35 results on '"Miriam Machwitz"'
Search Results
2. Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging
- Author
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Asmaa Abdelbaki, Martin Schlerf, Rebecca Retzlaff, Miriam Machwitz, Jochem Verrelst, and Thomas Udelhoven
- Subjects
LUT-based inversion ,hybrid method ,statistical method ,leaf area index ,fractional vegetation cover ,canopy chlorophyll content ,Science - Abstract
Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.
- Published
- 2021
- Full Text
- View/download PDF
3. Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing
- Author
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Miriam Machwitz, Martin Schlerf, Frédéric Mayer, Franz Ronellenfitsch, Christian Bossung, Philippe Delfosse, Thomas Udelhoven, and Lucien Hoffmann
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agriculture ,bioenergy ,biomethane potential ,hyperspectral remote sensing ,Science - Abstract
Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMPFM, Nm3 biogas/t fresh matter (Nm3/t FM)) and BMP per area (BMParea, Nm3 biogas/ha (Nm3/ha)). We found that BMPFM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (Rcv2 = 0.82, n = 35), especially at levels >190 Nm3/t. In the in situ approach PLSR prediction quality declined (Rcv2 = 0.50, n = 20). BMParea, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR.
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- 2013
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4. Pronounced Seasonal Changes in the Movement Ecology of a Highly Gregarious Central-Place Forager, the African Straw-Coloured Fruit Bat (Eidolon helvum).
- Author
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Jakob Fahr, Michael Abedi-Lartey, Thomas Esch, Miriam Machwitz, Richard Suu-Ire, Martin Wikelski, and Dina K N Dechmann
- Subjects
Medicine ,Science - Abstract
Straw-coloured fruit bats (Eidolon helvum) migrate over vast distances across the African continent, probably following seasonal bursts of resource availability. This causes enormous fluctuations in population size, which in turn may influence the bats' impact on local ecosystems. We studied the movement ecology of this central-place forager with state-of-the-art GPS/acceleration loggers and concurrently monitored the seasonal fluctuation of the colony in Accra, Ghana. Habitat use on the landscape scale was assessed with remote sensing data as well as ground-truthing of foraging areas.During the wet season population low (~ 4000 individuals), bats foraged locally (3.5-36.7 km) in urban areas with low tree cover. Major food sources during this period were fruits of introduced trees. Foraging distances almost tripled (24.1-87.9 km) during the dry season population peak (~ 150,000 individuals), but this was not compensated for by reduced resting periods. Dry season foraging areas were random with regard to urban footprint and tree cover, and food consisted almost exclusively of nectar and pollen of native trees.Our study suggests that straw-coloured fruit bats disperse seeds in the range of hundreds of meters up to dozens of kilometres, and pollinate trees for up to 88 km. Straw-coloured fruit bats forage over much larger distances compared to most other Old World fruit bats, thus providing vital ecosystem services across extensive landscapes. We recommend increased efforts aimed at maintaining E. helvum populations throughout Africa since their keystone role in various ecosystems is likely to increase due to the escalating loss of other seed dispersers as well as continued urbanization and habitat fragmentation.
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- 2015
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5. CropGIS - A web application for the spatial and temporal visualization of past, present and future crop biomass development.
- Author
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Miriam Machwitz, Erik Haß, Jürgen Junk, Thomas Udelhoven, and Martin Schlerf
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- 2019
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6. Forest vs. grassland drought response inferred from eddy covariance and Earth observations
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Anne Hoek van Dijke, René Orth, Adriaan Teuling, Martin Herold, Martin Schlerf, Mirco Migliavacca, Miriam Machwitz, Tessa van Hateren, Xin Yu, and Kaniska Mallick
- Abstract
Temperate forests and grasslands have different drought response strategies. Trees often control their stomata to reduce water loss in order to prevent hydraulic failure and ensure the survival of their aboveground biomass. In contrast, grasses generally have a less strong stomatal control and maintain high photosynthesis and transpiration until the soil moisture gets depleted. That is when their leaves wilt and the grasslands see a reduction in their aboveground green biomass. Both the increased stomatal control and the reduction in aboveground biomass decrease the surface conductance, i.e. decrease the exchange of water and carbon between the leaves and the atmosphere. Therefore, the drought response of vegetation has major impacts on the land-atmosphere fluxes of water, energy, and carbon, as well as the development of droughts and heat waves.Here, we study to which extent the different drought responses of forests and grasslands are reflected in remote sensing data. We hypothesise that (i) for both forests and grasslands, there are drought-induced changes in thermal infrared based data (e.g., land surface temperature), because of the decreased surface conductance for both land cover types. Furthermore, we hypothesise that (ii) drought-induced changes in optical based indices (e.g. the normalized difference vegetation index) can be detected for grasslands but not for forests, because of the different drought response strategies of trees and grasses. In this study we jointly analyze site-scale and remote sensing data. We use eddy-covariance data for 52 forest sites and 11 grassland sites across the northern hemisphere to calculate the surface conductance, and we identify droughts from low soil moisture content and reduced surface conductance. Then we analyse how the drought response is reflected in thermal and optical indices derived from MODIS satellite data.The results show that our hypotheses are largely confirmed. The land surface temperature increases with drought-induced reductions in surface conductance for both forests and grasslands. By contrast, the optical indices show a much stronger response for grasslands than for forests. We conclude that the different canopy-level drought response strategies of trees and grasses are reflected in remote sensing data. Our study highlights that the joint investigation of multiple remote sensing data streams enables insights beyond the analyses of individual indices, such as a better understanding of the drought response strategies across land cover types. Further, a host of different satellite data should be used to monitor and study vegetation drought responses of forests and grasslands to ensure accurate inference on the implications on water, energy, and carbon fluxes.
