Remote-sensing based approach to forecast habitat quality under climate change scenarios

This project deals with the implementation of an innovative water management system in Mediterranean countries (i.e. Tunisia and Egypt), which suffer from chronic water scarcity, together with two European countries (Germany and Italy). The consortium is developing and applying synergic methods and algorithms for investigating the water cycle, using remote sensing techniques.The focus is on the use of satellite data (both optical and microwave) for monitoring vegetation cover and water status along with soil moisture temporal evolutions in order to improve the knowledge of the water cycle in arid areas. Both local and regional monitoring are carried out in order to investigate different spatial scales.Environmental models and algorithms for the retrieval of hydrological parameters have been developed in the frame of this project in order to match the main goal of the project, i.e. to propose practical and cost-effective solutions for driving and updating a method for the sustainable use of water in agriculture.&#160;An optimized management of water resources for cultivated lands on Egyptian Delta (Northern part) and Tunisian territory will be realized by analyzing the available spatial and temporal data for the areas of interest appropriately selected for this purpose. As such, an efficient water use, equitable distribution of water resources, community participation in decisions, and sustainable system operation over time can be supported.First of all, we aim to localize different crop and irrigation techniques for the study regions. This information is required as a basis for further investigations and assessments. Secondly, the water efficiency for different lands, crop types and irrigation systems will be assessed.Afterwards, possible improvements in agricultural practice with respect to climate change scenarios and information on water efficiency will be determined by rating the outcome from the assessment.

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Coupling Remote Sensing and Hydrological Model for Evaluating the Impacts of Climate Change on Streamflow in Data-Scarce Environment

Sustainability ◽

10.3390/su132414025 ◽

2021 ◽

Vol 13 (24) ◽

pp. 14025

Author(s):

Fazlullah Akhtar ◽

Usman Khalid Awan ◽

Christian Borgemeister ◽

Bernhard Tischbein

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Water Resources ◽

Swat Model ◽

Coefficient Of Determination ◽

Climate Change Scenarios ◽

Impact Of Climate Change ◽

Streamflow Data ◽

The Impact ◽

Impacts Of Climate Change

The Kabul River Basin (KRB) in Afghanistan is densely inhabited and heterogenic. The basin’s water resources are limited, and climate change is anticipated to worsen this problem. Unfortunately, there is a scarcity of data to measure the impacts of climate change on the KRB’s current water resources. The objective of the current study is to introduce a methodology that couples remote sensing and the Soil and Water Assessment Tool (SWAT) for simulating the impact of climate change on the existing water resources of the KRB. Most of the biophysical parameters required for the SWAT model were derived from remote sensing-based algorithms. The SUFI-2 technique was used for calibrating and validating the SWAT model with streamflow data. The stream-gauge stations for monitoring the streamflow are not only sparse, but the streamflow data are also scarce and limited. Therefore, we selected only the stations that are properly being monitored. During the calibration period, the coefficient of determination (R2) and Nash–Sutcliffe Efficiency (NSE) were 0.75–0.86 and 0.62–0.81, respectively. During the validation period (2011–2013), the NSE and R2 values were 0.52–0.73 and 0.65–0.86, respectively. The validated SWAT model was then used to evaluate the potential impacts of climate change on streamflow. Regional Climate Model (RegCM4-4) was used to extract the data for the climate change scenarios (RCP 4.5 and 8.5) from the CORDEX domain. The results show that streamflow in most tributaries of the KRB would decrease by a maximum of 5% and 8.5% under the RCP 4.5 and 8.5 scenarios, respectively. However, streamflow for the Nawabad tributary would increase by 2.4% and 3.3% under the RCP 4.5 and 8.5 scenarios, respectively. To mitigate the impact of climate change on reduced/increased surface water availability, the SWAT model, when combined with remote sensing data, can be an effective tool to support the sustainable management and strategic planning of water resources. Furthermore, the methodological approach used in this study can be applied in any of the data-scarce regions around the world.

