbon fluxes of forest ecosystems in China at the national level. In this study, an individual tree species FORCCHN model, which could flexibly use the inventory data as the initial field (more accurately) or use the remote sensing information to inverse initial field was applied. The dynamics of key carbon cycle fluxes (net primary productivity (NPP) and net ecosystem productivity (NEP)) and carbon sequestration of forest ecosystems in China from 1982 to 2019 were simulated based on remote sensing data and FORCCHN model. The results showed that forest ecosystems in China had great carbon sequestration potential over the past 39 years. From 1982 to 2019, the NPP of Chinese forests presented a fluctuated increase. Total NPP from 2011 to 2019 ranged from 0.91 PgC·a-1 to 1.14 PgC·a-1. Annual average NEP of forest ecosystems in China from 2011 to 2019 was 0.199 PgC·a-1 (1Pg = 1015 g). Influenced by climate, soil and vegetation, carbon sequestration potential in Chinese forest ecosystems presented obvious regional differences in space. The spatial distribution of NEP gradually increased from Northwest to Southeast China. From 2011 to 2019, forests in Yunnan Province had the strongest carbon storage capacity (72.79 TgC·a-1, 1Tg = 1012 g), followed by forests in Guangxi (18.49 TgC·a-1) and forests in Guangdong (10.01 TgC·a-1). Our results not only address concerns about carbon sequestration but also reflect the importance of Chinese forest resources in the development of the national economy and society.


Assuntos
Ecossistema , Tecnologia de Sensoriamento Remoto , Ciclo do Carbono , Sequestro de Carbono , China , Florestas , Árvores
19.
Environ Monit Assess ; 194(1): 45, 2021 Dec 27.
Artigo em Inglês | MEDLINE | ID: mdl-34958415

RESUMO

Canopy cover is an important structural trait that is frequently used in forest inventories to assess sustainability as well as many other important aspects of forest stands. Remote sensing data is more effective and suitable for canopy cover estimating than traditional field measurements such as sample plots, especially at broad scales. Measurement and mapping this attribute in fine-scale is a difficult task. Aerial imagery using unmanned aerial vehicle (UAV) has been recognized as an excellent tool to estimate canopy attributes. In this study, we compared the potential of using digital hemispherical photography (DHP), digital cover photography (DCP), UAV RGB data, and canopy height model (CHM) for estimation of canopy cover of mix broad-leaved forest on seven different stands. The canopy cover was measured from two digital canopy photographic methods, including DHP (as the reference method) and DCP. The stand orthophotos were segmented using a multi-resolution image segmentation method. Afterward, the classification in two classes of the canopy cover and the non-canopy cover was conducted using minimum distance classification to estimate canopy cover. The CHM layer was generated based on the SfM algorithm and utilized in the canopy cover estimation in each stand as auxiliary data. The results showed a slight improvement when we used the CHM as auxiliary data. Overall, the results showed that the efficiency of the ground digital canopy photographic methods (zenith view) in multi-storied and dense forests is the lowest. In return, our method for digital aerial canopy photography (object-based canopy segmentation and classification) is simple, quick, efficient, and cost-effective.


Assuntos
Tecnologia de Sensoriamento Remoto , Árvores , Monitoramento Ambiental , Florestas
20.
Ying Yong Sheng Tai Xue Bao ; 32(12): 4315-4326, 2021 Dec.
Artigo em Chinês | MEDLINE | ID: mdl-34951273

RESUMO

We analyzed the relationship between gross primary productivity (GPP) and environmental factors at Sidaoqiao Superstation of the Ejina Oasis in China's Gobi Desert, by combining eddy flux and meteorological data from 2018 to 2019 and Sentinel-2 remote sensing images from 2017 to 2020. We evaluated the applicability of 12 remote sensing vegetation indices to simulate the growth of Tamarix chinensis and extract key phenological metrics. A seven-parameter double-logistic function (DL-7) + global model function (GMF) was used to fit the growth curves of GPP and vegetation indices. Three key phenological metrics, i.e., the start of the growing season (SOS), the peak of the growing season (POS), and the end of the growing season (EOS), were extracted for each year. Growing season degree days (GDD) and soil water content were the main environmental factors affecting the phenological dynamics of T. chinensis. Compared with 2018, the lower temperatures in 2019 resulted in slower accumulation rate of accumulated temperature before the SOS. T. chinensis required longer heat accumulation to enter growing season, which might cause later SOS in 2019. The hydrothermal conditions between SOS and POS were similar for 2018 and 2019. Howe-ver, the POS in 2019 was 8 days later than that in 2018, because of the late SOS in 2019. Following the POS in 2019, high GDD and low soil water content caused the T. chinensis to suffer from water stress, resulting in a shortened late growing season. The linear regression between the standardized Sentinel-2 vegetation index and the average value of GPP between 10:00 and 14:00 indicated that the enhanced vegetation index of the broadband vegetation index and the chlorophyll red edge index, inverted red edge chlorophyll index, and red-edge normalized difference vegetation index (NDVI705) of the narrowband vegetation index were highly consistent with the GPP of T. chinensis. Remote sensing extraction of SOS and POS of T. chinensis suggested that the Sentinel-2 narrowband vegetation index was more accurate than the broadband vegetation index. The modified chlorophyll absorption in reflectance index provided the most accurate extraction of SOS, while the MERIS terrestrial chlorophyll index provided the most accurate extraction of EOS. Conversely, the Sentinel-2 broadband vegetation index was the most accurate for extracting POS, especially the 2-band enhanced vegetation index and the near-infrared reflectance of vegetation. Overall, NDVI705 was the best index to estimate phenological metrics.


Assuntos
Tamaricaceae , Benchmarking , Dióxido de Carbono , Tecnologia de Sensoriamento Remoto , Estações do Ano
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