- Published
- 2023
7. Estimation of canopy nitrogen content in winter wheat from Sentinel-2 images for operational agricultural monitoring
- Author
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Christian Bossung, Martin Schlerf, and Miriam Machwitz
- Subjects
General Agricultural and Biological Sciences - Abstract
Canopy nitrogen content (CNC, kg/ha) provides crucial information for site-specific crop fertilization and the usability of Sentinel-2 (S2) satellite data for CNC monitoring at high fertilization levels in managed agricultural fields is still underexplored. Winter wheat samples were collected in France and Belgium in 2017 (n = 126) and 2018 (n = 18), analysed for CNC and S2-spectra were extracted at the sample locations. A comparison of three established remote sensing methods to retrieve CNC was carried out: (1) look-up-table (LUT) inversion of the canopy reflectance model PROSAIL, (2) Partial Least Square Regression (PLSR) and (3) nitrogen-sensitive vegetation indices (VI). The spatial and temporal model transferability to new data was rigorously assessed. The PROSAIL-LUT approach predicted CNC with a root mean squared error of 33.9 kg/ha on the 2017 dataset and a slightly larger value of 36.8 kg/ha on the 2018 dataset. Contrary, PLSR showed an error of 27.9 kg N/ha (R2 = 0.52) in the calibration dataset (2017) but a substantially larger error of 38.4 kg N/ha on the independent dataset (2018). VIs revealed calibration errors were slightly larger than the PLSR results but showed much higher validation errors for the independent dataset (> 50 kg/ha). The PROSAIL inversion was more stable and robust than the PLSR and VI methods when applied to new data. The obtained CNC maps may support farmers in adapting their fertilization management according to the actual crop nitrogen status.
- Published
- 2022
8. Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information.
- Author
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Miriam Machwitz, Ursula Gessner, Christopher Conrad, Ulrike Falk, Jochen Richters, and Stefan W. Dech
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- 2015
- Full Text
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9. Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging
- Author
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Miriam Machwitz, Rebecca Retzlaff, Asmaa Abdelbaki, Jochem Verrelst, Thomas Udelhoven, and Martin Schlerf
- Subjects
Canopy ,statistical method ,010504 meteorology & atmospheric sciences ,Science ,0211 other engineering and technologies ,Growing season ,02 engineering and technology ,LUT-based inversion ,hybrid method ,leaf area index ,fractional vegetation cover ,canopy chlorophyll content ,01 natural sciences ,Leaf area index ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Mathematics ,Remote sensing ,Hyperspectral imaging ,Statistical model ,Random forest ,VNIR ,General Earth and Planetary Sciences ,Scale (map) - Abstract
Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.
- Published
- 2022
10. Enhanced biomass prediction by assimilating satellite data into a crop growth model.
- Author
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Miriam Machwitz, Laura Giustarini, Christian Bossung, David Frantz, Martin Schlerf, Holger Lilienthal, Loise Wandera, Patrick Matgen, Lucien Hoffmann, and Thomas Udelhoven
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- 2014
- Full Text
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11. Land Cover Analysis on Sub-Continental Scale: FAO LCCS Standard with 250 Meter MODIS Satellite Observations in West Africa.
- Author
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Miriam Machwitz, Tobias Landmann, Christopher Conrad, Anna Cord, and Stefan W. Dech
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- 2008
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12. A Land Cover Change Synthesis Study for the GLOWA Volta Basin in West Africa using Time Trajectory Satellite Observations and Cellular Automata Models.
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Tobias Landmann, Miriam Machwitz, Quang Bao Le, Lulseged T. Desta, Paul L. G. Vlek, Stefan W. Dech, and Michael Schmidt 0003
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- 2008
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13. Impacts of socio-economic development and urbanization on natural resources - case studies from Africa.
- Author
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Ursula Gessner, Kim Knauer, Miriam Machwitz, Stefan W. Dech, and Claudia Kuenzer
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- 2016
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14. Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing.
- Author
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Thomas Udelhoven, Philippe Delfosse, Christian Bossung, Franz Ronellenfitsch, Frédéric Mayer, Martin Schlerf, Miriam Machwitz, and Lucien Hoffmann
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- 2013
- Full Text
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15. Bridging the Gap Between Remote Sensing and Plant Phenotyping—Challenges and Opportunities for the Next Generation of Sustainable Agriculture
- Author
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Uwe Rascher, Frédéric Baret, Helge Aasen, Sven Fahrner, Roland Pieruschka, Katja Berger, Miriam Machwitz, Martin Schlerf, José A. Jiménez-Berni, European Cooperation in Science and Technology, European Commission, German Centre for Air and Space Travel, and Federal Ministry of Economics and Technology (Germany)
- Subjects
open-data standards ,0106 biological sciences ,Opinion ,Bridging (networking) ,Multi-sensor synergies ,Plant Science ,01 natural sciences ,SB1-1110 ,Smart farming ,smart farming ,remote sensing ,03 medical and health sciences ,radiative transfer models (RTM) ,vegetation traits ,ddc:570 ,Sustainable agriculture ,unmanned aerial vehicles (UAVs) ,high-throughput field phenotyping ,030304 developmental biology ,Unmanned aerial vehicles (UAVs) ,2. Zero hunger ,0303 health sciences ,multi-sensor synergies ,business.industry ,Environmental resource management ,Plant culture ,Remote sensing ,15. Life on land ,Plant phenotyping ,Radiative transfer models (RTM) ,Vegetation traits ,Open-data standards ,Remote sensing (archaeology) ,High-throughput field phenotyping ,Environmental science ,business ,010606 plant biology & botany - Abstract
This article is based upon work from COST Action CA17134 Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), supported by COST. It was supported by European Plant Phenotyping Network (EPPN2020: Grant Agreement 731013), from EMPHASIS-PREP (Grant Agreement: 739514), from EOSC-Life (Grant Agreement: 824087). KB was funded within the EnMAP scientific preparation program (DLR Space and BMWi, Grant Number 50EE1923).
