This page has only limited features, please log in for full access.
This study explores the potential of Sentinel-1 Synthetic Aperture Radar (SAR) to identify phenological phases of wheat, sugar beet, and canola. Breakpoint and extreme value analyses were applied to a dense time series of interferometric (InSAR) and polarimetric (PolSAR) features recorded during the growing season of 2017 at the JECAM site DEMMIN (Germany). The analyses of breakpoints and extrema allowed for the distinction of vegetative and reproductive stages for wheat and canola. Certain phenological stages, measured in situ using the BBCH-scale, such as leaf development and rosette growth of sugar beet or stem elongation and ripening of wheat, were detectable by a combination of InSAR coherence, polarimetric Alpha and Entropy, and backscatter (VV/VH). Except for some fringe cases, the temporal difference between in situ observations and breakpoints or extrema ranged from zero to five days. Backscatter produced the signature that generated the most breakpoints and extrema. However, certain micro stadia, such as leaf development of BBCH 10 of sugar beet or flowering BBCH 69 of wheat, were only identifiable by the InSAR coherence and Alpha. Hence, it is concluded that combining PolSAR and InSAR features increases the number of detectable phenological events in the phenological cycles of crops.
Johannes Löw; Tobias Ullmann; Christopher Conrad. The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany). Remote Sensing 2021, 13, 2951 .
AMA StyleJohannes Löw, Tobias Ullmann, Christopher Conrad. The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany). Remote Sensing. 2021; 13 (15):2951.
Chicago/Turabian StyleJohannes Löw; Tobias Ullmann; Christopher Conrad. 2021. "The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany)." Remote Sensing 13, no. 15: 2951.
The particle size distribution (PSD) of soil plays a vital role in wind erosion prediction. However, the impact of different pretreatments to remove binding agents for PSD and consequences for wind erosion modelling have not been tested. We collected 90 topsoil samples of Chernozems and Kastanozems from different test sites in Kazakhstan. Soil samples covered typical land-use types and farming methods with calcium carbonate contents reaching from 2.2 to 117.3 g kg−1 and soil organic carbon content from 11.2 to 48.7 g kg−1. Prior to particle size analysis by laser diffraction, samples were chemically pretreated separately and successively with 10% hydrochloric acid (HCl), to dissolve carbonates and 30% hydrogen peroxide (H2O2), to oxidise organic binding material. The HCl pretreatment resulted in incomplete dispersion or even aggregation due to calcium ions released by the dissolution of carbonates, while removing organic matter with H2O2 caused complete sample dispersion. The associated changes in PSD were overall minor, and only a few of our samples were assigned to a different texture class. Obtained PSD data was used to calculate texture-based properties, such as the geometric mean diameter (GMD), with a pedotransfer function. Calculated and measured input data were applied to the Single–event Wind Erosion Evaluation Program (SWEEP) to estimate potential soil losses. As a result, SWEEP's simulations showed substantial variations if the GMD is calculated based on PSD under the influence of different pretreatments. At the same time, there was no variation if the GMD was independently measured. We suggest that for standard particle size analysis of calcareous soils, pretreatment with HCl should be avoided because it might cause misleading results. Considering the variation induced by PSD analysis and resulting potential soil losses, pretreatments for laser diffraction analysis can be omitted for the investigated, silt-dominated Chernozems and Kastanozems if additional texture-based parameters are measured.
Moritz Koza; Gerd Schmidt; Andrej Bondarovich; Kanat Akshalov; Christopher Conrad; Julia Pöhlitz. Consequences of chemical pretreatments in particle size analysis for modelling wind erosion. Geoderma 2021, 396, 115073 .
AMA StyleMoritz Koza, Gerd Schmidt, Andrej Bondarovich, Kanat Akshalov, Christopher Conrad, Julia Pöhlitz. Consequences of chemical pretreatments in particle size analysis for modelling wind erosion. Geoderma. 2021; 396 ():115073.
Chicago/Turabian StyleMoritz Koza; Gerd Schmidt; Andrej Bondarovich; Kanat Akshalov; Christopher Conrad; Julia Pöhlitz. 2021. "Consequences of chemical pretreatments in particle size analysis for modelling wind erosion." Geoderma 396, no. : 115073.
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64
Mehdi Hosseini; Heather McNairn; Scott Mitchell; Laura Robertson; Andrew Davidson; Nima Ahmadian; Avik Bhattacharya; Erik Borg; Christopher Conrad; Katarzyna Dabrowska-Zielinska; Diego de Abelleyra; Radoslaw Gurdak; Vineet Kumar; Nataliia Kussul; Dipankar Mandal; Y. Rao; Nicanor Saliendra; Andrii Shelestov; Daniel Spengler; Santiago Verón; Saeid Homayouni; Inbal Becker-Reshef. A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing 2021, 13, 1348 .
AMA StyleMehdi Hosseini, Heather McNairn, Scott Mitchell, Laura Robertson, Andrew Davidson, Nima Ahmadian, Avik Bhattacharya, Erik Borg, Christopher Conrad, Katarzyna Dabrowska-Zielinska, Diego de Abelleyra, Radoslaw Gurdak, Vineet Kumar, Nataliia Kussul, Dipankar Mandal, Y. Rao, Nicanor Saliendra, Andrii Shelestov, Daniel Spengler, Santiago Verón, Saeid Homayouni, Inbal Becker-Reshef. A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing. 2021; 13 (7):1348.
