共查询到20条相似文献，搜索用时 907 毫秒
1.
AbstractThis study used multi-date Landsat images to quantify mangrove cover changes in the whole of Bangladesh from 1976 to 2015. Images were pre-processed with an atmospheric correction using Dark Object Subtraction (DOS) and Relative Radiometric Normalization (RRN) using Pseudo-Invariant Features (PIFs). Land Use/Land Cover (LU/LC) classification map was generated using Maximum Likelihood (MaxLike) algorithm, indicating the areal extent of mangroves increased by 3.1% between 1976 and 2015, where 1.79% of this increase occurred between 2000 and 2015. Though mangrove areas remained almost constant in the Sundarbans, Chakaria Sundarbans has almost disappeared between 1976 and 1989. The overall accuracy of Landsat MSS, TM, ETM+, and L8 OLI classified images were 80%, 80%, 87%, and 97% respectively. The study also found deforestation, shrimp & salt farm, coastal erosion and sedimentation, and mangrove plantation could be responsible for mangrove changes in Bangladesh. 相似文献
2.
D. G. Shah Anjali Bahuguna B. Deshmukh S. R. Nayak H. S. Singh B. H. Patel 《Journal of the Indian Society of Remote Sensing》2005,33(1):155-163
Mangroves of the Marine National Park constitute the second largest patch of mangroves in Gujarat, extending up to 11,000
ha, comprising six species of mangroves. Earlier studies carried out using remote sensing data pertained to baseline data
generation and mapping and monitoring the mangroves (density-wise) of the Park from 1975 to 1993. Using IRS IC/ID LISS III
data (1998–2001) supported by ground data, the distribution of different mangrove communities in the Park has been attempted.
Amongst various image-processing techniques, band ratioing followed by supervised classification gave the best result (classification
accuracy was 92%).Avicennia community is the most dominant community accounting for more than 70% of the area. TheRhizophora community occupies the inward margins of the creeks and theCeriops community is present in the interior regions. The ecotone between the marsh and mangrove communities has been identified
as the transitional mangroves (Avicennia alba, Sueada), representing the transition from the less saline mangrove to the highly saline marsh community. The zoning of the mangroves
has also helped in assessing the diversity of the region. Based on the richness of species, three areas, namely Bhains Bid,
North-east Dide Ka Bet and South-east Chhad Island have been identified as highly diverse (most suitable area for preservation). 相似文献
3.
Cenk Donmez Suha Berberoglu Ahmet Cilek Peter Krause 《International Journal of Digital Earth》2020,13(8):915-938
ABSTRACT Mediterranean region is identified as a primary hot-spot for climate change due to the expected temperature and rainfall changes. Understanding the potential impacts of climate change on the hydrology in these regions is an important task to develop long-term water management strategies. The aim of this study was to quantify the potential impacts of the climate changes on local hydrological quantities at the Goksu Watershed at the Eastern Mediterranean, Turkey as a case study. A set of Representative Concentration Pathways (RCP) scenarios were used as drivers for the conceptual hydrological model J2000 to investigate how the hydrological system and the underlying processes would respond to projected future climate conditions. The model was implemented to simulate daily hydrological quantities including runoff generation, Actual Evapotranspiration (AET) and soil-water balance for present (2005–2015) and future (up to 2100). The results indicated an increase of both precipitation and runoff throughout the region from January to March. The region showed a strong seasonally dependent runoff regime with higher flows during winter and spring and lower flows in summer and fall. The study provides a comparative methodology to include meteorological-hydrological modelling integration that can be feasible to assess the climate change impacts in mountainous regions. 相似文献
4.
The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R2 > 0.85). 相似文献
5.