- Published
- 2021
16. CropGIS – A web application for the spatial and temporal visualization of past, present and future crop biomass development
- Author
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Jürgen Junk, Martin Schlerf, Erik Hass, Thomas Udelhoven, and Miriam Machwitz
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0106 biological sciences ,business.industry ,Biomass ,Forestry ,04 agricultural and veterinary sciences ,Horticulture ,01 natural sciences ,Computer Science Applications ,Visualization ,Agriculture ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Web application ,Environmental science ,Satellite imagery ,Agricultural productivity ,business ,Agronomy and Crop Science ,Spatial analysis ,Variable Rate Application ,010606 plant biology & botany ,Remote sensing - Abstract
Spatial information on crop status and development is required by agricultural managers for a site specific and adapted management. Here, a prototype of a web application is presented for the visualization of biomass production of maize (Zea mays). The web application displays past biomass development and future predictions for user-defined regions of interest along with summary statistics. Biomass is modelled using the crop growth model (CGM) APSIM (Agricultural Production Systems Simulator) using meteorological data from 2001 to 2014. Information on current crop status and subfield heterogeneity is assimilated into APSIM through high-resolution optical satellite imagery. The use of recent satellite data and regional, historical meteorological data increases the reliability of the biomass information provided. Through its unique combination of high-resolution satellite imagery together with mechanistic crop growth modelling, this web application can overcome the often sparse temporal or sparse spatial resolution of biomass information, which is based on remote sensing images or on crop growth modelling alone. The prototype presented, with its high resolution biomass maps, can be the basis for variable rate application as farmers can react site-specifically to plant development.
- Published
- 2019
17. The ‘global tree restoration potential’: a first estimation of the hydrological effects
- Author
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Imme Benedict, Adriaan J. Teuling, Miriam Machwitz, Martin Herold, Martin Schlerf, Kaniska Mallick, and Anne J. Hoek van Dijke
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Estimation ,Tree (data structure) ,Statistics ,Environmental science - Abstract
Vegetation plays an important role in the exchange of water between the land surface and the atmosphere through evaporation and redistribution of water. Hence, changes in vegetation cover alter the terrestrial hydrological cycle. Large-scale forest restoration is an effective climate change mitigation strategy through carbon sequestration and is expected to impact the water availability. A better understanding of the impact of reforestation is needed, given the numerous different reforestation missions.Our study aims to provide an estimation of the hydrological effects of 900 million hectares of reforestation, called the ‘global tree restoration potential’ (Bastin et al., 2019). We include the effects of forest planting on evaporation and moisture recycling, where evaporation effects local water availability, and moisture recycling effects both local and remote water availability. We used the conventional Budyko’s moisture index framework to calculate the effects of reforestation on evaporation, and afterwards we used the UTrack dataset to calculate the changes in precipitation. The UTrack dataset presents the monthly climatological mean atmospheric moisture flows from evaporation to precipitation and is created using the Lagrangian moisture tracking model UTrack (Tuinenburg et al., 2020).The results show that reforesting the ‘global tree restoration potential’ would effect water availability for most of the Earth’s surface. The global mean increase in terrestrial evaporation is 8 mm yr-1. The increase in evaporation is highest around the equator (on average 20 mm yr-1), with local maximum changes of up to 200 mm yr-1. This is related to a relatively high restoration potential in low latitude areas, and a generally large evaporation response in high precipitation regions. Enhanced moisture recycling has the potential to partly compensate for this decreased water availability by increasing the downwind precipitation. Bastin, J.-F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C.M., Crowther, T.W. The global tree restoration potential. Science, 365, 76-79, http://doi.org/10.1126/science.aax0848, 2019.Tuinenburg, O. A., Theeuwen, J. J. E., and Staal, A.: High-resolution global atmospheric moisture connections from evaporation to precipitation, Earth Syst. Sci. Data, 12, 3177–3188, https://doi.org/10.5194/essd-12-3177-2020, 2020.
- Published
- 2021
18. An improved life cycle impact assessment principle for assessing the impact of land use on ecosystem services
- Author
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Benedetto Rugani, Marco Beyer, Reinout Heijungs, Benoit Othoniel, Miriam Machwitz, Pim Post, Econometrics and Operations Research, Tinbergen Institute, and Theoretical and Computational Ecology (IBED, FNWI)
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Environmental Engineering ,010504 meteorology & atmospheric sciences ,Land cover ,010501 environmental sciences ,01 natural sciences ,Ecosystem services ,Life cycle assessment ,Integrated modeling ,Order (exchange) ,Environmental Chemistry ,Ecosystem ,Characterization factor ,Waste Management and Disposal ,Life-cycle assessment ,0105 earth and related environmental sciences ,Land use ,Impact assessment ,business.industry ,Environmental resource management ,Pollution ,System dynamics ,Environmental science ,business ,SDG 12 - Responsible Consumption and Production - Abstract
In order to consider the effects of land use, and the land cover changes it causes, on ecosystem services in life cycle assessment (LCA), a new methodology is proposed and applied to calculate midpoint and endpoint characterization factors. To do this, a cause-effect chain was established in line with conceptual models of ecosystem services to describe the impacts of land use and related land cover changes. A high-resolution, spatially explicit and temporally dynamic modeling framework that integrates land use and ecosystem services models was developed and used as an impact characterization model to simulate that cause-effect chain. Characterization factors (CFs) were calculated and regionalized at the scales of Luxembourg and its municipalities, taken as a case to show the advantages of the modeling approach. More specifically, the calculated CFs enable the impact assessment of six land cover types on six ecosystem functions and two final ecosystem services. A mapping and comparison exercise of these CFs allowed us to identify spatial trade-offs and synergies between ecosystem services due to possible land cover changes. Ultimately, the proposed methodology can offer a solution to overcome a number of methodological limitations that still exist in the characterization of impacts on ecosystem services in LCA, implying a rethinking of the modeling of land use in life cycle inventory.