Chicago/Turabian StyleMehdi Hosseini; Heather McNairn; Scott Mitchell; Laura Robertson; Andrew Davidson; Nima Ahmadian; Avik Bhattacharya; Erik Borg; Christopher Conrad; Katarzyna Dabrowska-Zielinska; Diego de Abelleyra; Radoslaw Gurdak; Vineet Kumar; Nataliia Kussul; Dipankar Mandal; Y. Rao; Nicanor Saliendra; Andrii Shelestov; Daniel Spengler; Santiago Verón; Saeid Homayouni; Inbal Becker-Reshef. 2021. "A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index." Remote Sensing 13, no. 7: 1348.
Approximately 10% of the global population rely on groundwater from karst aquifers. Due to complex karst structures, these aquifers have high infiltration capacities and hydraulic conductivities, which makes them vulnerable to pollution and, as prediction and management are complicated, overexploitation. As populations are growing and demand rises, we assess the current level of groundwater stress in karst aquifers with Mediterranean climates and their vulnerability (defined as the change in groundwater stress) to expected changes in temperature and precipitation on the global scale.
Our approach is based on a Groundwater Stress Index (GSI), which is calculated for 356 karst aquifers (as identified in the World Karst Aquifer Map) that have some of their area located in Mediterranean climate zones (Csa, Csb, and Csc after Köppen/Geiger). GSI are calculated from seven indicators: groundwater recharge, storage, and abstractions, surface runoff, climatic water balance, water-intensity of crops, and groundwater-dependent ecosystems. Each indicator is spatially and temporally averaged to describe a recent trend on aquifer level, resulting in one complex attribute table for the 356 aquifers. GSI is calculated as the average of the normalized indicators for each aquifer, ranging from 0 (no water stress) to 1 (extreme water stress).
Aquifers are then grouped based on similarities in two classification parameters – degree of karstification (P1) and land cover (P2). Comparison of aquifers with similar classification parameters allows to focus more directly on the relationship between groundwater stress and climate, disregarding relatively constant influences. For each group (e.g., well-developed karst, primarily agriculturally used), we plot calculated GSI values with current temperature and precipitation data. By investigating four Shared Socioeconomic Pathways (SSPs) until 2100, we identify aquifers that mimic future climate conditions for others with similar P1 and P2. We then measure the difference in groundwater stress accompanied by altered climatic factors. This change is interpreted as vulnerability to climate change.
This approach, which relies on present-day observed conditions, allows us to predict the effect of a changing climate without the need to develop a complex numerical model, which requires large amounts of data and functional understanding of aquifer behavior. While analysis is currently ongoing, we expect both groundwater stress and vulnerabilities to be high. Predicted climate zone shifts by Beck et al. (2018) indicate that, out of 356 karst aquifers with Mediterranean climates, 52 could move to more extreme arid climate zones by 2100.
Results will be visualized in the form of vulnerability maps that may serve as an “early-warning system”. For particularly threatened aquifers, we will derive recommendations for more sustainable management by suggesting strategies to lower groundwater stress. This is done by taking a closer look at the aquifer’s indicator values and identifying factors that currently contribute the most to groundwater stress.
Philipp Nußbaum; Márk Somogyvári; Christopher Conrad; Martin Sauter; Irina Engelhardt. Calculating groundwater stress and climate change-induced vulnerability of karst aquifers on a global scale. 2021, 1 .
AMA StylePhilipp Nußbaum, Márk Somogyvári, Christopher Conrad, Martin Sauter, Irina Engelhardt. Calculating groundwater stress and climate change-induced vulnerability of karst aquifers on a global scale. . 2021; ():1.
Chicago/Turabian StylePhilipp Nußbaum; Márk Somogyvári; Christopher Conrad; Martin Sauter; Irina Engelhardt. 2021. "Calculating groundwater stress and climate change-induced vulnerability of karst aquifers on a global scale." , no. : 1.
Irrigated agriculture In the Aral Sea Basin (ASB) is commonly known for its high water consumption, inefficient water management, and dysfunctional irrigation and drainage infrastructure. Since 1991, six states have been engaged in intensive irrigated agriculture in the Aral Sea Basin (ASB), Afghanistan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. In this region, irrigated agriculture is commonly known for its high water consumption, inefficient water management, and dysfunctional irrigation and drainage infrastructure. Extensive land degradation (e.g., soil salinization) is considered as the main result of mismanagement in the irrigation sector and sustainable solutions are urgently required. This study analysed international peer-reviewed scientific studies based on satellite remote sensing (RS) products and methods addressing potential improvements of irrigation water and land management in the ASB. Ways to transfer RS-based knowledge into practice were discussed using the example of the online tool WUEMoCA that was developed from 2015 to 2019 within the German Water Initiative in Central Asia (CAWa). For the period 2008–2019, a total of 49 studies contributed knowledge about land use, soils and vegetation, crop production and use of irrigation water in the ASB. The use of RS revealed increased diversification of agricultural production, spatial-temporal patterns of land degradation, and effects of varying water availability on cropping intensity. Modelling of crop yields and evapotranspiration at varying scales (i.e., farm to provincial scale) underlined the comparably moderate water productivity in the ASB. One relevant future research task is to intensively collect in-situ data for validation and secondary data and hence to mitigate the situation. In particular, improved socio-ecological and economic information could help to better understand the spatially differing drivers of soil and land degradation. Eventually, this study provides relevant information and data sources for decision-making and requirements for better integration of RS-based information into practice using online-tools like WUEMoCA.
Christopher Conrad; Muhammad Usman; Lucia Morper-Busch; Sarah Schönbrodt-Stitt. Remote sensing-based assessments of land use, soil and vegetation status, crop production and water use in irrigation systems of the Aral Sea Basin. A review. Water Security 2020, 11, 100078 .