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Estimating potential range shift of some wild bees in response to climate change scenarios in northwestern regions of Iran

Journal of Ecology and Environment ◽

10.1186/s41610-021-00189-8 ◽

2021 ◽

Vol 45 (1) ◽

Author(s):

Ehsan Rahimi ◽

Shahindokht Barghjelveh ◽

Pinliang Dong

Keyword(s):

Climate Change ◽

Habitat Quality ◽

Species Distribution ◽

Range Shift ◽

Potential Range ◽

Climate Change Scenarios ◽

Natural Ecosystem ◽

Quality Changes ◽

Wild Bees ◽

The Future

Abstract Background Climate change is occurring rapidly around the world, and is predicted to have a large impact on biodiversity. Various studies have shown that climate change can alter the geographical distribution of wild bees. As climate change affects the species distribution and causes range shift, the degree of range shift and the quality of the habitats are becoming more important for securing the species diversity. In addition, those pollinator insects are contributing not only to shaping the natural ecosystem but also to increased crop production. The distributional and habitat quality changes of wild bees are of utmost importance in the climate change era. This study aims to investigate the impact of climate change on distributional and habitat quality changes of five wild bees in northwestern regions of Iran under two representative concentration pathway scenarios (RCP 4.5 and RCP 8.5). We used species distribution models to predict the potential range shift of these species in the year 2070. Result The effects of climate change on different species are different, and the increase in temperature mainly expands the distribution ranges of wild bees, except for one species that is estimated to have a reduced potential range. Therefore, the increase in temperature would force wild bees to shift to higher latitudes. There was also significant uncertainty in the use of different models and the number of environmental layers employed in the modeling of habitat suitability. Conclusion The increase in temperature caused the expansion of species distribution and wider areas would be available to the studied species in the future. However, not all of this possible range may include high-quality habitats, and wild bees may limit their niche to suitable habitats. On the other hand, the movement of species to higher latitudes will cause a mismatch between farms and suitable areas for wild bees, and as a result, farmers will face a shortage of pollination from wild bees. We suggest that farmers in these areas be aware of the effects of climate change on agricultural production and consider the use of managed bees in the future.

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Some operational uses of satellite remote sensing and marine GIS for sustainable fisheries and aquaculture

ICES Journal of Marine Science ◽

10.1093/icesjms/fsq190 ◽

2011 ◽

Vol 68 (4) ◽

pp. 687-695 ◽

Cited By ~ 41

Author(s):

Sei-Ichi Saitoh ◽

Robinson Mugo ◽

I Nyoman Radiarta ◽

Shinsuke Asaga ◽

Fumihiro Takahashi ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Satellite Remote Sensing ◽

Simple Algorithm ◽

Climate Change Scenarios ◽

Sustainable Fisheries ◽

Intergovernmental Panel ◽

Fishing Activity ◽

Mizuhopecten Yessoensis ◽

The Impact

Abstract Saitoh, S-I., Mugo, R., Radiarta, I N., Asaga, S., Takahashi, F., Hirawake, T., Ishikawa, Y., Awaji, T., In, T., and Shima, S. 2011. Some operational uses of satellite remote sensing and marine GIS for sustainable fisheries and aquaculture. – ICES Journal of Marine Science, 68: 687–695. An overview of satellite remote-sensing (SRS) operational applications in fisheries is presented, and includes two case studies illustrating the societal benefits of SRS. The first describes the use of satellite-based vessel monitoring systems (VMS) and SRS data in a skipjack tuna (Katsuwonus pelamis) fishery, including a simple algorithm for determining fishing activity from vessel speed. The second case study illustrates the application of remotely sensed information in determining the impact of climate change on site suitability for scallop (Mizuhopecten yessoensis) aquaculture. Global warming simulated according to Intergovernmental Panel on Climate Change scenarios had a significant impact on sites with the greatest suitability for scallop aquaculture. Some challenges in the field of fisheries information systems are also discussed.

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PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-883-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 883-887

Author(s):

Hidetake Hirayama ◽

Mizuki Tomita ◽

Keitarou Hara

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Remote Sensing Data ◽

Future Climate Change ◽

Slope Aspect ◽

Climate Change Scenarios ◽

Vegetation Distribution ◽

Climate Conditions ◽

Distribution Maps ◽

Sensing Data

The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan’s cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperature of the coldest month (TMC)，warmth index (WI)， winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.