- Published
- 2019
19. Does NDVI explain spatial and temporal variability in sap velocity in temperate forest ecosystems?
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Theresa Blume, Miriam Machwitz, Anne J. Hoek van Dijke, Sibylle Hassler, Kaniska Mallick, Adriaan J. Teuling, Martin Schlerf, and Martin Herold
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Phenology ,Evapotranspiration ,Temperate forest ,Environmental science ,Ecosystem ,Enhanced vegetation index ,Temperate deciduous forest ,Atmospheric sciences ,Normalized Difference Vegetation Index ,Transpiration - Abstract
There is a need for a better understanding of the link between vegetation characteristics and tree transpiration to facilitate satellite derived transpiration estimation. Many studies use the normalized difference vegetation index (NDVI), a proxy for tree biophysical characteristics, to estimate evapotranspiration. In this study we investigated the link between sap velocity and 30 m resolution Landsat derived NDVI for twenty days during two contrasting precipitation years in a temperate deciduous forest catchment. Sap velocity was measured in the Attert catchment in Luxembourg in 25 plots of 20 × 20 m covering three geologies with sensors installed in 2–4 trees per plot. The results show that sap velocity and NDVI were significantly positively correlated in April, i.e., NDVI successfully captured the pattern of sap velocity during the phase of green-up. After green-up, a significant negative correlation was found during half of the studied days. During a dry period, sap velocity was uncorrelated to NDVI, but influenced by geology and aspect. In summary, in our study area, the correlation between sap velocity and NDVI was not constant, but varied with phenology and water availability. The same behaviour was found for the Enhanced Vegetation Index (EVI). This suggests that methods using NDVI or EVI to predict small-scale variability in (evapo)transpiration should be carefully applied and that NDVI and EVI cannot be used to scale sap velocity to stand level transpiration in temperate forest ecosystems.
- Published
- 2018
20. Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information
- Author
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Ursula Gessner, Miriam Machwitz, Stefan Dech, Jochen Richters, Christopher Conrad, and Ulrike Falk
- Subjects
Global and Planetary Change ,Meteorology ,Eddy covariance ,Climate change ,Vegetation ,Land cover ,Management, Monitoring, Policy and Law ,Atmospheric sciences ,Normalized Difference Vegetation Index ,Geography ,Photosynthetically active radiation ,West Africa ,FPAR ,Light Use Efficiency ,Gross Primary Productivity ,Moderate-resolution imaging spectroradiometer ,Computers in Earth Sciences ,Regional Biomass Model ,Earth-Surface Processes ,Downscaling - Abstract
Global warming associated with climate change is one of the greatest challenges of today’s world. Increasing emissions of the greenhouse gas CO 2 are considered as a major contributing factor to global warming. One regulating factor of CO 2 exchange between atmosphere and land surface is vegetation. Measurements of land cover changes in combination with modelling the Gross Primary Productivity (GPP) can contribute to determine important sources and sinks of CO 2 . The aim of this study is to accurately model the GPP for a region in West Africa with a spatial resolution of 250 m, and the differentiation of GPP based on woody and herbaceous vegetation. For this purpose, the Regional Biomass Model (RBM) was applied, which is based on a Light Use Efficiency (LUE) approach. The focus was on the spatial enhancement of the RBM from the original 1000–250 m spatial resolution (RBM+). The adaptation to the 250 m scale included the modification of two main input parameters: (1) the fraction of absorbed Photosynthetically Active Radiation (FPAR) based on the 1000 m MODIS MOD15A2 FPAR product which was downscaled to 250 m using MODIS NDVI time series; (2) the fractional cover of woody and herbaceous vegetation, which was improved by using a multi-scale approach. For validation and regional adjustments of GPP and the input parameters, in situ data from a climate station and eddy covariance measurements were integrated. The results of this approach show that the input parameters could be improved significantly: downscaling considerably reduces data gaps of the original FPAR product and the improved dataset differed less than 5.0% from the original data for cloud free regions. The RMSE of the fractional vegetation cover varied between 5.1 and 12.7%. Modelled GPP showed a slight overestimation in comparison to eddy covariance measurements. The in situ data was exceeded by 8.8% for 2005 and by 2.0% for 2006. The model results were converted to NPP and also agreed well with previous NPP measurements reported from different studies. Altogether a high accuracy and suitability of the regionally adjusted and downscaled model RBM+ can be concluded. The differentiation between vegetation growth forms allows a separation of long-term and short-term carbon storage based on woody and herbaceous vegetation, respectively.
- Published
- 2015
21. Multi-sensor mapping of West African land cover using MODIS, ASAR and TanDEM-X/TerraSAR-X data
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Vahid Naeimi, Ursula Gessner, Miriam Machwitz, Claudia Kuenzer, Stefan Dech, Adina Tillack, and Thomas Esch
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Land cover ,Earth observation ,Time series ,Land use ,WaMaPro ,Land management ,Soil Science ,Geology ,GUF ,law.invention ,Random forest ,MODIS ,law ,Radar imaging ,West Africa ,Environmental science ,Moderate-resolution imaging spectroradiometer ,TanDEM-X ,Computers in Earth Sciences ,Radar ,ASAR ,TerraSAR-X ,Remote sensing - Abstract
Land cover information plays an elementary role for regional water and land management, and is an essential variable for the assessment of ecosystem services and regional climate impact. This paper describes the generation of a regionally optimized land cover dataset for West Africa with a spatial resolution of 250 m, which is based on earth observation data from three optical and radar instruments. The choice of sensors is based on their individual strengths and weaknesses in assessing specific land surface types. Annual profiles of the optical Moderate Resolution Imaging Spectroradiometer (MODIS) are analyzed for the classification of vegetated classes including agriculture. The classification approach builds on random forest classification with learning data extracted from higher resolution land cover maps. Envisat Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS) time series are used, in combination with MODIS data, to delineate permanent and seasonal water bodies. Here, an approach integrating threshold classification and morphological operations is applied. Built-up areas of different densities are identified based on a seamless coverage of radar imagery collected by the satellites TanDEM-X and TerraSAR-X. The detection of settlements is based on an unsupervised classification scheme which exploits texture metrics and backscattering amplitudes of the fine resolution radar sensors. The accuracy assessment of the multi-sensor land cover map yields an overall accuracy of 80% at legend level 1 (9 classes) and 73% at the more detailed legend level 2 (14 classes). Comparisons with available wall-to-wall datasets of the region demonstrate the valuable information content of the presented West African land cover map.