AMA StyleChristopher Conrad, Muhammad Usman, Lucia Morper-Busch, Sarah Schönbrodt-Stitt. Remote sensing-based assessments of land use, soil and vegetation status, crop production and water use in irrigation systems of the Aral Sea Basin. A review. Water Security. 2020; 11 ():100078.
Chicago/Turabian StyleChristopher Conrad; Muhammad Usman; Lucia Morper-Busch; Sarah Schönbrodt-Stitt. 2020. "Remote sensing-based assessments of land use, soil and vegetation status, crop production and water use in irrigation systems of the Aral Sea Basin. A review." Water Security 11, no. : 100078.
Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change.
Muhammad Usman; Talha Mahmood; Christopher Conrad; Habib Bodla. Remote Sensing and Modelling Based Framework for Valuing Irrigation System Efficiency and Steering Indicators of Consumptive Water Use in an Irrigated Region. Sustainability 2020, 12, 9535 .
AMA StyleMuhammad Usman, Talha Mahmood, Christopher Conrad, Habib Bodla. Remote Sensing and Modelling Based Framework for Valuing Irrigation System Efficiency and Steering Indicators of Consumptive Water Use in an Irrigated Region. Sustainability. 2020; 12 (22):9535.
Chicago/Turabian StyleMuhammad Usman; Talha Mahmood; Christopher Conrad; Habib Bodla. 2020. "Remote Sensing and Modelling Based Framework for Valuing Irrigation System Efficiency and Steering Indicators of Consumptive Water Use in an Irrigated Region." Sustainability 12, no. 22: 9535.
Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.
Gideon Tetteh; Alexander Gocht; Marcel Schwieder; Stefan Erasmi; Christopher Conrad. Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels From Satellite Images in Different Agricultural Landscapes. Remote Sensing 2020, 12, 3096 .
AMA StyleGideon Tetteh, Alexander Gocht, Marcel Schwieder, Stefan Erasmi, Christopher Conrad. Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels From Satellite Images in Different Agricultural Landscapes. Remote Sensing. 2020; 12 (18):3096.
Chicago/Turabian StyleGideon Tetteh; Alexander Gocht; Marcel Schwieder; Stefan Erasmi; Christopher Conrad. 2020. "Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels From Satellite Images in Different Agricultural Landscapes." Remote Sensing 12, no. 18: 3096.
Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) data enables a broad representation of biophysical and structural information on target objects, enhancing crop type mapping. However, the fusion of multi-sensor dense time-series data often comes with the challenge of high dimensional feature space. In this study, we (1) evaluate how the usage of only optical, only SAR, and their fusion affect the classification accuracy; (2) identify the combination of which time-steps and feature-sets lead to peak accuracy; (3) analyze misclassifications based on the parcel size, optical data availability, and crops’ temporal profiles. Two fusion approaches were considered and compared in this study: feature stacking and decision fusion. To distinguish the most relevant feature subsets time- and variable-wise, grouped forward feature selection (gFFS) was used. gFFS allows focusing analysis and interpretation on feature sets of interest like spectral bands, vegetation indices (VIs), or data sensing time rather than on single features. This feature selection strategy leads to better interpretability of results while substantially reducing computational expenses. The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets. Similar to most other studies, the optical-SAR combination outperformed single sensor predictions. No significant difference was recorded between feature stacking and decision fusion. Random Forest (RF) appears to be robust to high feature space dimensionality. The feature selection did not improve the accuracies even for the optical-SAR feature stack with 320 features. Nevertheless, the combination of RF feature importance and time- and variable-wise gFFS rankings in one visualization enhances interpretability and understanding of the features’ relevance for specific classification tasks. For example, by enabling the identification of features that have high RF feature importance values but are, in their information content, correlated with other features. This study contributes to the growing domain of interpretable machine learning.
Aiym Orynbaikyzy; Ursula Gessner; Benjamin Mack; Christopher Conrad. Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies. Remote Sensing 2020, 12, 2779 .
AMA StyleAiym Orynbaikyzy, Ursula Gessner, Benjamin Mack, Christopher Conrad. Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies. Remote Sensing. 2020; 12 (17):2779.
Chicago/Turabian StyleAiym Orynbaikyzy; Ursula Gessner; Benjamin Mack; Christopher Conrad. 2020. "Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies." Remote Sensing 12, no. 17: 2779.
This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).
Maninder Singh Dhillon; Thorsten Dahms; Carina Kuebert-Flock; Erik Borg; Christopher Conrad; Tobias Ullmann. Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany. Remote Sensing 2020, 12, 1 .
AMA StyleManinder Singh Dhillon, Thorsten Dahms, Carina Kuebert-Flock, Erik Borg, Christopher Conrad, Tobias Ullmann. Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany. Remote Sensing. 2020; 12 (11):1.
Chicago/Turabian StyleManinder Singh Dhillon; Thorsten Dahms; Carina Kuebert-Flock; Erik Borg; Christopher Conrad; Tobias Ullmann. 2020. "Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany." Remote Sensing 12, no. 11: 1.