氮素是植被整个生命周期的必要元素,红树林冠层氮素含量（CNC）遥感估算对红树林健康监测具有重要意义。以广东湛江高桥红树林保护区为研究区,本文旨在基于Sentinel-2影像超分辨率重建技术进行红树林CNC估算和空间制图。研究首先基于三次卷积重采样、Sen2Res和SupReMe算法实现Sentinel-2影像从20 m分辨率到10 m的重建;然后以重建后的影像和原始20 m影像为数据源构建40个相关植被指数,采用递归特征消除法（SVM-RFE）确定CNC估算的最优变量组合,进而构建CNC反演的核岭回归（KRR）模型;最后选取最优模型实现CNC制图。研究结果表明：基于Sen2Res和SupReMe超分辨率算法的重建影像不仅与原始影像具有很高的光谱一致性,且明显提高了影像的清晰度和空间细节。红树林CNC反演波段主要集中在红（B4）、红边（B5）、近红外波段（B8a）以及短波红外波段（B11和B12）,与“红边波段”相关的植被指数（RSSI和TCARIre1/OSAVI）也是红树林CNC反演的有效变量。基于3种方法重建后10 m的影像构建的模型反演精度（R2val>0.579）均优于原始20 m的影像（R2val=0.504）;基于Sen2Res算法重建影像构建的反演模型拟合精度（R2val=0.630,RMSE_val=5.133,RE_val=0.179）与基于三次卷积重采样重建影像的模型拟合精度（R2val=0.640,RMSE_val=5.064,RE_val=0.179）基本相当,前者模型验证精度（R2cv=0.497,RMSE_cv=5.985,RE_cv=0.214）较高且模型变量选择数量最为合理。综合重建影像光谱细节及模型精度,基于Sen2Res算法重建的Sentinel-2影像在红树林CNC估算中具有良好的应用潜力,能为区域尺度红树林冠层健康状况的精细监测提供有效的方法借鉴和数据支撑。 相似文献
6.
Forests play a critical role in ecological functioning, global warming and climate change through its unique potential to capture and hold carbon (C). Biomass is one of the indicator of the status of forests hence accurate assessment and biomass mapping is important for sustainable forest management. The objectives of this study is to estimate above ground biomass (AGB) from field inventory data and to map AGB combining field inventory data, remote sensing and geo-statistical model. In the present study stratified random sampling were used for estimation of biomass in which 59 plots were laid down in different homogenous strata depending on the NDVI values for the region of Maharashtra Western Ghats. The above ground biomass from field ranged from 0.05 to 271 t-dry wt ha?1 in which trees added maximum towards total biomass followed by shrubs and herbs. This paper evaluates the best vegetation indices to estimate biomass. This study was carried out by using Landsat TM satellite data and field inventory data in the Ratnagiri district of Maharashtra, India. A significant correlation was observed between biomass and vegetation indices. The best fit regression equation developed from field above ground biomass and NDVI with R2 value of 0.61 was used for spectral modeling to estimate the geospatial distribution of AGB in the entire region. The results of spatial predictions Geostatistical technique and remotely sensed data as auxiliary variables were compared using statistical error methods. This study employed Mean error, Root-Mean-Square error, Average Standard error and Root-Mean Square Standardized error. The ME, RMSE, Average Standard error and Root-Mean Square Standardized error was 0.078, 8.032, 7.982 and 0.967 respectively. The results showed that cokriging technique is one of the geostatistical method for spatial predictions of biomass in the studied region. The present study revealed that remote sensing technique combined with field sampling provides quick and reliable estimates of above ground biomass and carbon pool and can be used as baseline information for further temporal studies of biomass status of the region and in planning of forest and natural resources management. 相似文献
7.
Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use. 相似文献
8.
Biodiversity Conservation in the REDD 总被引：1，自引：0，他引：1
Gary D Paoli Philip L Wells Erik Meijaard Matthew J Struebig Andrew J Marshall Krystof Obidzinski Aseng Tan Andjar Rafiastanto Betsy Yaap JW Ferry Slik Alexandra Morel Balu Perumal Niels Wielaard Simon Husson Laura D'Arcy 《Carbon balance and management》2010,5(1):1-9
Background
Forests occur across diverse biomes, each of which shows a specific composition of plant communities associated with the particular climate regimes. Predicted future climate change will have impacts on the vulnerability and productivity of forests; in some regions higher temperatures will extend the growing season and thus improve forest productivity, while changed annual precipitation patterns may show disadvantageous effects in areas, where water availability is restricted. While adaptation of forests to predicted future climate scenarios has been intensively studied, less attention was paid to mitigation strategies such as the introduction of tree species well adapted to changing environmental conditions.Results
We simulated the development of managed forest ecosystems in Germany for the time period between 2000 and 2100 under different forest management regimes and climate change scenarios. The management regimes reflect different rotation periods, harvesting intensities and species selection for reforestations. The climate change scenarios were taken from the IPCC's Special Report on Emission Scenarios (SRES). We used the scenarios A1B (rapid and successful economic development) and B1 (high level of environmental and social consciousness combined with a globally coherent approach to a more sustainable development). Our results indicate that the effects of different climate change scenarios on the future productivity and species composition of German forests are minor compared to the effects of forest management.Conclusions
The inherent natural adaptive capacity of forest ecosystems to changing environmental conditions is limited by the long life time of trees. Planting of adapted species and forest management will reduce the impact of predicted future climate change on forests. 相似文献9.