- Published
- 2015
22. Enhanced biomass prediction by assimilating satellite data into a crop growth model
- Author
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Loise Wandera, Miriam Machwitz, Lucien Hoffmann, Holger Lilienthal, Laura Giustarini, Thomas Udelhoven, Patrick Matgen, David Frantz, Martin Schlerf, and Christian Bossung
- Subjects
Environmental Engineering ,Atmospheric radiative transfer codes ,Data assimilation ,Coupling (computer programming) ,Pixel ,Ecological Modeling ,Environmental science ,Biomass ,Spatial variability ,Particle filter ,Spatial analysis ,Software ,Remote sensing - Abstract
Complex crop growth models (CGM) require a large number of input parameters, which can cause large errors if they are uncertain. Furthermore, they often lack spatial information. The coupling of a CGM with a radiative transfer model offers the possibility to assimilate remote sensing data while taking into account uncertainties in input parameters. A particle filter was used to assimilate satellite data into a CGM coupled with a leaf-canopy radiative transfer model to update biomass simulations of maize. The synthetic experiment set up to test the reliability of the procedure, highlighted the importance of the acquisition time. The real case study with RapidEye observations confirmed these findings. Data assimilation increased the accuracy of biomass predictions in the majority of the six maize fields where biomass validation data was available, with improvements of up to 15%. The smallest and largest errors in biomass prediction after assimilation were 82?kg/ha and 2116?kg/ha, respectively. Furthermore, data assimilation enabled the production of biomass maps showing detailed spatial variability. Data assimilation using a particle filter for biomass estimation was conducted.Proof of concept with synthetic case studies.Multispectral satellite data (visible and near infrared) was found to be suitable for data assimilation.Assimilation of satellite data allowed biomass prediction on a pixel basis.
- Published
- 2014
23. Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery
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Miriam Machwitz, Andrea Ehammer, Stefan Dech, Sebastian Fritsch, and Christopher Conrad
- Subjects
validation ,Biome ,Uzbekistan ,Vegetation ,Normalized Difference Vegetation Index ,Spatial heterogeneity ,MODIS ,Photosynthetically active radiation ,FPAR ,General Earth and Planetary Sciences ,Environmental science ,Satellite ,Moderate-resolution imaging spectroradiometer ,RapidEye ,Scale (map) ,Remote sensing - Abstract
The fraction of photosynthetically active radiation FPAR absorbed by a vegetation canopy is an important variable for global vegetation modelling and is operationally available from data of the Terra Moderate Resolution Imaging Spectroradiometer MODIS satellite sensor starting from the year 2000. Product validation is ongoing and important for constant product improvement, but few studies have investigated the specific accuracy of MODIS FPAR using in situ measurements and none have focused on agricultural areas. This study therefore presents a validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in western Uzbekistan. High-resolution FPAR maps were compiled via linear regression between in situ FPAR measurements and the RapidEye normalized difference vegetation index NDVI for the 2009 season. The data were aggregated to the MODIS scale for comparison. Data on the percentage cover of agricultural crops per MODIS pixel allowed investigation of the impact of spatial heterogeneity on MODIS FPAR accuracy. Overall, the collection 5 MODIS FPAR overestimated RapidEye FPAR between approximately 6% and 15%. MODIS quality flags, the underlying biome classification and spatial heterogeneity were investigated as potential sources of error. MODIS data quality was very good in all cases. A comparison of the MODIS land-cover product with high-resolution land-use classification revealed a significant misclassification by MODIS. Yet, we found that the overestimation of MODIS FPAR is independent of classification accuracy. The results indicate that the amount of background information, present even in the most homogeneous pixels ∼70% crop cover, is most likely the reason for the overestimation. The behaviour of pure pixels could not be investigated due to a lack of appropriate pixels.
- Published
- 2012
24. REDD payments as incentive for reducing forest loss
- Author
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Gill Shepherd, Christiane Ehringhaus, Efrem Garedew, Gilles Etoga, Miriam Machwitz, Daniel Yeboah, Driss Ezzine de Blas, Neil Collier, Bruce M. Campbell, Johannes Förster, Osofo Dankama Kwasi Quarm, Marieke Sandker, Senja Vaatainen, Samuel Kofi Nyame, and Jacob Anati
- Subjects
Climate change ,Dégradation de l'environnement ,Agroforesterie ,Participatory modeling ,Deforestation ,United Nations Framework Convention on Climate Change ,K01 - Foresterie - Considérations générales ,Reducing emissions from deforestation and forest degradation ,Politique de l'environnement ,Theobroma cacao ,K70 - Dégâts causés aux forêts et leur protection ,Modélisation environnementale ,Ecology, Evolution, Behavior and Systematics ,Nature and Landscape Conservation ,geography ,geography.geographical_feature_category ,Incitation ,Ecology ,Agroforestry ,Old-growth forest ,Déboisement ,Incentive ,Climate change mitigation ,approches participatives ,Forêt ,P01 - Conservation de la nature et ressources foncières ,Business - Abstract
Strategies for reducing emissions from deforestation and forest degradation (REDD) could become an important part of a new agreement for climate change mitigation under the United Nations Framework Convention on Climate Change. We constructed a system dynamics model for a cocoa agroforest landscape in southwestern Ghana to explore whether REDD payments are likely to promote forest conservation and what socio-economic implications would be. Scenarios were constructed for business as usual (cocoa production at the expense of forest), for payments for avoided deforestation of old-growth forest only and for payments for avoided deforestation of all forests, including degraded forest. The results indicate that in the short term, REDD is likely to be preferred by farmers when the policy focuses on payments that halt the destruction of old-growth forests only. However, there is the risk that REDD contracts may be abandoned in the short term. The likeliness of farmers to opt for REDD is much lower when also avoiding deforestation of degraded forest since this land is needed for the expansion of cocoa production. Given that it is mainly the wealthier households that control the remaining forest outside the reserves, REDD payments may increase community differentiation, with negative consequences for REDD policies.