Textile products made with cotton produced in Pakistan, Turkey, and Uzbekistan are largely imported to European markets. This is responsible for high virtual water imports from these countries and thus puts immense pressure on their water resources, which is further extravagated due to climate change and population growth. The solution to combat the issue, on one hand, is to cut water usage for cotton irrigation, and on the other hand, to increase water productivity. The biggest challenge in this regard is the correct quantification of consumptive water use, cotton yield estimation and crop water productivities at a finer spatial resolution on regional levels, which is now possible by utilizing remote sensing (RS) data and approaches. It can also facilitate comparing regions of interest, like in this study, Pakistan, Turkey, and Uzbekistan by utilizing similar data and techniques. For the current study, MODIS data along with various climatic variables were utilized for the estimation of consumptive water use and cotton yield estimation by employing SEBAL and Light Use Efficiency (LUE) models, respectively. These estimations were then used for working out water productivities of different regions of selected countries as case studies. The results show that the study area in Turkey achieved maximum cotton water productivity (i.e. 0.75 - 1.2 kg.m-3) followed by those in Uzbekistan (0.05 – 0.85 kg.m-3) and Pakistan (0.04 – 0.23 kg.m-3). The variability is higher for Uzbekistan possibly due to agricultural transition post-soviet-union era. In the case of Pakistan, the lower cotton water productivities are mainly attributed to lower crop yields (400 – 1200 kg.ha-1) in comparison to Turkey (3850 – 5800 kg.ha-1) and Uzbekistan (450 – 2500 kg.ha-1). Although the highest crop water productivity is achieved for the study region in Turkey, there is still potential for further improvement by introducing on-farm water management. In the case of the other two countries, especially for Pakistan, major improvements are possible through maximizing crop yields. The next steps include comparisons of the results in economic out-turns.
Muhammad Usman; Talha Mahmood; Christopher Conrad. Remote Sensing based comparative analysis of cotton irrigation and water productivity in Pakistan, Turkey and Uzbekistan. 2020, 1 .
AMA StyleMuhammad Usman, Talha Mahmood, Christopher Conrad. Remote Sensing based comparative analysis of cotton irrigation and water productivity in Pakistan, Turkey and Uzbekistan. . 2020; ():1.
Chicago/Turabian StyleMuhammad Usman; Talha Mahmood; Christopher Conrad. 2020. "Remote Sensing based comparative analysis of cotton irrigation and water productivity in Pakistan, Turkey and Uzbekistan." , no. : 1.
Extensive over-exploitation of land and water resources is characterizing irrigated agriculture in the Aral Sea Basin (ASB). Over decades, inefficient and excessive water use had remarkable negative impacts on the groundwater and soil quality, hence on crop production. The countries sharing to the ASB look for opportunities to increase the sustainability in the water intensive agricultural sector that is of utmost importance for the densely populated oases as well as for the ecosystems along the river systems. This is also of urgent pressure as there is high evidence that climate change will deplete natural storages such as glaciers. One major bottleneck for spatially targeted decision and policy-making is the absence of scientific information and tools that would allow for informed decisions, e.g. on the implementation of water saving technologies, alternative land use options or water allocation. A review on scientific literature published in the period 2008-2019 underpins the potentials of remote sensing technology in combination with climate data and further geospatial information to close this gap. However, the key question is how to increase the sustainability of irrigated agriculture and water security using this technology in reality? This contribution aims to outline requirements and challenges to bring knowledge from remote sensing into practice. This will be done using the example of the online-tool Water Use Efficiency Monitor for Central Asia (WUEMoCA, http://wuemoca.net/) developed within the German Water Initiative in Central Asia (https://www.cawa-project.net/).
It was observed that remote sensing-based results remain isolated as long as they are not integrated into accessible databases, thus are unlinked from regional knowledge and information platforms, e.g., providing commonly applied approaches to water distribution. The tool WUEMoCA combines the remote sensing knowledge with climate data and socio-economic information and serves as an online database with hydrological and land-use indicators requested by regional decision-makers. To increase the ownership of the WUEMoCA tool by potential users (water management authorities and governments) and to account for the sensitivity of data in transnational water management, a toolbox is integrated allowing for user-specific own calculations and development of local databases. By doing so, users can decide by themselves to share information with others or not. So far, user feedback from the water distribution sector and governmental departments in Uzbekistan, but also from other countries assessed WUEMoCA as an important regional data source and database, but also a calculation tool for supporting informed decisions-making, highlight the tool’s relevance for increasing water security in the ASB.
Technically, the next steps may include the development of early warning systems, e.g. for droughts. Yet, it must be clear to the responsible users from the region that long-running tools from research projects can never take over important national tasks. Long-term cooperation is required. In addition, for a sustainable development of such tools, national scientific institutions require a strengthening of the capacity in the application of geoinformation technology. The latter is indicated by the fact that almost all of the published articles were submitted under affiliations from abroad.
Christopher Conrad; Muhammad Usman; Lucia Morper-Busch; Sarah Schönbrodt-Stitt. Geoinformation technology for increasing the sustainability of agricultural production and water security in the Aral Sea Basin. 2020, 1 .
AMA StyleChristopher Conrad, Muhammad Usman, Lucia Morper-Busch, Sarah Schönbrodt-Stitt. Geoinformation technology for increasing the sustainability of agricultural production and water security in the Aral Sea Basin. . 2020; ():1.
Chicago/Turabian StyleChristopher Conrad; Muhammad Usman; Lucia Morper-Busch; Sarah Schönbrodt-Stitt. 2020. "Geoinformation technology for increasing the sustainability of agricultural production and water security in the Aral Sea Basin." , no. : 1.
