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Dr. Ahmed Laamrani
Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, Morocco

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0 LiDAR
0 land cover change
0 Digital Soil Mapping
0 geoinformation

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Review
Published: 20 July 2021 in Land
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Globally, agricultural soils are being evaluated for their role in climate change regulation as a potential sink for atmospheric carbon dioxide (CO2) through sequestration of organic carbon as soil organic matter. Scientists and policy analysts increasingly seek to develop programs and policies which recognize the importance of mitigation of climate change and insurance of ecological sustainability when managing agricultural soils. In response, many countries are exploring options to develop local land-use carbon inventories to better understand the flow of carbon in agriculture to estimate its contribution to greenhouse gas (GHG) reporting. For instance, the Canadian province of Ontario does not currently have its own GHG inventory and relies on the Canada’s National Inventory Report (NIR). To address this, the province explored options to develop its own land-use carbon inventory to better understand the carbon resource in agricultural soils. As part of this undertaking, a gap analysis was conducted to identify the critical information gaps and limitations in estimating soil organic carbon (SOC) monitoring to develop a land-use carbon inventory (LUCI) for the cropland sector in Ontario. We conducted a review of analytical and modeling methods used to quantify GHG emissions and reporting for the cropland sectors in Canada, and compared them with the methods used in seven other countries (i.e., France, United Kingdom; Germany; United States of America, Australia, New Zealand, and Japan). From this comparison, four target areas of research were identified to consider in the development of a cropland sector LUCI in Ontario. First, there needs to be a refinement of the modelling approach used for SOC accounting. The Century model, which is used for Ontario’s cropland sector, can benefit from updates to the crop growth model and from the inclusion of manure management and other amendments. Secondly, a raster-based spatially explicit modelling approach is recommended as an alternative to using polygon-based inputs for soil data and census information for land management. This approach can leverage readily available Earth Observation (EO) data (e.g., remote sensing maps, digital soil maps). Thirdly, the contributions from soil erosion need to be included in inventory estimates of SOC emissions and removals from cropland. Fourth, establishment of an extensive network of long-term experimental sites to calibrate and validate the SOC models (i.e., CENTURY) is required. This can be done by putting in place a ground-truth program, through farmer-led research initiatives and collaboration, to deal with uncertainties due to spatial variability and regional climates. This approach would provide opportunities for farmers to collaborate on data collection by keeping detailed records of their cropping and soil management practices, and crop yields.

ACS Style

Ahmed Laamrani; Paul Voroney; Adam Gillespie; Abdelghani Chehbouni. Development of a Land Use Carbon Inventory for Agricultural Soils in the Canadian Province of Ontario. Land 2021, 10, 765 .

AMA Style

Ahmed Laamrani, Paul Voroney, Adam Gillespie, Abdelghani Chehbouni. Development of a Land Use Carbon Inventory for Agricultural Soils in the Canadian Province of Ontario. Land. 2021; 10 (7):765.

Chicago/Turabian Style

Ahmed Laamrani; Paul Voroney; Adam Gillespie; Abdelghani Chehbouni. 2021. "Development of a Land Use Carbon Inventory for Agricultural Soils in the Canadian Province of Ontario." Land 10, no. 7: 765.

Journal article
Published: 06 February 2021 in Remote Sensing
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Changing land use patterns is of great importance in environmental studies and critical for land use management decision making over farming systems in arid and semi-arid regions. Unfortunately, ground data scarcity or inadequacy in many regions can cause large uncertainties in the characterization of phenological changes in arid and semi-arid regions, which can hamper tailored decision making towards best agricultural management practices. Alternatively, state-of-the-art methods for phenological metrics’ extraction and long time-series analysis techniques of multispectral remote sensing imagery provide a viable solution. In this context, this study aims to characterize the changes over farming systems through trend analysis. To this end, four farming systems (fallow, rainfed, irrigated annual, and irrigated perennial) in arid areas of Morocco were studied based on four phenological metrics (PhM) (i.e., great integral, start, end, and length of the season). These were derived from large Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series using both a machine learning algorithm and a pixel-based change analysis method. Results showed that during the last twenty-year period (i.e., 2000–2019), a significant dynamism of the plant cover was linked to the behavior of farmers who tend to cultivate intensively and to invest in high-income crops. More specifically, a relevant variability in fallow and rainfed areas, closely linked to the weather conditions, was found. In addition, significant lag trends of the start (−6 days) and end (+3 days) were found, which indicate that the length of the season was related to the spatiotemporal variability of rainfall. This study has also highlighted the potential of multitemporal moderate spatial resolution data to accurately monitor agriculture and better manage land resources. In the meantime, for operationally implementing the use of such work in the field, we believe that it is essential consider the perceptions, opinions, and mutual benefits of farmers and stakeholders to improve strategies and synergies whilst ensuring food, welfare, and sustainability.

