62 research outputs found

    Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

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    Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

    Mapping Soil Salinity and Its Impact on Agricultural Production in Al Hassa Oasis in Saudi Arabia

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    Soil salinity is considered as one of the major environmental issues globally that restricts agricultural growth and productivity, especially in arid and semi-arid regions. One such region is Al Hassa Oasis in the eastern province of Saudi Arabia, which is one of the most productive date palm (Phoenix dactylifera L.) farming regions in Saudi Arabia and is seriously threatened by soil salinity. Development of remote sensing techniques and modelling approaches that can assess and map soil salinity and the associated agricultural impacts accurately and its likely future distribution should be useful in formulating more effective, long-term management plans. The main objective of this study was to detect, assess and map soil salinity and and its impact on agricultural production in the Al Hassa Oasis. The presented research first started by reviewing the related literature that have utilized the use of remote sensing data and techniques to map and monitor soil salinity. This review started by discussing soil salinity indicators that are commonly used to detect soil salinity. Soil salinity can be detected either directly from the spectral reflectance patterns of salt features visible at the soil surface, or indirectly using the vegetation reflectance since it impacts vegetation. Also, it investigated the most commonly used remote sensors and techniques for monitoring and mapping soil salinity in previous studies. Both spectral vegetation and salinity indices that have been developed and proposed for soil salinity detection and mapping have been reviewed. Finally, issues limiting the use of remote sensing for soil salinity mapping, particularly in arid and semi-arid regions have been highlighted. In the second study, broadband vegetation and soil salinity indices derived from IKONOS images along with ground data in the form of soil samples from three sites across the Al Hassa Oasis were used to assess soil salinity in the Al-Hassa Oasis. The effectiveness of these indices to assess soil salinity over a dominant date palm region was examined statistically. The results showed that very strongly saline soils with different salinity level ranges are spread across the three sites in the study area. Among the investigated indices, the Soil Adjusted Vegetation Index (SAVI), Normalized Differential Salinity Index (NDSI) and Salinity Index (SI-T) yielded the best results for assessing the soil salinity in densely vegetated area, while NDSI and SI-T revealed the highest significant correlation with salinity for less densely vegetated lands and bare soils. In the third study, combined spectral-based statistical regression models were developed using IKONOS images to model and map the spatial variation of the soil salinity in the Al Hassa Oasis. Statistical correlation between Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Integrating SI and band 3 into one model produced the best fit with R2 = 0.65. The high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of SI in enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors.16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. In the fourth study, Landsat time series data of years 1985, 2000 and 2013 were used to detect the temporal change in soil salinity and vegetation cover in the Al Hassa Oasis and investigate whether there is any linkage of vegetation cover change to the change in soil salinity over a 28-year period. Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) differencing images were used to identify vegetation and salinity change/no-change for the two periods. The results revealed that soil salinity during 2000-2013 exhibited much higher increase compared to 1985-2000, while the vegetation cover declined for the same period. Highly significant (p In the fifth study, the effects of physical and proximity factors, including elevation, slope, soil salinity, distance to water, distance to built-up areas, distance to roads, distance to drainage and distance to irrigation factors on agricultural expansion in the Al Hassa Oasis were investigated. A logistic regression model was used for two time periods of agricultural change in 1985 and 2015. The probable agricultural expansion maps based on agricultural changes in 1985 was used to test the performance of the model to predict the probable agricultural expansion after 2015. This was achieved by comparing the probable maps of 1985 and the actual agricultural land of 2015 model. The Relative Operating Characteristic (ROC) method was also used and together these two methods were used to validate the developed model. The results showed that the prediction model of 2015 provides a reliable and consistent prediction based on the performance of 1985. The logistic regression results revealed that among the investigated factors, distance to water, distance to built-up areas and soil salinity were the major factors having a significant influence on agricultural expansion. In the last study, the potential distribution of date palm was assessed under current and future climate scenarios of 2050 and 2100. Here, CLIMEX (an ecological niche model) and two different Global Climate Models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), were employed with the A2 emission scenario to model the potential date palm distribution under current and future climates in Saudi Arabia. A sensitivity analysis was conducted to identify the CLIMEX model parameters that had the most influence on date palm distribution. The model was also run with the incorporation of six non-climatic parameters, which are soil taxonomy, soil texture, soil salinity, land use, landform and slopes, to further refine the distributions. The results from both GCMs showed a significant reduction in climatic suitability for date palm cultivation in Saudi Arabia by 2100 due to increment of heat stress. The lower optimal soil moisture, cold stress temperature threshold and wet stress threshold parameters had the greatest impact on sensitivity, while other parameters were moderately sensitive or insensitive to change. A more restricted distribution was projected with the inclusion of non-climatic parameters. Overall, the research demonstrated the potential of remote sensing and modeling techniques for assessing and mapping soil salinity and providing the essential information of its impacts on date palm plantation. The findings provide useful information for land managers, environmental decision makers and governments, which may help them in implementing more suitable adaptation measures, such as the use of new technologies, management practices and new varieties, to overcome the issue of soil salinity and its impact on this important economic crop so that long-term sustainable production of date palm in this region can be achieved. Additionally, the information derived from this research could be considered as a useful starting point for public policy to promote the resilience of agricultural systems, especially for smallholder farmers who might face more challenges, if not total loss, not only due to soil salinity but also due to climate change

