Table 2: Example applications of the use of remote sensing technologies to detect change in vegetation.

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops

Correlación del contenido de clorofila foliar de la especie Coffea arabica con índices espectrales en imágenes

Chlorophyll is a fundamental pigment for the photosynthetic processes of plant species and constitutes a limitation in agricultural production. The estimation of the content of leaf chlorophyll (LCC) is generally carried out through invasive spectrophotometric techniques. The multispectral images and the Vegetation Indexes (IV) constitute an important alternative because they allow the estimation in situ of the pigment. In this work, it is intended to find the variability and relationships between the localized content of chlorophyll, of the Coffea arabica species, and IV taken from multispectral images. A random sampling of leaves was carried out, and healthy and sick leaves were selected. The LCC of 60 samples was estimated by spectrophotometry and the correlation coefficient with IV was found. The best indicators of the pigment were the GARI, GNDVI, and NDVI indexes, among 14 indexes studied. It was found that the variability of IV data in different areas of diseased leaves, agrees with the distribution of non-homogeneous chlorophyll in those leaves since chlorophyll degradation in this variety does not behave in an isotropic way. This result encourages the possibility of using this technique to infer the health status of this plant.La clorofila es un pigmento fundamental para los procesos fotosintéticos de las especies vegetales, y constituye una limitante en la producción agrícola. La estimación del contenido de clorofila foliar (LCC) se realiza generalmente mediante técnicas invasivas de espectrofotometría. Las imágenes multiespectrales y los Índices de Vegetación (IV), constituyen una alternativa importante debido a que permiten la estimación in situ del pigmento. En este trabajo se pretende encontrar la variabilidad y relaciones entre el contenido localizado de clorofila, de la especie Coffea arabica, e IV tomados de imágenes multiespectrales. Se realizó un muestreo de hojas al azar, y se seleccionaron hojas sanas y enfermas. Se estimó el LCC de 60 muestras mediante espectrofotometría y se encontró el coeficiente de correlación con IV. Los mejores indicadores del pigmento fueron los índices GARI, GNDVI y NDVI, entre 14 índices estudiados. Se encontró que la variabilidad de los datos de IV en diferentes zonas de hojas enfermas, concuerda con la distribución de clorofila no homogénea en esas hojas, ya que la degradación de clorofila en esta variedad no se comporta de forma isotrópica. Este resultado alienta la posibilidad de usar esta técnica para inferir el estado de salud de esta planta.A clorofila é um pigmento fundamental para os a os processos fotossintéticos das espécies vegetais e constitui um fator limitante na produção agrícola. A plantação de café está sendo afetada por essa situação, e a medição desse pigmento é essencial. A estimativa do teor de clorofila é geralmente realizada usando técnicas espectrofotométricas no laboratório. Essas técnicas de análise são invasivas e a amostra é destruída. Aliás, as imagens multiespectrais em conjunto com os índices de vegetação (IV), constituem uma alternativa importante para avaliar remotamente o pigmento. Neste trabalho, é investigada a viabilidade do uso dos índices IV, obtidos a partir de imagens multiespectrais, como um método indireto para estimar o conteúdo da clorofila nas folhas das espécies vegetais de Coffea arábica. Foram estudados 14 índices de vegetação e os melhores foram os GARI, GNDVI y NDVI. Além disso, verificou-se que a variabilidade dos dados dos índices IV, em diferentes áreas de folhas não saudáveis concorda com a distribuição de clorofila não homogênea nessas folhas, porque a degradação da clorofila nessa variedade não se comporta isotropicamente. Este resultado incentiva a possibilidade de usar esta técnica para inferir o estado de saúde desta planta

Sensors Application in Agriculture

Novel technologies are playing an important role in the development of crop and livestock farming and have the potential to be the key drivers of sustainable intensification of agricultural systems. In particular, new sensors are now available with reduced dimensions, reduced costs, and increased performances, which can be implemented and integrated in production systems, providing more data and eventually an increase in information. It is of great importance to support the digital transformation, precision agriculture, and smart farming, and to eventually allow a revolution in the way food is produced. In order to exploit these results, authoritative studies from the research world are still needed to support the development and implementation of new solutions and best practices. This Special Issue is aimed at bringing together recent developments related to novel sensors and their proved or potential applications in agriculture