ACS Style

Youssef Lebrini; Abdelghani Boudhar; Ahmed Laamrani; Abdelaziz Htitiou; Hayat Lionboui; Adil Salhi; Abdelghani Chehbouni; Tarik Benabdelouahab. Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data. Remote Sensing 2021, 13, 578 .

AMA Style

Youssef Lebrini, Abdelghani Boudhar, Ahmed Laamrani, Abdelaziz Htitiou, Hayat Lionboui, Adil Salhi, Abdelghani Chehbouni, Tarik Benabdelouahab. Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data. Remote Sensing. 2021; 13 (4):578.

Chicago/Turabian Style

Youssef Lebrini; Abdelghani Boudhar; Ahmed Laamrani; Abdelaziz Htitiou; Hayat Lionboui; Adil Salhi; Abdelghani Chehbouni; Tarik Benabdelouahab. 2021. "Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data." Remote Sensing 13, no. 4: 578.

Review
Published: 07 November 2020 in Forests
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Northern boreal forests are characterized by accumulation of accumulation of peat (e.g., known as paludification). The functioning of northern boreal forest species and their capacity to adapt to environmental changes appear to depend on soil conditions. Climate warming is expected to have particularly pronounced effects on paludified boreal ecosystems and can alter current forest species composition and adaptation by changing soil conditions such as moisture, temperature regimes, and soil respiration. In this paper, we review and synthesize results from various reported studies (i.e., 88 research articles cited hereafter) to assess the effects of climatic warming on soil conditions of paludified forests in North America. Predictions that global warming may increase the decomposition rate must be considered in combination with its impact on soil moisture, which appears to be a limiting factor. Local adaptation or acclimation to current climatic conditions is occurring in boreal forests, which is likely to be important for continued ecosystem stability in the context of climate change. The most commonly cited response of boreal forest species to global warming is a northward migration that tracks the climate and soil conditions (e.g., temperature and moisture) to which they are adapted. Yet, some constraints may influence this kind of adaptation, such as water availability, changes in fire regimes, decomposer adaptations, and the dynamic of peat accumulation. In this paper, as a study case, we examined an example of potential effects of climatic warming on future paludification changes in the eastern lowland region of Canada through three different combined hypothetical scenarios based on temperature and precipitation (e.g., unchanged, increase, or decrease). An increase scenario in precipitation will likely favor peat accumulation in boreal forest stands prone to paludification and facilitate forested peatland expansion into upland forest, while decreased or unchanged precipitation combined with an increase in temperature will probably favor succession of forested peatlands to upland boreal forests. Each of the three scenarios were discussed in this study, and consequent silvicultural treatment options were suggested for each scenario to cope with anticipated soil and species changes in the boreal forests. We concluded that, despite the fact boreal soils will not constrain adaptation of boreal forests, some consequences of climatic warming may reduce the ability of certain species to respond to natural disturbances such as pest and disease outbreaks, and extreme weather events.

ACS Style

Ahmed Laamrani; Osvaldo Valeria; Abdelghani Chehbouni; Yves Bergeron. Analysis of the Effect of Climate Warming on Paludification Processes: Will Soil Conditions Limit the Adaptation of Northern Boreal Forests to Climate Change? A Synthesis. Forests 2020, 11, 1176 .