Abstract This study investigates urban climatologic modification associated with development and changing land use in the relatively arid urban environment of Phoenix, Arizona. An analysis of surface temperatures, as portrayed on Landsat thermal remotely sensed data, were compared to current land use patterns in regions of the rapidly expanding urban landscape. A second focus of this study involved investigation of the surface temperatures of this environment, as extracted from the radiometric data of the Landsat thermal band, to provide insights into the complexities of the relationship to the near‐surface atmospheric temperature, a parameter used extensively in climate change analyses and in models for energy and water demand in this desert region. The near surface air temperature is usually measured approximately two meters above the ground surface. In general, spatial temperature patterns of the metropolitan region were strongly correlated with the presence of open water or biomass which provide an evapotranspirative heat sink. 相似文献
10.
César Alejandro Berlanga-Robles Arturo Ruiz-Luna Marta Rocío Nepita Villanueva 《地理信息系统科学与遥感》2019,56(3):338-361
Monthly time series, from 2001 to 2016, of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from MOD13Q1 products were analyzed with Seasonal Trend Analysis (STA), assessing seasonal and long-term changes in the mangrove canopy of the Teacapan-Agua Brava lagoon system, the largest mangrove ecosystem in the Mexican Pacific coast. Profiles from both vegetation indices described similar phenological trends, but the EVI was more sensitive in detecting intra-annual changes. We identified a seasonal cycle dominated by Laguncularia racemosa and Rhizophora mangle mixed patches, with the more closed canopy occurring in the early autumn, and the maximum opening in the dry season. Mangrove patches dominated by Avicennia germinans displayed seasonal peaks in the winter. Curves fitted for the seasonal vegetation indices were better correlated with accumulated precipitation and solar radiation among the assessed climate variables (Pearson’s correlation coefficients, estimated for most of the variables, were r ≥ 0.58 p < 0.0001), driving seasonality for tidal basins with mangroves dominated by L. racemosa and R. mangle. For tidal basins dominated by A. germinans, the maximum and minimum temperatures and monthly precipitation fit better seasonally with the vegetation indices (r ≥ 0.58, p < 0.0001). Significant mangrove canopy reductions were identified in all the analyzed tidal basins (z values for the Mann-Kendall test ≤ ?1.96), but positive change trends were recorded in four of the basins, while most of the mangrove canopy (approximately 87%) displayed only seasonal canopy changes or canopy recovery (z > ?1.96). The most resilient mangrove forests were distributed in tidal basins dominated by L. racemosa and R. mangle (Mann-Kendal Tau t ≥ 0.4, p ≤ 0.03), while basins dominated by A. germinans showed the most evidence of disturbance. 相似文献
11.
The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha−1. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R2 = 0.62 > 0.52, RMSE = 50.36 Mg ha−1<56.63 Mg ha−1). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R2 = 0.62, RMSE = 51.03 Mg ha−1). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests. 相似文献
12.
The present study attempts to assess the biological richness in Sunderban Biosphere Reserve (SBR) using a three-pronged approach
i.e. satellite image (IRS 1D LISS-III) for vegetation/land use stratification, landscape analysis for disturbance regimes
assessment and the disturbance regimes together with the ecosystem uniqueness, species richness and importance value for biological
richness modelling. The study showed that four mangrove categories, viz., Avicennia, Phoenix, mixed mangroves and mangrove scrub, cover 23.21 per cent of the total geographical area of SBR. The largest area is occupied
by mixed mangroves (18.31%). The overall accuracy of the vegetation/land use map worked out to be 91.67 per cent. The disturbance
analysis revealed that the vegetation types were not much disturbed. Shannon-Weaver’s index of diversity was highest in case
of mixed mangrove. The results revealed that 75 per cent forest area has high biological richness. 相似文献
13.
Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities. 相似文献
14.
Mărgărit-Mircea Nistor 《地球空间信息科学学报》2013,16(4):345-358
ABSTRACTThe impact of climate change on groundwater vulnerability has been assessed in the Pannonian basin over 1961–2070. High-resolution climate models, aquifers composition, land cover, and digital elevation model were the main factors which served to perform the spatial analysis using Geographical Information Systems. The analysis reported here is focused on the long-term period, including three temporal time sets: the past period of 1961–1990 (1990s), the present period of 2011–2040 (2020s), and the future period of 2041–2070 (2050s). During the 1990s, the high and very high areas of groundwater vulnerability were identified in all the central, western, eastern, southeastern, and northern sides of the Pannonian basin. In these areas, the water availability is lower and the pollution load index is high, due to the agricultural activities. The low and very low vulnerability class was depicted in the South-West part of the basin and in few locations from the peripheral areas, mainly in the North and West. The medium groundwater vulnerability spreads over the Pannonian basin, but it is more concentrated in the central, South, and South-West. The most affected territory is Hungary, while the territories of Slovenia, Croatia, and Bosnia and Herzegovina are less affected. In the present and future periods, the very high groundwater vulnerability increased in areas by 0.74% and 0.87%, respectively. The low class area decreased between the 1990s and the 2020s by 2.33% and it is expected to decrease up to 2.97% in the 2050s. Based on this analysis and the groundwater vulnerability maps, the Pannonian basin appears more vulnerable to climate change in the present and future. These findings demonstrate that the aquifers from Pannonian basin experience high negative effect under climate conditions. In addition, the land cover contributes to this negative status of groundwater resources. The original maps of groundwater vulnerability represent an instrument for water management planning and for research. 相似文献
15.
Gianpaolo Coro Pasquale Pagano Anton Ellenbroek 《International Journal of Digital Earth》2020,13(5):567-585
ABSTRACTForecasting environmental parameters in the distant future requires complex modelling and large computational resources. Due to the sensitivity and complexity of forecast models, long-term parameter forecasts (e.g. up to 2100) are uncommon and only produced by a few organisations, in heterogeneous formats and based on different assumptions of greenhouse gases emissions. However, data mining techniques can be used to coerce the data to a uniform time and spatial representation, which facilitates their use in many applications. In this paper, streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to (i) standardise and harmonise the data representations, (ii) produce intermediate scenarios and new informative parameters, and (iii) align all sets on a common time and spatial resolution. Time series cross-correlation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas. Our results highlight that (i) the Mediterranean Sea may have a standalone ‘response’ to climate change with respect to other areas, (ii) the Poles are most representative of global forecasted change, and (iii) the trends are generally alarming for most oceans. 相似文献
16.
Jinfeng Chang Philippe Ciais Nicolas Viovy Jean-François Soussana Katja Klumpp Benjamin Sultan 《Carbon balance and management》2017,12(1):11
Background
Europe has warmed more than the global average (land and ocean) since pre-industrial times, and is also projected to continue to warm faster than the global average in the twenty-first century. According to the climate models ensemble projections for various climate scenarios, annual mean temperature of Europe for 2071–2100 is predicted to be 1–5.5 °C higher than that for 1971–2000. Climate change and elevated CO2 concentration are anticipated to affect grassland management and livestock production in Europe. However, there has been little work done to quantify the European-wide response of grassland to future climate change. Here we applied ORCHIDEE-GM v2.2, a grid-based model for managed grassland, over European grassland to estimate the impacts of future global change.Results
Increases in grassland productivity are simulated in response to future global change, which are mainly attributed to the simulated fertilization effect of rising CO2. The results show significant phenology shifts, in particular an earlier winter-spring onset of grass growth over Europe. A longer growing season is projected over southern and southeastern Europe. In other regions, summer drought causes an earlier end to the growing season, overall reducing growing season length. Future global change allows an increase of management intensity with higher than current potential annual grass forage yield, grazing capacity and livestock density, and a shift in seasonal grazing capacity. We found a continual grassland soil carbon sink in Mediterranean, Alpine, North eastern, South eastern and Eastern regions under specific warming level (SWL) of 1.5 and 2 °C relative to pre-industrial climate. However, this carbon sink is found to saturate, and gradually turn to a carbon source at warming level reaching 3.5 °C.Conclusions
This study provides a European-wide assessment of the future changes in productivity and phenology of grassland, and their consequences for the management intensity and the carbon balance. The simulated productivity increase in response to future global change enables an intensification of grassland management over Europe. However, the simulated increase in the interannual variability of grassland productivity over some regions may reduce the farmers’ ability to take advantage of the increased long-term mean productivity in the face of more frequent, and more severe drops of productivity in the future.17.