The shrinking groundwater resource is a major cause of ecosystem imbalance, which is further intensified by rapid changes in land use and land cover (LULC) and climate in the lower Chenab canal (LCC) of Pakistan. Present study aims to investigate groundwater dynamics using a novel approach by incorporating remote sensing data in combination with actual patterns of LULC, while statistical approach is employed for downscaling of climatic data under two emission scenarios including H3A2 and H3B2. A 3-D numerical groundwater flow model is used for evaluating current patterns of groundwater use and its dynamics. The results of water budget show a total horizontal groundwater inflow of 2844 Mm3 and an outflow of 2720.2 Mm3. The groundwater abstraction through pumping is about 17374.43 Mm3 as compared to groundwater recharge of 19933.20 Mm3, yields a surplus of 2682.87 Mm3, which raises groundwater levels in major parts of LCC. Change in rice cultivation has the highest impact on groundwater levels in upper regions of LCC, whereas higher negative changes are observed for lower parts under decreased fodder area in place of rice, cotton and sugarcane. For climate scenarios, a rise in groundwater level is observed for 2011 to 2025, whereas, its drop is expected for the periods 2026–2035 and 2036–2045 under H3A2 scenario. Due to no imminent threats to groundwater, there is an opportunity for groundwater development through water re-allocation. Groundwater status under H3B2 emission regime is rather complex during 2011–2025. Water management under such situation requires revisiting of cropping patterns and augmenting water supply through additional surface water resources. Considering the limitations of the current study, it is recommended to update model with river flow under changing climate, and to extend investigations for combined effects of LULC and climate change.
Muhammad Usman; Muhammad Uzair Qamar; Rike Becker; Muhammad Zaman; Christopher Conrad; Shoaib Salim. Numerical modelling and remote sensing based approaches for investigating groundwater dynamics under changing land-use and climate in the agricultural region of Pakistan. Journal of Hydrology 2019, 581, 124408 .
AMA StyleMuhammad Usman, Muhammad Uzair Qamar, Rike Becker, Muhammad Zaman, Christopher Conrad, Shoaib Salim. Numerical modelling and remote sensing based approaches for investigating groundwater dynamics under changing land-use and climate in the agricultural region of Pakistan. Journal of Hydrology. 2019; 581 ():124408.
Chicago/Turabian StyleMuhammad Usman; Muhammad Uzair Qamar; Rike Becker; Muhammad Zaman; Christopher Conrad; Shoaib Salim. 2019. "Numerical modelling and remote sensing based approaches for investigating groundwater dynamics under changing land-use and climate in the agricultural region of Pakistan." Journal of Hydrology 581, no. : 124408.
The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other. Biomassebewertung von landwirtschaftlichen Kulturen mit multitemporalen Methoden basierend auf dual-polarimetrischen TerraSAR-X-Daten. Die Studie zielt auf die Bestimmung von Biomasse dreier landwirtschaftlicher Kulturen, Winterweizen (Triticum aestivum L.), Gerste (Hordeum vulgare L.) und Raps (Brassica napus L.) mit multitemporalen dual-polarimetrischen TerraSAR-X-Daten. Der Radarrückstreuungskoeffizient Sigma Null der beiden Polarisationskanäle HH und VV wurde aus den Satellitenbildern extrahiert. Anschließend wurden Kombinationen von HH- und VV-Polarisationen berechnet (z. B. HH/VV, HH + VV, HH × VV), um Beziehungen zwischen den SAR-Daten und der frischen und der trockenen Biomasse jeder Kulturart unter Verwendung einer multiplen schrittweisen Regression zu bestimmen. Zusätzlich wurde das semi-empirische Water Cloud Model (WCM) verwendet, um die Wirkung von Pflanzenbiomasse auf Radarrückstreudaten abzuschätzen. Das Potenzial des maschinellen Lernens mit Random Forest (RF) wurde ebenfalls untersucht. Das Verfahren der geteilten Stichprobe (split sampling, 70% Training und 30% Test) wurde durchgeführt, um die schrittweisen Regressionen, WCM und RF zu validieren. Das multiple schrittweise Regressionsverfahren unter Verwendung von dual-polarimetrischen Daten war in der Lage, die Biomasse der drei Kulturen, insbesondere für trockene Biomasse mit R² > 0,7, ohne weitere externe Eingangsgrößen wie beispielsweise Informationen über die (tatsächliche) Bodenfeuchte zu erfassen. Ein Vergleich der Random Forest (RF) Methode mit dem WCM zeigt, dass die RF Methode das WCM bei der Biomassenabschätzung deutlich übertroffen hat, insbesondere für frische Biomasse. Beispielsweise ergab die RF Methode ein R² > 0,68 für die Schätzung der frischen Biomasse verschiedener Kulturarten, während das WCM nur ein R² > 0,35 zeigte. Andererseits ähnelten sich die Ergebnisse beider Ansätze im Fall der trockenen Biomasse.
Nima Ahmadian; Tobias Ullmann; Jochem Verrelst; Erik Borg; Reinhard Zölitz; Christopher Conrad. Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 2019, 87, 159 -175.
AMA StyleNima Ahmadian, Tobias Ullmann, Jochem Verrelst, Erik Borg, Reinhard Zölitz, Christopher Conrad. Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2019; 87 (4):159-175.
Chicago/Turabian StyleNima Ahmadian; Tobias Ullmann; Jochem Verrelst; Erik Borg; Reinhard Zölitz; Christopher Conrad. 2019. "Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 87, no. 4: 159-175.