- Published
- 2010
25. Controls on dissolved organic matter leaching from forest litter grown under elevated atmospheric CO2
- Author
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Miriam Machwitz and Frank Hagedorn
- Subjects
chemistry.chemical_classification ,Tilia platyphyllos ,biology ,Chemistry ,Ecology ,Soil organic matter ,Soil Science ,Lessivage ,Mineralization (soil science) ,biology.organism_classification ,Microbiology ,Fagus sylvatica ,Environmental chemistry ,Dissolved organic carbon ,Organic matter ,Leaching (agriculture) - Abstract
The aim of our study was to identify controls on initial dissolved organic matter (DOM) leaching from decomposing forest litter and to estimate how it is affected by increasing atmospheric CO 2 . Using microcosms, we measured initial C mineralization and leaching rates of DOC, DON and biodegradable DOC from litter of eight tree species from CO 2 enrichment experiments in a 100 year-old broadleaf forest and a 30 year-old treeline ecosystem. Over 11 weekly leaching cycles, between 2.5% (Pinus uncinata, Fagus sylvatica) and 15% (Carpinus betulus) of litter C were leached as DOC, corresponding to 9–36% of the total mass loss. Significantly less, 0.9% (Pinus) to 4.5% (Tilia platyphyllos) of litter N was leached as DON. Leaching of DOC was not correlated to C mineralization, which ranged between 12% (Fagus) and 32% (Tilia) of litter C. While C mineralization increased with decreasing litter C/N ratios and lignin contents, DOC leaching particularly the initially leached DOC was significantly related to concentrations of non-structural carbohydrates (NSC) and phenolics. DOC leached after the third leaching cycle did not correlate with any of the measures of litter quality, but with the molar UV absorptivity of DOC, suggesting that DOC production is linked to lignin degradation. Previous CO 2 enrichment increased NSC and phenolics in the litter and decreased lignin contents, which resulted in significantly greater initial C mineralization ( + 5 % ) and DOC leaching rates ( + 16 % ) . However, these CO 2 effects were only significant during the initial leaching phase and much smaller than the differences between tree species. Initially leached DOC was less biodegradable when its parent litter was grown under elevated than under ambient CO 2 (38% vs. 42% of DOC across all species, P 0.05). Therefore, leaching of ‘refractory’ DOC was increased under elevated CO 2 , which will rather accelerate DOC inputs into mineral soils than further stimulate microbial activity. In summary, our study shows (1) that initial DOM leaching is controlled by other factors than C mineralization; and (2) that CO 2 enrichment of forests can stimulate initial mineralization and leaching of C from litter by altering its quality, but these effects will be short-term and much smaller than any change in species composition.
- Published
- 2007
26. Satellite based calculation of spatially distributed crop water requirements for cotton and wheat cultivation in Fergana Valley, Uzbekistan
- Author
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Miriam Machwitz, Galina Stulina, Stefan Dech, Christopher Conrad, Heiko Paeth, and Maren Rahmann
- Subjects
Hydrology ,Global and Planetary Change ,Irrigation ,geography ,geography.geographical_feature_category ,business.industry ,Drainage basin ,Remote sensing ,Oceanography ,Fergana Valley ,Water resources ,Crop ,Water management ,Agriculture ,Crop water requirement ,Environmental science ,Object-based classification ,Cropping system ,Orchard ,business ,RapidEye ,Water use - Abstract
This study focuses on the generation of reliable data for improving land and water use in Central Asia. An object-based remote sensing classification is applied and combined with the CropWat model developed by the Food and Agriculture Organization (FAO) to determine crop distribution and water requirements for irrigation of cotton and winter-wheat in Fergana Valley, Uzbekistan. The crop classification is conducted on RapidEye and Landsat data acquired before the onset of the main summer irrigation phases in July using a random forest algorithm. The ClimWat database of FAO is utilized for calculating crop water requirements (CWR) and crop irrigation requirements (CIR). Classification reveals an overall accuracy of 86.2% and exceeds a producer's (user's) accuracy of 95% (89%) for both, cotton and wheat. In 2010, cotton and winter-wheat are planted on 66.7% of the agricultural area under investigation, whereas orchard areas amount to 15.5%. The CWR modelled for winter-wheat and cotton cultivation revealed 5443 m 3 ha − 1 and 9278 m 3 ha − 1 , respectively. Subtracting effective precipitation leads to CIR of 4133 m 3 ha − 1 and 8813 m 3 ha − 1 . Comparisons of CWR and CIR for the area dominating crops with the total of water officially allocated for irrigation underline the pressure on the water resources in the entire Syr Darya catchment and suggest modifications of the cropping system towards more winter crops. The early season crop maps can be used for water saving as they enable modifications of water allocation plans within the different irrigation subsystems of the valley. The method for mapping spatially distributed CWR and CIR can be transferred to other irrigated areas in Central Asia and beyond.