AMA Style

Ahmed Laamrani, Osvaldo Valeria, Abdelghani Chehbouni, Yves Bergeron. Analysis of the Effect of Climate Warming on Paludification Processes: Will Soil Conditions Limit the Adaptation of Northern Boreal Forests to Climate Change? A Synthesis. Forests. 2020; 11 (11):1176.

Chicago/Turabian Style

Ahmed Laamrani; Osvaldo Valeria; Abdelghani Chehbouni; Yves Bergeron. 2020. "Analysis of the Effect of Climate Warming on Paludification Processes: Will Soil Conditions Limit the Adaptation of Northern Boreal Forests to Climate Change? A Synthesis." Forests 11, no. 11: 1176.

Journal article
Published: 12 June 2020 in Agronomy
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The impacts of tillage practices and crop rotations are fundamental factors influencing changes in the soil carbon, and thus the sustainability of agricultural systems. The objective of this study was to compare soil carbon status and temporal changes in topsoil from different 4 year rotations and tillage treatments (i.e., no-till and conventional tillage). Rotation systems were primarily corn and soy-based and included cereal and alfalfa phases along with red clover cover crops. In 2018, soil samples were collected from a silty-loam topsoil (0–15 cm) from the 36 year long-term experiment site in southern Ontario, Canada. Total carbon (TC) contents of each sample were determined in the laboratory using combustion methods and comparisons were made between treatments using current and archived samples (i.e., 20 year and 9 year change, respectively) for selected crop rotations. Overall, TC concentrations were significantly higher for no-till compared with conventional tillage practices, regardless of the crop rotations employed. With regard to crop rotation, the highest TC concentrations were recorded in corn–corn–oats–barley (CCOB) rotations with red clover cover crop in both cereal phases. TC contents were, in descending order, found in corn–corn–alfalfa–alfalfa (CCAA), corn–corn–soybean–winter wheat (CCSW) with 1 year of seeded red clover, and corn–corn–corn–corn (CCCC). The lowest TC concentrations were observed in the corn–corn–soybean–soybean (CCSS) and corn–corn–oats–barley (CCOB) rotations without use of cover crops, and corn–corn–soybean–winter wheat (CCSW). We found that (i) crop rotation varieties that include two consecutive years of soybean had consistently lower TC concentrations compared with the remaining rotations; (ii) TC for all the investigated plots (no-till and/or tilled) increased over the 9 year and 20 year period; (iii) the no-tilled CCOB rotation with 2 years of cover crop showed the highest increase of TC content over the 20 year change period time; and (iv) interestingly, the no-till continuous corn (CCCC) rotation had higher TC than the soybean–soybean–corn–corn (SSCC) and corn–corn–soybean–winter wheat (CCSW). We concluded that conservation tillage (i.e., no-till) and incorporation of a cover crop into crop rotations had a positive effect in the accumulation of TC topsoil concentrations and could be suitable management practices to promote soil fertility and sustainability in our agricultural soils.

ACS Style

Ahmed Laamrani; Paul R. Voroney; Aaron A. Berg; Adam W. Gillespie; Michael March; Bill Deen; Ralph C. Martin. Temporal Change of Soil Carbon on a Long-Term Experimental Site with Variable Crop Rotations and Tillage Systems. Agronomy 2020, 10, 840 .

AMA Style

Ahmed Laamrani, Paul R. Voroney, Aaron A. Berg, Adam W. Gillespie, Michael March, Bill Deen, Ralph C. Martin. Temporal Change of Soil Carbon on a Long-Term Experimental Site with Variable Crop Rotations and Tillage Systems. Agronomy. 2020; 10 (6):840.

Chicago/Turabian Style

Ahmed Laamrani; Paul R. Voroney; Aaron A. Berg; Adam W. Gillespie; Michael March; Bill Deen; Ralph C. Martin. 2020. "Temporal Change of Soil Carbon on a Long-Term Experimental Site with Variable Crop Rotations and Tillage Systems." Agronomy 10, no. 6: 840.