    Signals in the Soil: Subsurface Sensing

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    In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

    Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

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    Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

    Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

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    By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way

    Quantifying soybean phenotypes using UAV imagery and machine learning, deep learning methods

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    Crop breeding programs aim to introduce new cultivars to the world with improved traits to solve the food crisis. Food production should need to be twice of current growth rate to feed the increasing number of people by 2050. Soybean is one the major grain in the world and only US contributes around 35 percent of world soybean production. To increase soybean production, breeders still rely on conventional breeding strategy, which is mainly a 'trial and error' process. These constraints limit the expected progress of the crop breeding program. The goal was to quantify the soybean phenotypes of plant lodging and pubescence color using UAV-based imagery and advanced machine learning. Plant lodging and soybean pubescence color are two of the most important phenotypes for soybean breeding programs. Soybean lodging and pubescence color is conventionally evaluated visually by breeders, which is time-consuming and subjective to human errors. The goal of this study was to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in the assessment of lodging conditions and deep learning in the assessment pubescence color of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1,266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores and pubescence scores were visually assessed by experienced breeders. Lodging scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. In contrast, pubescence color scores were grouped into three classes, i.e., gray, tawny, and segregation. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN), and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data pre-processing methods were used to treat the imbalanced dataset to improve the classification accuracy. Results indicate that the pre-processing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Over-sampling-Edited Nearest Neighbor (SMOTE-ENN) may be an excellent pre-processing method for using unbalanced datasets and classification tasks. Furthermore, an overall accuracy of 96 percent was obtained using the SMOTE-ENN dataset and ANN classifier. On the other hand, to classify the soybean pubescence color, seven pre-trained deep learning models, i.e., DenseNet121, DenseNet169, DenseNet201, ResNet50, InceptionResNet-V2, Inception-V3, and EfficientNet were used, and images of each plot were fed into the model. Data was enhanced using two rotational and two scaling factors to increase the datasets. Among the seven pre-trained deep learning models, ResNet50 and DenseNet121 classifiers showed a higher overall accuracy of 88 percent, along with higher precision, recall, and F1-score for all three classes of pubescence color. In conclusion, the developed UAV-based high-throughput phenotyping system can gather image features to estimate soybean crucial phenotypes and classify the phenotypes, which will help the breeders in phenotypic variations in breeding trials. Also, the RGB imagery-based classification could be a cost-effective choice for breeders and associated researchers for plant breeding programs in identifying superior genotypes.Includes bibliographical references

    Application of high resolution remote sensing to detect and map the pasture weed Paterson’s curse (Echium plantagineum) in Western Australia

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    This study investigated the utility of three types of remotely sensed data (field spectroscopy, airborne multispectral and satellite hyperspectral) for detecting and mapping Paterson’s curse (Echium plantagineum) in the Wheatbelt Region of Western Australia. Using different classification, statistical and quantitative validation approaches, the study found that spectral resolution and timing of image capture were the most important factors for discriminating Paterson’s curse and producing acceptable levels of mapping accuracy

    Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

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    Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

    UAV and field spectrometer based remote sensing for maize phenotyping, varietal discrimination and yield forecasting.