The occurrence of species in rapidly changing environments, such as agricultural landscapes, is affected by their ability to recolonise habitats. Knowledge of the landscape scale affecting colonisation is essential for large‐scale pest management. Colonisation by insects can be affected on multiple landscape scales, as different morphs of a species may have specific dispersal abilities. The cabbage whitefly, Aleyrodes proletella (L.) (Hemiptera: Aleyrodidae), a major pest of Brassica vegetables, is known to colonise Brassica vegetables primarily from fields of oilseed rape, Brassica napus L. (Brassicaceae). We used field mapping and remote sensing to characterise the relevant scales for colonisation of Brussels sprouts by cabbage whiteflies. Surprisingly, oilseed rape fields in wide landscapes (2–8 km around study sites) explained colonisation better than oilseed rape areas in local landscapes (200–1 000 m around study sites). The explained variance increased when additional weight was given to upwind source habitats, indicating wind transport of whitefly colonisers. Low importance of local compared to wide landscape source habitats can be explained by the flight behaviour of whitefly morphs. Migratory morphs show phototactic attraction but are attracted by hosts only during the later phases of flight. Therefore, they ignore host plants close to their origin and disperse several kilometres. Trivial flight morphs rarely move more than a few hundred metres. In conclusion, as most whitefly colonisers reached Brassica vegetables from source habitats at a distance of 2–8 km, predictions on pest pressure and landscape‐scale whitefly management should consider these distances. In contrast, oilseed rape fields in the local landscape, which usually worry farmers, had little effect.
Martin Ludwig; Hella Ludwig; Christopher Conrad; Thorsten Dahms; Rainer Meyhöfer. Cabbage whiteflies colonise Brassica vegetables primarily from distant, upwind source habitats. Entomologia Experimentalis et Applicata 2019, 167, 713 -721.
AMA StyleMartin Ludwig, Hella Ludwig, Christopher Conrad, Thorsten Dahms, Rainer Meyhöfer. Cabbage whiteflies colonise Brassica vegetables primarily from distant, upwind source habitats. Entomologia Experimentalis et Applicata. 2019; 167 (8):713-721.
Chicago/Turabian StyleMartin Ludwig; Hella Ludwig; Christopher Conrad; Thorsten Dahms; Rainer Meyhöfer. 2019. "Cabbage whiteflies colonise Brassica vegetables primarily from distant, upwind source habitats." Entomologia Experimentalis et Applicata 167, no. 8: 713-721.
Aiym Orynbaikyzy; Ursula Gessner; Christopher Conrad. Crop type classification using a combination of optical and radar remote sensing data: a review. International Journal of Remote Sensing 2019, 40, 6553 -6595.
AMA StyleAiym Orynbaikyzy, Ursula Gessner, Christopher Conrad. Crop type classification using a combination of optical and radar remote sensing data: a review. International Journal of Remote Sensing. 2019; 40 (17):6553-6595.
Chicago/Turabian StyleAiym Orynbaikyzy; Ursula Gessner; Christopher Conrad. 2019. "Crop type classification using a combination of optical and radar remote sensing data: a review." International Journal of Remote Sensing 40, no. 17: 6553-6595.
The Northeast German Lowland Observatory (TERENO-NE) was established to investigate the regional impact of climate and land use change. TERENO-NE focuses on the Northeast German lowlands, for which a high vulnerability has been determined due to increasing temperatures and decreasing amounts of precipitation projected for the coming decades. To facilitate in-depth evaluations of the effects of climate and land use changes and to separate the effects of natural and anthropogenic drivers in the region, six sites were chosen for comprehensive monitoring. In addition, at selected sites, geoarchives were used to substantially extend the instrumental records back in time. It is this combination of diverse disciplines working across different time scales that makes the observatory TERENO-NE a unique observation platform. We provide information about the general characteristics of the observatory and its six monitoring sites and present examples of interdisciplinary research activities at some of these sites. We also illustrate how monitoring improves process understanding, how remote sensing techniques are fine-tuned by the most comprehensive ground-truthing site DEMMIN, how soil erosion dynamics have evolved, how greenhouse gas monitoring of rewetted peatlands can reveal unexpected mechanisms, and how proxy data provides a long-term perspective of current ongoing changes. Copyright © 2018. . Copyright © by the Soil Science Society of America, Inc.
Ingo Heinrich; Daniel Balanzategui; Oliver Bens; Gerald Blasch; Theresa Blume; Falk Böttcher; Erik Borg; Brian Brademann; Achim Brauer; Christopher Conrad; Elisabeth Dietze; Nadine Dräger; Peter Fiener; Horst H. Gerke; Andreas Güntner; Iris Heine; Gerhard Helle; Marcus Herbrich; Katharina Harfenmeister; Karl-Uwe Heußner; Christian Hohmann; Sibylle Itzerott; Gerald Jurasinski; Knut Kaiser; Christoph Kappler; Franziska Koebsch; Susanne Liebner; Gunnar Lischeid; Bruno Merz; Klaus Dieter Missling; Markus Morgner; Sylvia Pinkerneil; Birgit Plessen; Thomas Raab; Thomas Ruhtz; Torsten Sachs; Michael Sommer; Daniel Spengler; Vivien Stender; Peter Stüve; Florian Wilken. Interdisciplinary Geo-ecological Research across Time Scales in the Northeast German Lowland Observatory (TERENO-NE). Vadose Zone Journal 2018, 17, 180116 .
AMA StyleIngo Heinrich, Daniel Balanzategui, Oliver Bens, Gerald Blasch, Theresa Blume, Falk Böttcher, Erik Borg, Brian Brademann, Achim Brauer, Christopher Conrad, Elisabeth Dietze, Nadine Dräger, Peter Fiener, Horst H. Gerke, Andreas Güntner, Iris Heine, Gerhard Helle, Marcus Herbrich, Katharina Harfenmeister, Karl-Uwe Heußner, Christian Hohmann, Sibylle Itzerott, Gerald Jurasinski, Knut Kaiser, Christoph Kappler, Franziska Koebsch, Susanne Liebner, Gunnar Lischeid, Bruno Merz, Klaus Dieter Missling, Markus Morgner, Sylvia Pinkerneil, Birgit Plessen, Thomas Raab, Thomas Ruhtz, Torsten Sachs, Michael Sommer, Daniel Spengler, Vivien Stender, Peter Stüve, Florian Wilken. Interdisciplinary Geo-ecological Research across Time Scales in the Northeast German Lowland Observatory (TERENO-NE). Vadose Zone Journal. 2018; 17 (1):180116.