- Published
- 2013
27. Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing
- Author
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Christian Bossung, Lucien Hoffmann, Miriam Machwitz, Philippe Delfosse, Fréderic Mayer, Thomas Udelhoven, Franz Ronellenfitsch, and Martin Schlerf
- Subjects
agriculture ,bioenergy ,biomethane potential ,hyperspectral remote sensing ,Silage ,Science ,Biomass ,Energy crop ,Anaerobic digestion ,Biogas ,Agronomy ,Biofuel ,Bioenergy ,General Earth and Planetary Sciences ,Environmental science ,HyMap - Abstract
Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMPFM, Nm3 biogas/t fresh matter (Nm3/t FM)) and BMP per area (BMParea, Nm3 biogas/ha (Nm3/ha)). We found that BMPFM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (Rcv2 = 0.82, n = 35), especially at levels >190 Nm3/t. In the in situ approach PLSR prediction quality declined (Rcv2 = 0.50, n = 20). BMParea, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR.
- Published
- 2013
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28. Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles
- Author
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Ursula Gessner, Miriam Machwitz, Christopher Conrad, and Stefan Dech
- Subjects
geography ,Land cover ,geography.geographical_feature_category ,Biome ,Soil Science ,Geology ,Vegetation ,Grassland ,Shrubland ,Vegetation structure ,Thematic Mapper ,Sub-pixel fractional cover ,Grassland and shrubland biome ,Savanna ,Africa ,Environmental science ,Satellite imagery ,Moderate-resolution imaging spectroradiometer ,Computers in Earth Sciences ,Multi-resolution analysis ,Landoberfläche ,Remote sensing - Abstract
Evaluations of existing land cover maps have revealed that high landscape heterogeneity and small patch sizes are a major reason for misclassification. These problems globally occur in biomes of mixed vegetation structure and are particularly relevant for African savannas. This paper presents a multi-resolution approach to derive fractional cover of vegetation growth forms at sub-pixel level, aiming at an improved mapping of land cover in the African grassland, savanna and shrubland biome. Fractional cover is delineated for woody growth forms (trees and shrubs), herbaceous growth forms, and bare surface. The approach incorporates very high resolution (QuickBird/IKONOS, 0.6–1 m), high resolution (Landsat TM/ETM+, 30 m), and medium resolution data (MODIS, 250 m). While QuickBird/IKONOS data are classified into discrete classes, at Landsat and MODIS resolutions, sub-pixel cover is delineated using non-parametric ensemble regression trees from the random forest family. The propagation of errors in the hierarchical multi-resolution approach is assessed with Monte Carlos simulations. The multi-resolution approach allows the adequate description of the heterogeneous vegetation structure in selected study regions of Southern Africa. The RMSE of the delineated fractional cover values range between 3.1% and 8.2% when compared with higher resolution data and between 4.4% and 9.9% when compared with field surveys. Errors at the Landsat resolution show minor influence on the accuracy of the MODIS results. Regarding the inter-resolution error propagation, for 90% of the Monte Carlo simulations, errors at the Landsat resolution resulted in RMSEs for MODIS increased by less than 4% (woody vegetation), 3.5% (herbaceous vegetation) and 2% (bare surface).
- Published
- 2013
29. Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling
- Author
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Miriam Machwitz, Gunter Menz, Asia Khamzina, Christopher Conrad, Elena Kan, and Olena Dubovyk
- Subjects
Conservation of Natural Resources ,Geological Phenomena ,Salinity ,Cropland abandonment ,Water table ,NDVI ,Linear trend analysis ,Management, Monitoring, Policy and Law ,Article ,Normalized Difference Vegetation Index ,Logistic regression modeling ,Spatio-Temporal Analysis ,Environmental Science(all) ,Environmental monitoring ,Water Movements ,Groundwater ,General Environmental Science ,Hydrology ,Lower reaches of Amu Darya River ,Agriculture ,Uzbekistan ,General Medicine ,Vegetation ,Pollution ,Soil quality ,Logistic Models ,MODIS ,Remote Sensing Technology ,Geographic Information Systems ,Land degradation ,Common spatial pattern ,Environmental science ,ddc:526 ,Environmental Monitoring - Abstract
Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds = 330 %), land-use intensity (odds = 103 %), low soil quality (odds = 49 %), slope (odds = 29 %), and salinity of the groundwater (odds = 26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.
- Published
- 2012
30. Relationships between high resolution RapidEye based fPAR and MODIS vegetation indices in a heterogeneous agricultural region
- Author
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Stefan Dech, Miriam Machwitz, Sebastian Fritsch, and Christopher Conrad
- Subjects
subpixel heterogenity ,Enhanced Vegetation Index (EVI) ,Vegetation ,Enhanced vegetation index ,Rapid Eye ,Normalized Difference Vegetation Index ,Spatial heterogeneity ,Remote Sensing ,Central Asia ,Spectroradiometer ,Geography ,MODIS ,Photosynthetically active radiation ,Upscaling ,Spatial ecology ,Scale (map) ,Remote sensing - Abstract
The Moderate Imaging Spectroradiometer (MODIS) provides operational products of the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the fraction of photosynthetic active radiation (fPAR). FPAR can be used in productivity models, but agricultural applications depend on sub-pixel heterogeneity. Examples for heterogeneous areas are the irrigation systems of the inner Aral Sea Basin, where the 1 km fPAR product proved less suited. An alternative can be to upscale fPAR to the 250 m scale, but there are few studies evaluating this approach. In this study, the use of MODIS 250 m NDVI and EVI for this approach was investigated in an irrigation system in western Uzbekistan. The analysis was based on high resolution fPAR maps and a crop map for the growing season 2009, derived from ground measurements and multitemporal RapidEye data. The data was used to explore statistical relationships between RapidEye fPAR and MODIS NDVI/EVI with respect to spatial heterogeneity. The correlations varied between products (daily NDVI, 8-day NDVI, 16-day NDVI/EVI), with results suggesting that 8-day NDVI performed best. The analyses and the compiled fPAR maps show that, compared to 1 km MODIS fPAR, the 250 m scale is more homogeneous, allows for crop-specific analyses, and better captures the spatial patterns in the study region.