Journal article
Published: 28 April 2020 in Remote Sensing
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Growing cover or winter crops and retaining crop residue on agricultural lands are considered beneficial management practices to address soil health and water quality. Remote sensing is a valuable tool to assess and map crop residue cover and cover crops. The objective of this study is to evaluate the performance of linear spectral unmixing for estimating soil cover in the non-growing season (November–May) over the Canadian Lake Erie Basin using seasonal multitemporal satellite imagery. Soil cover ground measurements and multispectral Landsat-8 imagery were acquired for two areas throughout the 2015–2016 non-growing season. Vertical soil cover photos were collected from up to 40 residue and 30 cover crop fields for each area (e.g., Elgin and Essex sites) when harvest, cloud, and snow conditions permitted. Images and data were reviewed and compiled to represent a complete coverage of the basin for three time periods (post-harvest, pre-planting, and post-planting). The correlations between field measured and satellite imagery estimated soil covers (e.g., residue and green) were evaluated by coefficient of determination (R2) and root mean square error (RMSE). Overall, spectral unmixing of satellite imagery is well suited for estimating soil cover in the non-growing season. Spectral unmixing using three-endmembers (i.e., corn residue-soil-green cover; soybean residue-soil-green cover) showed higher correlations with field measured soil cover than spectral unmixing using two- or four-endmembers. For the nine non-growing season images analyzed, the residue and green cover fractions derived from linear spectral unmixing using corn residue-soil-green cover endmembers were highly correlated with the field-measured data (mean R2 of 0.70 and 0.86, respectively). The results of this study support the use of remote sensing and spectral unmixing techniques for monitoring performance metrics for government initiatives, such as the Canada-Ontario Lake Erie Action Plan, and as input for sustainability indicators that both require knowledge about non-growing season land management over a large area.

ACS Style

Ahmed Laamrani; Pamela Joosse; Heather McNairn; Aaron Berg; Jennifer Hagerman; Kathryn Powell; Mark Berry. Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques. Remote Sensing 2020, 12, 1397 .

AMA Style

Ahmed Laamrani, Pamela Joosse, Heather McNairn, Aaron Berg, Jennifer Hagerman, Kathryn Powell, Mark Berry. Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques. Remote Sensing. 2020; 12 (9):1397.

Chicago/Turabian Style

Ahmed Laamrani; Pamela Joosse; Heather McNairn; Aaron Berg; Jennifer Hagerman; Kathryn Powell; Mark Berry. 2020. "Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques." Remote Sensing 12, no. 9: 1397.

Journal article
Published: 13 January 2020 in Sustainability
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The accumulation of organic material on top of the mineral soil over time (a process called paludification) is common in Northern Boreal coniferous forests. This natural process leads to a marked decrease in forest productivity overtime. Topography both at the surface of the forest floor (i.e., above ground) and the subsurface (i.e., top of mineral soil which is underground) is known to play a critical role in the paludification process. Until recently, the availability of more accurate topographic information regarding the surface and subsurface was a limiting factor for land management and modeling of spatial organic layer thickness (OLT) variability, a proxy for paludification. However so far, no research has assessed which of these two topographic variables has the greatest influence on paludification. This study aims to assess which topographic variable (surface or subsurface) better explains paludification, using high-resolution remote sensing technology (i.e., Light Detection and Ranging: LiDAR and Ground Penetrating Radar: GPR). To this end, field soil measurements were made in over 1614 sites distributed throughout the reference Valrennes Experimental site in Canadian northern coniferous forests. Then, a machine learning model (i.e., Random Forest, RF) was implemented to rank a set of selected predictor topographic variables (i.e., slope, aspect, mean curvature, plan curvature, profile curvature, and topographic wetness index) using the Mean Decrease Gini (MDG) index as an indicator of importance. Results showed that overall 83% of the overall variance was explained by the RF selected model, while the derived subsurface topography predictors had the lowest MDGs for predicting paludification. On the other hand, the surface slope predictor had the highest MDGs and better explained paludification. This finding would be particularly useful for implanting sustainable management strategies based on the surface variables of paludified northern boreal forests. This study has also highlighted the potential of LiDAR data to provide surface topographic spatial detail information for planning and optimizing forest management activities in paludified boreal forests. This is even of great importance when we know that LiDAR variables are easier to obtain compared to GPR derived variables (subsurface topographic variables).