Chicago/Turabian StyleIngo Heinrich; Daniel Balanzategui; Oliver Bens; Gerald Blasch; Theresa Blume; Falk Böttcher; Erik Borg; Brian Brademann; Achim Brauer; Christopher Conrad; Elisabeth Dietze; Nadine Dräger; Peter Fiener; Horst H. Gerke; Andreas Güntner; Iris Heine; Gerhard Helle; Marcus Herbrich; Katharina Harfenmeister; Karl-Uwe Heußner; Christian Hohmann; Sibylle Itzerott; Gerald Jurasinski; Knut Kaiser; Christoph Kappler; Franziska Koebsch; Susanne Liebner; Gunnar Lischeid; Bruno Merz; Klaus Dieter Missling; Markus Morgner; Sylvia Pinkerneil; Birgit Plessen; Thomas Raab; Thomas Ruhtz; Torsten Sachs; Michael Sommer; Daniel Spengler; Vivien Stender; Peter Stüve; Florian Wilken. 2018. "Interdisciplinary Geo-ecological Research across Time Scales in the Northeast German Lowland Observatory (TERENO-NE)." Vadose Zone Journal 17, no. 1: 180116.
Accurate information of soil salinity levels enables for remediation actions in long-term operating irrigation systems with malfunctioning drainage and shallow groundwater (GW), as they are widespread throughout the Aral Sea Basin (ASB). Multi-temporal Landsat 5 data combined with GW levels and potentials, elevation and relative topographic position, and soil (clay content) parameters, were used for modelling bulk electromagnetic induction (EMI) at the end of the irrigation season. Random forest (RF) regressionwas applied to predict in situ observations of 2008–2011 which originated from a cotton research station in Uzbekistan. Validation, i.e. median statistics from 100 RF runs with a holdout of each 20% of the samples, revealed that mono-temporal (R2: 0.1–0.18, RMSE: 16.7–24.9 mSm−1) underperformed multi-temporal RS data (R2: 0.29–0.45; RMSE: 15.1–20.9 mSm−1). Combinations of multi-temporal RS data with environmental parameters achieved highest accuracies (R2: 0.36–0.50, RMSE: 13.2–19.9 mSm−1). Beside RS data recorded at the initial peaks of the major irrigation phases, terrain and GW parameters turned out to be important variables for the model. RF preferred neither raw data nor spectral indices known to be suitable for detecting soil salinity. Unexplained variance components result from missing environmental variables, but also from processes not considered in the data. A calibration of the EMI for electrical conductivity and the standard soil salinity classification returned an overall accuracy of 76–83% for the period 2008–2011. The presented indirect approach together with the in situ calibration of the EMI data can support an accurate mapping of soil salinity at the end of the season, at least in the type of irrigation systems found in the ASB.
Murodjon Sultanov; Mirzakhayot Ibrakhimov; Akmal Akramkhanov; Christian Bauer; Christopher Conrad. Modelling End-of-Season Soil Salinity in Irrigated Agriculture Through Multi-temporal Optical Remote Sensing, Environmental Parameters, and In Situ Information. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 2018, 86, 221 -233.
AMA StyleMurodjon Sultanov, Mirzakhayot Ibrakhimov, Akmal Akramkhanov, Christian Bauer, Christopher Conrad. Modelling End-of-Season Soil Salinity in Irrigated Agriculture Through Multi-temporal Optical Remote Sensing, Environmental Parameters, and In Situ Information. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2018; 86 (5-6):221-233.
Chicago/Turabian StyleMurodjon Sultanov; Mirzakhayot Ibrakhimov; Akmal Akramkhanov; Christian Bauer; Christopher Conrad. 2018. "Modelling End-of-Season Soil Salinity in Irrigated Agriculture Through Multi-temporal Optical Remote Sensing, Environmental Parameters, and In Situ Information." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 86, no. 5-6: 221-233.
Global warming is predicted to increase water scarcity in many drylands worldwide. In Central Asia, one of the most intensively irrigated dryland agricultural regions, climate change is likely to exacerbate the regional water supply–demand gaps, particularly in downstream areas. The withdrawal of degraded, highly salinized croplands from irrigated farming in favor of tree plantations that effectively utilize saline groundwater may contribute to irrigation water saving, which can generate valuable ecosystem services and provide rural income opportunities. To facilitate the spatial planning of afforestation in the lower Amudarya region, we developed a hydrological algorithm to map the spatio-temporal pattern of water supply–demand. The resulting map, based on seven-year continuous data of cropping pattern and corresponding irrigation dynamics, rainfall, and evapotranspiration at 250 m resolution, revealed the overly irrigated areas from which excess water can be redistributed to water-stressed areas. Furthermore, combining this information with spatial data on marginally productive croplands and with water requirement of tree plantations showed that 67% of these croplands are characterized by water availability sufficient for the introduction of salt-tolerant tree species. The algorithm developed is of potential use for defining the feasibility of introducing alternative (tree) crops with known growth and water use characteristics.
Navneet Kumar; Asia Khamzina; Bernhard Tischbein; Patrick Knöfel; Christopher Conrad; John P.A. Lamers. Spatio-temporal supply–demand of surface water for agroforestry planning in saline landscape of the lower Amudarya Basin. Journal of Arid Environments 2018, 162, 53 -61.