科学准确地监测红树林是保护海陆过渡性生态系统的基础和前提,但红树林分布于潮间带,难以进行大规模人工监测。遥感技术能够对红树林进行长时间、大面积监测,但已有研究尚存不足。一方面,红树林分布于热带、亚热带区域,受到天气条件限制难以获得长时间覆盖的有效光学遥感数据;另一方面,红树林极易与其他陆生植被混淆,仅利用多波段数据的光谱信息难以精确识别。本文以恒河三角洲孙德尔本斯地区为例,基于谷歌地球引擎GEE（Google Earth Engine）获取2016年全年的Landsat 8 OLI和Sentinel-2 MSI数据,利用物候信息进行红树林提取研究。首先,基于最小二乘回归构建两个传感器在相同指数之间的关系,重建时间序列数据,之后根据可分性判据选取增强型植被指数EVI（Enhanced Vegetation Index）和陆地表面水分指数LSWI（Land Surface Water Index）。其次,对两个指数的时间序列数据进行Savitzky-Golay滤波处理,并分别提取生长期始期等13种物候信息。最后,将两个指数的物候信息进行特征级联,采用随机森林RF（Random Forest）方法进行分类,提取研究区红树林范围。实验结果表明：Landsat 8 OLI和Sentinel-2 MSI数据融合可有效提升时间序列质量,与基于单一传感器数据的分类结果相比,总体精度提高1.58%;物候信息可以显著增强红树林与其他植被的可分性,与直接使用时间序列数据的分类结果相比,总体精度提高1.92%;同时考虑EVI和LSWI指数可极大地提升分类效果,与采用单一指数相比,总体精度分别提高14.11%和9.69%。因此,本文通过数据融合、物候信息提取和指数特征级联可以更好地提取红树林,总体精度达到91.02%,Kappa系数为0.892。研究验证了物候信息在红树林遥感监测中的应用潜力,提出的方法对科学准确地监测全球或区域红树林具有一定参考价值。 相似文献
18.
Anirban Akhand Anirban Mukhopadhyay Abhra Chanda Sandip Mukherjee Abhishek Das Sourav Das Sugata Hazra Debasis Mitra S. B. Choudhury K. H. Rao 《Journal of the Indian Society of Remote Sensing》2017,45(1):147-154
Sundarban, the largest single patch of mangrove forest of the world is shared by Bangladesh (~ 60 %) and India (~ 40 %). Loss of mangrove biomass and subsequent potential emission of carbon dioxide is reported from different parts of the world. We estimated the loss of above ground mangrove biomass and subsequent potential emission of carbon dioxide in the Indian part of the Sundarban during the last four decades. The loss of mangrove area has been estimated with the help of remotely sensed data and potential emission of carbon dioxide has been evaluated with the help of published above ground biomass data of Indian Sundarban. Total loss of mangrove area was found to be 107 km2 between the year 1975 and 2013. Amongst the total loss ~60 % was washed away in the water by erosion, ~ 23 % was converted into barren lands and the rest were anthropogenically transformed into other landforms. The potential carbon dioxide emission due to the degradation of above ground biomass was estimated to be 1567.98 ± 551.69 Gg during this period, which may account to 64.29 million $ in terms of the social cost of carbon. About three-forth of the total mangrove loss was found in the peripheral islands which are much more prone to erosion. Climate induced changes and anthropogenic land use change could be the major driving force behind this loss of ‘blue carbon’. 相似文献
19.
Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research. 相似文献
20.
Fabián B Gálvez Andrew T Hudak John C Byrne Nicholas L Crookston Robert F Keefe 《Carbon balance and management》2014,9(1):1-13