Remote sensing of impervious surface area and its interaction with land surface temperature variability in Pretoria, South Africa

Includes summary for chapter 1-5Pretoria, City of Tshwane (COT), Gauteng Province, South Africa is one of the cities that continues to experience rapid urban sprawl as a result of population growth and various land use, leading to the change of natural vegetation lands into impervious surface area (ISA). These are associated with transportation (paved roads, streets, highways, parking lots and sidewalks) and cemented buildings and rooftops, made of completely or partly impermeable artificial materials (e.g., asphalt, concrete, and brick). These landscapes influence the micro-climate (e.g., land surface temperature, LST) of Pretoria City as evidenced by the recent heat waves characterized by high temperature. Therefore, understanding ISA changes will provide information for city planning and environmental management. Conventionally, deriving ISA information has been dependent on field surveys and manual digitizing from hard copy maps, which is laborious and time-consuming. Remote sensing provides an avenue for deriving spatially explicit and timely ISA information. Numerous methods have been developed to estimate and retrieve ISA and LST from satellite imagery. There are limited studies focusing on the extraction of ISA and its relationship with LST variability across major cities in Africa. The objectives of the study were: (i) to explore suitable spectral indices to improve the delineation of built-up impervious surface areas from very high resolution multispectral data (e.g., WorldView-2), (ii) to examine exposed rooftop impervious surface area based on different colours, and their interplay with surface temperature variability, (iii) to determine if the spatio-temporal built-up ISA distribution pattern in relation to elevation influences urban heat island (UHI) extent using an optimal analytical scale and (iv) to assess the spatio-temporal change characteristics of ISA expansion using the corresponding surface temperature (LST) at selected administrative subplace units (i.e., local region scale). The study objectives were investigated using remote sensing data such as WorldView-2 (a very high-resolution multispectral sensor), medium resolution Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) at multiple scales. The ISA mapping methods used in this study can be grouped into two major categories: (i) the classification-based approach consisting of an object-based multi-class classification with overall accuracy ~90.4% and a multitemporal pixel-based binary classification. The latter yielded an area under the receiver operating characteristic curve (AUROC) = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015. (ii) the spectral index-based approach such as a new built-up extraction index (NBEI) derived in this study which yielded a high AUROC = ~0.82 compared to Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The multitemporal built-up Index (BUI) also estimated with AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. This indicates that all these methods employed, mapped ISA with high predictive accuracy from remote sensing data. Furthermore, the single-channel algorithm (SCA) was employed to retrieve LST from the thermal infrared (TIR) band of the Landsat images. The LST overall retrieval error for the entire study generally was quite low (overall root mean square RMSE ≤ ~1.48OC), which signifies that the Landsat TIR used provided good results for further analysis. In conclusion, the study showed the potential of multispectral remote sensing data to quantify ISA and evaluate its interaction with surface temperature variability despite the complex urban landscape in Pretoria. Also, using impervious surface LST as a complementary metric in this research helped to reveal urban heat island distribution and improve understanding of the spatio-temporal developing trend of urban expansion at a local spatial scale.Rapid urbanization because of population growth has led to the conversion of natural lands into large man-made landscapes which affects the micro-climate. Rooftop reflectivity, material, colour, slope, height, aspect, elevation are factors that potentially contribute to temperature variability. Therefore, strategically designed rooftop impervious surfaces have the potential to translate into significant energy, long-term cost savings, and health benefits. In this experimental study, we used the semi-automated Environment for Visualizing Images (ENVI) Feature Extraction that uses an object-based image analysis approach to classify rooftop based on colours from WorldView-2 (WV-2) image with overall accuracy ~90.4% and kappa coefficient ~0.87 respectively. The daytime retrieved surface temperatures were derived from 15m pan-sharpened Landsat 8 TIRS with a range of ~14.6OC to ~65OC (retrieval error = 0.38OC) for the same month covering Lynwood Ridge a residential area