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PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-883-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 883-887

Author(s):

Hidetake Hirayama ◽

Mizuki Tomita ◽

Keitarou Hara

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Remote Sensing Data ◽

Future Climate Change ◽

Slope Aspect ◽

Climate Change Scenarios ◽

Vegetation Distribution ◽

Climate Conditions ◽

Distribution Maps ◽

Sensing Data

The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan’s cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperature of the coldest month (TMC)，warmth index (WI)， winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.

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Estimation of emission from organic soils

10.5194/egusphere-egu21-6451 ◽

2021 ◽

Author(s):

Sergiy Stepanenko ◽

Anatoliy Polevoy ◽

Alexander Mykytiuk

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Land Cover ◽

Soil Carbon ◽

Input Data ◽

Carbon Deposition ◽

Organic Soils ◽

Climate Change Scenarios ◽

Space Images ◽

Soil System

Dynamic modeling of the processes of transformation of soil organic matter is part of a more complex problem - modeling the processes of soil formation and functioning of soils, and the development of the entire soil system. It is important tool for studying the functioning and predicting changes in the soil system, quantifying the role of the soil cover in the balance of greenhouse gases in the atmosphere and in the processes of climate changeThe PEAT-GHG-Model (furthermore &#8211; PEAT-GHG-MODEL), based on further development of ROTHC-model (Coleman, Jenkinson, 2008) for mineral soil and ECOSSE model (Smith, Gottschalk et al., 2010) for organic soils.&#160;The PEAT-GHG-MODEL evaluates of CO2, CH4, N2O fluxes values at organic soils and soil carbon deposition for non-forest types of land cover. The model utilize data from existing weather stations, published soil data, and data generated by remote sensing of land cover. The model evaluates of CO2, CH4, N2O fluxes values at organic soils and soil carbon deposition, including at peatlands, retrospectively for targeted period or back in time with available space images library. The model can evaluates of CO2, CH4, N2O fluxes values at organic soils and soil carbon deposition for future period based on meteorological input data generated by climate change scenarios and land cover data generated by relevant habitats (land cover) change scenarios. The PEAT-GHG-MODEL estimates of CO2, CH4, N2O fluxes from organic soils and soil carbon deposition for non-forest types of land cover. The model input data generates by existing weather stations, remote sensing of land cover and published soils data. The model estimates of GHG emissions from organic soils, including peatlands, retrospectively for targeted period or back in time with available space images library. The model can simulates of GHG emissions for future period based on meteorological input data generated by climate change scenarios and land cover data generated by relevant habitats change scenarios. The model generates georeferenced data. Minimum land surface area, which can be evaluates by model, equal of size of one pixel of land cover images, used for remote sensing of land cover, it can be 1 m2 or less. Due to high resolution, the model estimates highly variable in space CO2, CH4, N2O fluxes with high accuracy. Maximum land surface area is not limited. The model generates data on decade and/or annual bases. Article presents the model&#8217; verification results. The model verified in 2017 by independent, from the model authors, verification team in frame of &#8220;CLIMA EAST: conservation and sustainable use of peatlands&#8221; project (UNDP-Ukraine). Direct field measurement data for two peatlands used for model verification, including one site drained, and another one is under natural hydrological conditions.&#160; The cumulative annual of CH4 and CO2 emission presented in Table.The model calculations were compared with the experimental data obtained for peat soils in the western Polesie of Belarus. The cumulative annual of CH4 and CO2 emission presented in Table.Table. Cumulative annual of CH4 and CO2 emissions according to field measurements and assessment of PEAT-GHG-MODEL<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.ccbf8aaeedff56227740161/sdaolpUECMynit/12UGE&app=m&a=0&c=482eb671aeb385948d36c48791670031&ct=x&pn=gepj.elif&d=1" alt="" width="906" height="718">&#160;<div> <div>&#160;</div> </div>

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