Understanding Striga occurrence and risk under changing climatic conditions across different agroecological farming systems at local and regional scales122

Philosophiae Doctor - PhDThe invasion by Striga in most cereal crop fields in Africa has posed an acute threat to food security and socioeconomic integrity. Consequently, numerous technological and research developments have been made to minimize and even control the Striga impacts on crop production. So far, efforts to control Striga have primarily focused on the manipulation of the genetics of the host crops, as well as understanding the phenological and physiological traits, along with the chemical composition of the weed

FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING

Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology

Mapping And Quantifying The Spatial Characteristics Of Agricultural Drainage Systems In Red River Valley Of The North

The purpose of the study is to map agricultural drainage systems (ADS) at the watershed scale using remote sensing and GIS techniques and examine the effect of ADS. For achieving the purpose of the study, this study selected the Red River Valley (RRV) of the North in which agriculture is a primary industry at the region. Excessive nutrients, sediment, and pesticide from this agricultural area flow into the Red River throughout subsurface drainages. The ADS aims to remove excessed water from agricultural fields, and this ADS is divided into two systems - uncontrolled drainage system (UCDS) and controlled drainage system (CDS). While UCDS allows water flows to the stream or river through using pipes without controlling water table, CDS regulates water table by an equipped structure that controls the volume of water flows in the agricultural fields. For mapping artificially drained tiles between UCDS and CDS fields in the RRV, this study used DEM to digitize linear, map slope, and to calculate surface area. This study digitized linear maps with eight UCDS and twenty CDS fields and the map contains a digitization of tile drainage locations, drainage system patterns, ADS types, and artificially drained surface areas. In the analysis of the two different ADS systems – i.e., UCDS and CDS, this study obtained the indexes by using NDVI, NDWI, and MSAVI2 provided by PlanetScope imagery. In testing the group difference between UCDS and CDS in the three different indexes is examined by computing Analysis of Variance Analysis (ANOVA). Also, this study postulated CDS is more effective to the healthiness of crops than UCDS does in the ADS system. The results of ANOVA indicated that there is no difference in the spectral indexes analyzed by ADS type (i.e., NDVI, NDWI, and MSAVI2). This result implies that the healthiness of crops is not affected ADS type, at least for the year studied here. The causes of this result, as a limitation, is derived from missing information, which is that agricultural research should consider region-specific crop calendars that involve significant idiosyncratic information, such as crop types and cycles, and regional climates. Nonetheless, natural factors in the RRV in 2019 – e.g., weather – are, of course, things outside of the control of researchers and agricultural producers

MACHINE LEARNING AND BIG DATA TECHNIQUES FOR SATELLITE-BASED RICE PHENOLOGY MONITORING

New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice using remote sensing techniques. This study attempts to implement a methodology aimed at monitoring rice phenology using optical satellite data. The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. Two optical moderate-resolution missions were combined to detect growth phases. Three machine-learning approaches (random forest, support vector machine, and gradient boosting trees) were trained with multitemporal NDVI data. Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. Tested models yielded an overall accuracy of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive, ripening and harvested categories. A second exploration was carried out by combining Sentinel-2 data and ground-based information about rice disease incidence. K-means clustering was used to map rice biophysical changes across reproductive and ripening phases. The findings ascertained the remote sensing capabilities to create new information about rice for Colombia’s conditions

Machine learning and big data techniques for satellite-based rice phenology

New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice using remote sensing techniques. This study attempts to implement a methodology aimed at monitoring rice phenology using optical satellite data. The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. Two optical moderate-resolution missions were combined to detect growth phases. Three machine-learning approaches (random forest, support vector machine, and gradient boosting trees) were trained with multitemporal NDVI data. Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. Tested models yielded an overall accuracy of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive, ripening and harvested categories. A second exploration was carried out by combining Sentinel-2 data and ground-based information about rice disease incidence. K-means clustering was used to map rice biophysical changes across reproductive and ripening phases. The findings ascertained the remote sensing capabilities to create new information about rice for Colombia’s conditions

Detecting and mapping forest nutrient deficiencies: eucalyptus variety (Eucalyptus grandis x and Eucalyptus urophylla) trees in KwaZulu-Natal, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal informatio

UAV Remote Sensing: An Innovative Tool for Detection and Management of Rice Diseases

Unmanned aerial vehicle (UAV) remote sensing is a new alternative to traditional diagnosis and detection of rice diseases by visual symptoms, providing quick, accurate and large coverage disease detection. UAV remote sensing offers an unprecedented spectral, spatial, and temporal resolution that can distinguish diseased plant tissue from healthy tissue based on the characteristics of disease symptoms. Research has been conducted on using RGB sensor, multispectral sensor, and hyperspectral sensor for successful detection and quantification of sheath blight (Rhizoctonia solani), using multispectral sensor to accurately detect narrow brown leaf spot (Cercospora janseana), and using infrared thermal sensor for detecting the occurrence of rice blast (Magnaporthe oryzae). UAV can also be used for aerial application, and UAV spraying has become a new means for control of rice sheath blight and other crop diseases in many countries, especially China and Japan. UAV spraying can operate at low altitudes and various speeds, making it suitable for situations where arial and ground applications are unavailable or infeasible and where precision applications are needed. Along with advances in digitalization and artificial intelligence for precision application across fertilizer, pest and crop management needs, this UAV technology will become a core tool in a farmer’s precision equipment mix in the future

Application of UAV Remote Sensing in Monitoring Banana Fusarium Wilt

Fusarium wilt poses a current threat to worldwide banana plantation areas. To treat the Fusarium wilt disease and adjust banana planting methods accordingly, it is important to introduce timely monitoring processes. In this chapter, the multispectral images acquired by unmanned aerial vehicle (UAV) was used to establish a method to identify which banana regions were infected or uninfected with Fusarium wilt disease. The vegetation indices (VIs), including the normalised difference vegetation index (NDVI), normalised difference red edge index (NDRE), structural independent pigment index (SIPI), red-edge structural independent pigment index (SIPIRE), green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), anthocyanin reflectance index (ARI), and carotenoid index (CARI), were selected for deciding the biophysical and biochemical characteristics of the banana plants. The relationships between the VIs and those plants infected or uninfected with Fusarium wilt were assessed using the binary logistic regression method. The results suggest that UAV-based multispectral imagery with a red-edge band is effective to identify banana Fusarium wilt disease, and that the CIRE had the best performance