- Published
- 2011
31. Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia
- Author
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Sebastian Fritsch, Christopher Conrad, Miriam Machwitz, Fabian Löw, Stefan Dech, and Gunther Schorcht
- Subjects
Irrigation ,business.industry ,Agriculture ,Forestry ,Vegetation ,Crop rotation ,Rapid Eye ,Remote Sensing ,Crop ,Central Asia ,Geography ,Agricultural land ,Zoning ,business ,Cropping ,Accuracy - Abstract
In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.
- Published
- 2011
32. Mapping of large irrigated areas in Central Asia using MODIS time series
- Author
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Miriam Machwitz, J. Bloethe, Christopher Conrad, Doris Klein, and Stefan Dech
- Subjects
remote senising ,Land use ,Sampling (statistics) ,Land cover ,Vegetation ,Training (civil) ,Random forest ,crop mapping ,Central Asia ,Remote sensing (archaeology) ,Environmental science ,Scale (map) ,Cartography ,Remote sensing - Abstract
Remote sensing offers the opportunity to produce land cover classifications for large and remote areas on a yearly basis and is an important tool in regions that lack these information. However often training and validation data to generate annual land cover maps are not available in necessary quantity - being from one year only or covering only a small extent of the region of interest. This study was focused on land use classifications at regional scale with a special emphasize on annual updates under the constraint of limited sampling data. Often, sampling is reduced to one year or to an unrepresentative area extend within the region of interest. The investigations for the period between 2004 and 2009 were conducted in the irrigation systems of the Amu Darya Delta in Central Asia, where reliable information on crop rotations is required for sustainable land and water management. Annual training and validation data were extracted from high resolution land use classifications. For classification, statistical features based on MODIS time series of vegetation indices, reflectance and land surface temperature (LST) were calculated and a random forest algorithm was applied. By a combination of training data from different years, the accuracy could be enhanced from an overall accuracy of 70% to more than 90% for a focused subregion and also good consistency with high resolution images for the other parts of the delta, which has to be confirmed using quantitative validation. A combination of a different number of years was tested. Already two years can be sufficient to generate a robust and transferable random forest to produce yearly land use maps. The study shows the possibility to combine training data from different years for the annual classification of irrigated croplands on a regional scale.
- Published
- 2010
33. A Land Cover Change Synthesis Study for the GLOWA Volta Basin in West Africa using Time Trajectory Satellite Observations and Cellular Automata Models
- Author
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Paul L. G. Vlek, L.T. Desta, Quang Bao Le, Michael Schmidt, S. Dech, Tobias Landmann, and Miriam Machwitz
- Subjects
Hydrology ,geography ,geography.geographical_feature_category ,Remote sensing (archaeology) ,Trajectory ,Drainage basin ,Environmental science ,Satellite ,Physical geography ,Vegetation ,Moderate-resolution imaging spectroradiometer ,Woodland ,Land cover - Abstract
Quantifying the regional effects of land cover change is imperative to improve future hydrological budget estimates within large river basins. In this study we aim to utilize binary logistic regressions models within a cellular automation (CA) modeling environment to find causalities for satellite remote sensing measured land cover change (LCC). We used 30-meter Landsat and 250-meter MODIS time-series observations to map LCC for different time trajectories in two large study areas in Burkina Faso and Ghana. We used the FAO land cover classification system (LCCS) legend to map LCC processes from the satellite trajectories. Socio-economic data on population density, distances to roads, and biophysical data sets were processed in the CA model. The neighborhood effect of the change predictors were accounted for by using an enrichment factor. The relationship between the satellite derived LCC and the major biophysical and socio-economic drivers showed that population density, and the increase of cropland areas are responsible for the conversion of forests and woodlands. This was observed for both study areas.
- Published
- 2008
34. Land Cover Analysis on Sub-Continental Scale: FAO LCCS Standard with 250 Meter MODIS Satellite Observations in West Africa
- Author
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Anna F. Cord, Christopher Conrad, Tobias Landmann, Stefan Dech, and Miriam Machwitz
- Subjects
Earth system science ,Advanced Spaceborne Thermal Emission and Reflection Radiometer ,Reference data ,Geography ,Decision tree learning ,Satellite ,Land cover ,Moderate-resolution imaging spectroradiometer ,Scale (map) ,Cartography ,Remote sensing - Abstract
Land cover information is an essential parameter for many earth system models and studies. However, especially in Africa, rigorous, regional standardized and geometrical spatially explicit land cover data on medium scale is missing, or often inconsistent and outdated. In this study we employed well corrected 250-meter MODIS time-series observations from 2006 and 2007 to map land cover rigorously over the Volta Basin in West Africa, with focus on the Burkina Faso and Ghana respectively. We used the FAO land cover classification system (LCCS) standard [8] for the legend codes and 15-30 ASTER imagery as reference data to train the classification tree algorithm. Primary aim is to contribute to a continental African and standardized medium scale land over data base, with improved mapping accuracies. Therefore, the dataset presented here can be deemed interoperable that is between local scale studies, at finer resolutions, and 1-kilometer global land cover products.
- Published
- 2008
35. Bridging the Gap Between Remote Sensing and Plant Phenotyping-Challenges and Opportunities for the Next Generation of Sustainable Agriculture
- Author
-
'Miriam Machwitz
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