AMA StyleNavneet Kumar, Asia Khamzina, Bernhard Tischbein, Patrick Knöfel, Christopher Conrad, John P.A. Lamers. Spatio-temporal supply–demand of surface water for agroforestry planning in saline landscape of the lower Amudarya Basin. Journal of Arid Environments. 2018; 162 ():53-61.
Chicago/Turabian StyleNavneet Kumar; Asia Khamzina; Bernhard Tischbein; Patrick Knöfel; Christopher Conrad; John P.A. Lamers. 2018. "Spatio-temporal supply–demand of surface water for agroforestry planning in saline landscape of the lower Amudarya Basin." Journal of Arid Environments 162, no. : 53-61.
Reduction of natural vegetation cover in the savannah of West Africa constitutes a pressing environmental concern that may lead to soil degradation. With the aim to assess the degradation of natural vegetation in the savannah of Burkina Faso, this study combined NDVI trends and fractional Land Use/Cover Change (LULCC). Fractional LULCC maps, derived from the aggregation of a 30 m Landsat LULCC map (1999–2011) to 250 m resolution of MODIS, were used to assess natural vegetation conversions in the small-scale spatial patterns of savannah landscapes. Mann-Kendall's monotonic trend test was applied to 250 m MODIS NDVI time series (2000–2011) to assess modifications of natural vegetation cover. Finally, the Spearman's correlation was employed to determine the relationship of natural vegetation degradation with environmental factors. The study revealed a vast conversion of natural vegetation into agriculture (15.9%) and non-vegetated area (1.8%) between 1999 and 2011. Significant decreasing NDVI trends (p < .05) indicated negative modifications of natural vegetation (2000–2011 period) occurring along the protected areas borders and in fragmented landscapes characterized by disruption of continuity in natural vegetation. Spearman's correlation showed that accessibility, climatic and topographic conditions favored natural vegetation degradation. The results can enable the development of efficient land degradation policies.
Benewindé Jean-Bosco Zoungrana; Christopher Conrad; Michael Thiel; Leonard K. Amekudzi; Evariste Dapola Da. MODIS NDVI trends and fractional land cover change for improved assessments of vegetation degradation in Burkina Faso, West Africa. Journal of Arid Environments 2018, 153, 66 -75.
AMA StyleBenewindé Jean-Bosco Zoungrana, Christopher Conrad, Michael Thiel, Leonard K. Amekudzi, Evariste Dapola Da. MODIS NDVI trends and fractional land cover change for improved assessments of vegetation degradation in Burkina Faso, West Africa. Journal of Arid Environments. 2018; 153 ():66-75.
Chicago/Turabian StyleBenewindé Jean-Bosco Zoungrana; Christopher Conrad; Michael Thiel; Leonard K. Amekudzi; Evariste Dapola Da. 2018. "MODIS NDVI trends and fractional land cover change for improved assessments of vegetation degradation in Burkina Faso, West Africa." Journal of Arid Environments 153, no. : 66-75.
In recent years, extensive competition for groundwater use among different consumers has exploited major freshwater aquifers in Pakistan. There is an urgent need for appraisal of this precious resource followed by some mitigation strategies. This modelling study was conducted in the mixed cropping zone of the Punjab, Pakistan. Both remote sensing and secondary data were utilized to achieve objectives of this study. The data related to piezometric water levels, canal gauges, well logs, meteorological and lithological information were collected from Punjab Irrigation Department (PID), Water and Power Development Authority (WAPDA). Groundwater flow models for both steady and transient conditions were set-up using FEFLOW-3D. Water balance components and recharge were estimated using empirical relations and inverse modelling approaches. Both manual and automated approaches were utilized to calibrate the models. Moreover, sensitivity analysis was performed to see the response of model output against different model input parameters. Followed by calibration and validation, the model was run for different management scenarios, including lining of canal sections, minimization of field percolation, and change of groundwater abstraction. The study results show a drop in groundwater levels for almost all scenarios. The highest negative change was observed for the 4th scenario (i.e. 25% increase in groundwater pumping over a 10-year period), with a value of 3.73 m, by ignoring very wet summer and winter seasons. For normal weather conditions, the highest negative change was observed for the 4th scenario with a value of 2.91 m followed by 2.68 m for the 3rd scenario (i.e. 50% reduction in canal seepage and field percolation over a 10-year period). For very wet summer and winter seasons, only one positive change was observed, for the 5th scenario (i.e. 25% decrease in groundwater pumping during 10 years period), with a value of 1.17 m. The changes for all other scenarios were negative. The mitigation strategy may include less groundwater pumping, by supporting cultivation of low delta crops and adjusting cropping patterns considering canal water supplies. It is further suggested to support current modelling results by incorporating more detailed information on cropping and by exploring the effect of climate change.
Muhammad Usman; Rudolf Liedl; Muhammad Arshad; Christopher Conrad. 3-D numerical modelling of groundwater flow for scenario-based analysis and management. Water SA 2018, 44, 146-154 .
AMA StyleMuhammad Usman, Rudolf Liedl, Muhammad Arshad, Christopher Conrad. 3-D numerical modelling of groundwater flow for scenario-based analysis and management. Water SA. 2018; 44 (2):146-154.
Chicago/Turabian StyleMuhammad Usman; Rudolf Liedl; Muhammad Arshad; Christopher Conrad. 2018. "3-D numerical modelling of groundwater flow for scenario-based analysis and management." Water SA 44, no. 2: 146-154.