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    Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize is the major staple food crop in the majority of Sub-Saharan African (SSA) countries. However, production statistics (croplands and yields) are rarely measured, and where they are recorded, accuracy is poor because the statistics are updated through the farm survey method, which is error-prone and is time-consuming, and expensive. There is an urgent need to use affordable, accurate, timely, and readily accessible data collection and spatial analysis tools, including robust data extraction and processing techniques for precise yield forecasting for decision support and early warning systems. Meeting Africa’s rising food demand, which is driven by population growth and low productivity requires doubling the current production of major grain crops like maize by 2050. This requires innovative approaches and mechanisms that support accurate yield forecasting for early warning systems coupled with accelerated crop genetic improvement. Recent advances in remote sensing and geographical information system (GIS) have enabled detailed cropland mapping, spatial analysis of land suitability, crop type, and varietal discrimination, and ultimately grain yield forecasting in the developed world. However, although remote sensing and spatial analysis afforded us unprecedented opportunities for detailed data collection, their application in maize in Africa is still limited. In Africa, the challenge of crop yield forecasting using remote sensing is a daunting task because agriculture is highly fragmented, cropland is spatially heterogeneous, and cropping systems are highly diverse and mosaic. The dearth of data on the application of remote sensing and GIS in crop yield forecasting and land suitability analysis is not only worrying but catastrophic to food security monitoring and early warning systems in a continent burdened with chronic food shortages. Furthermore, accelerated crop genetic improvement to increase yield and achieve better adaptation to climate change is an issue of increasing urgency in order to satisfy the ever-increasing food demand. Recently, crop improvement programs are exploring the use of remotely sensed data that can be used cost-effectively for varietal evaluation and analysis in crop phenotyping, which currently remains a major bottleneck in crop genetic improvement. Yet studies on evaluation of maize varietal response to abiotic and biotic stresses found in the target production environments are limited. Therefore, the aim of this study was to model spatial land suitability for maize production using GIS and explore the potential use of field spectrometer and unmanned aerial vehicles (UAV) based remotely sensed data in maize varietal discrimination, high-throughput phenotyping, and yield prediction. Firstly, an overview of major remote-sensing platforms and their applicability to estimating maize grain yield in the African agricultural context, including research challenges was provided. Secondly, maize land suitability analysis using GIS and analytical hierarchical process (AHP) was performed in Zimbabwe. Finally, the utility of proximal and UAV-based remotely sensed data for maize phenotyping, varietal discrimination, and yield forecasting were explored. The results showed that the use of remote sensing data in estimating maize yield in the African agricultural systems is still limited and obtaining accurate and reliable maize yield estimates using remotely sensed data remains a challenge due to the highly fragmented and spatially heterogeneous nature of the cropping systems. Our results underscored the urgent need to use sensors with high spatial, temporal and spectral resolution, coupled with appropriate classification techniques and accurate ground truth data in estimating maize yield and its spatiotemporal dynamics in heterogeneous African agricultural landscapes for designing appropriate food security interventions. In addition, using modern spatial analysis tools is effective in assessing land suitability for targeting location-specific interventions and can serve as a decision support tool for policymakers and land-use planners regarding maize production and varietal placement. Discriminating maize varieties using remotely sensed data is crucial for crop monitoring, high throughput phenotyping, and yield forecasting. Using proximal sensing, our study showed that maize varietal discrimination is possible at certain phenological growth stages at the field level, which is crucial for yield forecasting and varietal phenotyping in crop improvement. In addition, the use of proximal remote sensing data with appropriate pre-processing algorithms such as auto scaling and generalized least squares weighting significantly improved the discrimination ability of partial least square discriminant analysis, and identify optimal spectral bands for maize varietal discrimination. Using proximal sensing was not only able to discriminate maize varieties but also identified the ideal phenological stage for varietal discrimination. Flowering and onset of senescence appeared to be the most ideal stages for accurate varietal discrimination using our data. In this study, we also demonstrated the potential use of UAV-based remotely sensed data in maize varietal phenotyping in crop improvement. Using multi-temporal UAV-derived multispectral data and Random Forest (RF) algorithm, our study identified not only the optimal bands and indices but also the ideal growth stage for accurate varietal phenotyping under maize streak virus (MSV) infection. The RF classifier selected green normalized difference vegetation index (GNDVI), green Chlorophyll Index (CIgreen), Red-edge Chlorophyll Index (CIred-edge), and the Red band as the most important variables for classification. The results demonstrated that spectral bands and vegetation indices measured at the vegetative stage are the most important for the classification of maize varietal response to MSV. Further analysis to predict MSV disease and grain yield using UAV-derived multispectral imaging data using multiple models showed that Red and NIR bands were frequently selected in most of the models that gave the highest prediction precision for grain yield. Combining the NIR band with Red band improved the explanatory power of the prediction models. This was also true with the selected indices. Thus, not all indices or bands measure the same aspect of biophysical parameters or crop productivity, and combining them increased the joint predictive power, consequently increased complementarity. Overall, the study has demonstrated the potential use of spatial analysis tools in land suitability analysis for maize production and the utility of remotely sensed data in maize varietal discrimination, phenotyping, and yield prediction. These results are useful for targeting location-specific interventions for varietal placement and integrating UAV-based high-throughput phenotyping systems in crop genetic improvement to address continental food security, especially as climate change accelerates
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