ACS Style

Ahmed Laamrani; Osvaldo Valeria. Ranking Importance of Topographical Surface and Subsurface Parameters on Paludification in Northern Boreal Forests Using Very High Resolution Remotely Sensed Datasets. Sustainability 2020, 12, 577 .

AMA Style

Ahmed Laamrani, Osvaldo Valeria. Ranking Importance of Topographical Surface and Subsurface Parameters on Paludification in Northern Boreal Forests Using Very High Resolution Remotely Sensed Datasets. Sustainability. 2020; 12 (2):577.

Chicago/Turabian Style

Ahmed Laamrani; Osvaldo Valeria. 2020. "Ranking Importance of Topographical Surface and Subsurface Parameters on Paludification in Northern Boreal Forests Using Very High Resolution Remotely Sensed Datasets." Sustainability 12, no. 2: 577.

Journal article
Published: 31 May 2019 in Remote Sensing
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The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

ACS Style

Ahmed Laamrani; Aaron A. Berg; Paul Voroney; Hannes Feilhauer; Line Blackburn; Michael March; Phuong D. Dao; Yuhong He; Ralph C. Martin. Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada. Remote Sensing 2019, 11, 1298 .

AMA Style

Ahmed Laamrani, Aaron A. Berg, Paul Voroney, Hannes Feilhauer, Line Blackburn, Michael March, Phuong D. Dao, Yuhong He, Ralph C. Martin. Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada. Remote Sensing. 2019; 11 (11):1298.

Chicago/Turabian Style

Ahmed Laamrani; Aaron A. Berg; Paul Voroney; Hannes Feilhauer; Line Blackburn; Michael March; Phuong D. Dao; Yuhong He; Ralph C. Martin. 2019. "Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada." Remote Sensing 11, no. 11: 1298.

Journal article
Published: 27 February 2018 in Sensors
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Quantifying the amount of crop residue left in the field after harvest is a key issue for sustainability. Conventional assessment approaches (e.g., line-transect) are labor intensive, time-consuming and costly. Many proximal remote sensing devices and systems have been developed for agricultural applications such as cover crop and residue mapping. For instance, current mobile devices (smartphones & tablets) are usually equipped with digital cameras and global positioning systems and use applications (apps) for in-field data collection and analysis. In this study, we assess the feasibility and strength of a mobile device app developed to estimate crop residue cover. The performance of this novel technique (from here on referred to as “app” method) was compared against two point counting approaches: an established digital photograph-grid method and a new automated residue counting script developed in MATLAB at the University of Guelph. Both photograph-grid and script methods were used to count residue under 100 grid points. Residue percent cover was estimated using the app, script and photograph-grid methods on 54 vertical digital photographs (images of the ground taken from above at a height of 1.5 m) collected from eighteen fields (9 corn and 9 soybean, 3 samples each) located in southern Ontario. Results showed that residue estimates from the app method were in good agreement with those obtained from both photograph–grid and script methods (R2 = 0.86 and 0.84, respectively). This study has found that the app underestimates the residue coverage by −6.3% and −10.8% when compared to the photograph-grid and script methods, respectively. With regards to residue type, soybean has a slightly lower bias than corn (i.e., −5.3% vs. −7.4%). For photos with residue <30%, the app derived residue measurements are within ±5% difference (bias) of both photograph-grid- and script-derived residue measurements. These methods could therefore be used to track the recommended minimum soil residue cover of 30%, implemented to reduce farmland topsoil and nutrient losses that impact water quality. Overall, the app method was found to be a good alternative to the point counting methods, which are more time-consuming.

ACS Style

Ahmed Laamrani; Renato Pardo Lara; Aaron A. Berg; Dave Branson; Pamela Joosse. Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields. Sensors 2018, 18, 708 .

AMA Style

Ahmed Laamrani, Renato Pardo Lara, Aaron A. Berg, Dave Branson, Pamela Joosse. Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields. Sensors. 2018; 18 (3):708.

Chicago/Turabian Style

Ahmed Laamrani; Renato Pardo Lara; Aaron A. Berg; Dave Branson; Pamela Joosse. 2018. "Using a Mobile Device “App” and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields." Sensors 18, no. 3: 708.