in Pretoria. Thereafter, the relationship between the rooftops and surface temperature (LST) were examined using multivariate statistical analysis. The results of this research reveal that the interaction between the applicable rooftop explanatory features (i.e., reflectance, texture measures and topographical properties) can explain over 22.10% of the variation in daytime rooftop surface temperatures. Furthermore, analysis of spatial distribution between mean daytime surface temperature and the residential rooftop indicated that the red, brown and green roof surfaces show lower LST values due to high reflectivity, high emissivity and low heat capacity during the daytime. The study concludes that in any study related to the spatial distribution of rooftop impervious surface area surface temperature, effect of various explanatory variables must be considered. The results of this experimental study serve as a useful approach for further application in urban planning and sustainable development.Evaluating changes in built-up impervious surface area (ISA) to understand the urban heat island (UHI) extent is valuable for governments in major cities in developing countries experiencing rapid urbanization and industrialization. This work aims at assessing built-up ISA spatio-temporal and influence on land surface temperature (LST) variability in the context of urban sprawl. Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) were used to quantify ISA using built-up Index (BUI) and spatio-temporal dynamics from 1993-2013. Thereafter using a suitable analytical sampling scale that represents the estimated ISA-LST, we examined its distribution in relation to elevation using the Shuttle Radar Topography Mission (SRTM) and also create Getis-Ord Gi* statistics hotspot maps to display the UHI extent. The BUI ISA extraction results show a high predictive accuracy with area under the receiver operating characteristic curve, AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. The ISA spatio-temporal changes within ten years interval time frame results revealed a 14% total growth rate during the study year. Based on a suitable analytical scale (90x90) for the hexagon polygon grid, the majority of ISA distribution across the years was at an elevation range of between >1200m – 1600m. Also, Getis-Ord Gi* statistics hotspot maps revealed that hotspot regions expanded through time with a total growth rate of 19% and coldspot regions decreased by 3%. Our findings can represent useful information for policymakers by providing a scientific basis for sustainable urban planning and management.Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ~1.44OC, 1.40OC, ~0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data is the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 to improve built-up material mapping irrespective of material type, age and colour. The new index was derived from spectral bands such as Green, Red edge, NIR1 and NIR2 bands that profoundly explain the variation in built-up areas on WorldView-2 image (WV-2). The result showed that NBEI improves the extraction of built-up areas with high accuracy (area under the receiver operating characteristic curve, AUROC = ~0.82) compared to the existing indices such as Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78 ), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material specific, and would be necessary for urban area mapping.Environmental SciencesD. Phil. (Environmental Sciences

Detecting Dubas bug infestations using high resolution multispectral satellite data in Oman

The Dubas bug, Ommatissus lybicus de Bergevin, is one of the major pests of the date palm, Phoenix dactylifera, in Oman, reducing its production by 28%. In addition to the important annual costs to control this pest nationwide, effort, cost and time are spent surveying and spotting O. lybicus infestations. Several studies have indicated the possibility of using remote sensing technology to identify plants stressed by pest infestation. The aim of the present study is to detect O. lybicus infestations by quantifying reflectance changes of different infestation levels and calculating different vegetation indices (VIs) using high-resolution multispectral (MS) images. An image of an area with different sub-locations that had varying levels of infestation was acquired in March 2017 using the WorldView-3 satellite. The reflectance of 8 bands, 32 spectral VIs and maximum likelihood classification (MLC) were derived from the image, and then the correlation was tested using ground-infestation data. The results revealed that the reflectance decreased in the red edge and near-infrared (NIR) bands as the infestation level increased. High levels of infestation showed a significant difference in three bands, red edge, NIR1 and NIR2, compared to no, low and medium levels of infestation. Nineteen out of 32 VIs showed a significant relation with the infestation levels. The relation ranged between r = −0.12, p