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Formative Assessment 1

Examine a digital photo you have taken recently. Can you estimate its spatial resolution?

640 by 320 pixel

If you were to create a raster data file showing the major land-use types in your county, which encoding method would you use?

Run-Length Raster Encoding- This is because the method encodes cell values in runs of similarly valued pixels resulting to image file that is highly compressed. The method is used where neighbouring pixels have similar values thus mostly used showing land use types.

 What method would you use if you were to encode a map of the major roads/rivers in your county and why

Chain Coding- This is because it uses relative positions from a start point to define the outer boundary. The exterior sequence is stored where the endpoint finishes at the start point.

What is topology?

It is the spatial relationships between neighbouring or adjacent features. It refers to how point, line, and polygon features are arranged and share geometry (Pilouk, 2009).

Explain topological and non-topological data structures

Topological data structures show the spatial relationships between point, line, and area features (Goodchild, 2018). The adjacent features are recognised, and they share the same arc or node. The geometry is aware of its neighbours and can represent features that are overlapping.

  • Connectivity
  • Area Definition
  • Contiguity 

Topological relationships

Figure 1: Topological relationships

Non-topological- In this data structure, a shared boundary is stored once for each polygon thus each geometry is not aware of its neighbours. The data structures can represent features that are non-overlapping, space-filling polygons.

What are raster and vector GIS models? Give an example of objects representing point, line or polygon?

Raster data model is made up of grids and pixels. The pixels in most cases are square and spaced regularly. Rasters models, each pixel has its own value thus sometimes looks pixelated.

Vector data model consist of points, lines or arcs, and polygons, lines have end points, which meet at nodes.

Point data it is used to represent discrete data points and non-adjacent features. Points have zero dimensions. Examples of point data include schools and points of interest etc

Line data represent linear features such as streets, rivers, and roads.  Line features are one dimensional and therefore length can be measured. They have a start and end points.

Polygons They are used represent forest, city boundary or even lake. Polygons being two dimensional means the area and perimeter of a geographic feature can be measured.

Topological Relations

What are geospatial models? Explain raster and vector GIS models.

Geospatial data is information that describes objects, events, or other features with a location on or near the surface of the earth. Geospatial data combines location information and attribute information with temporal information (Roberts, 2020).

Geospatial data models are divided into two.

Vector data It is data which represent features such as roads, mountains, rivers, streets, cities and forests using points, lines and polygons. Vector data model can represent buildings using a point, rivers using lines and entire city represented by polygons.

Raster data A raster is an array of cells, where each cell has a value representing a specific portion of an object or a feature.  Each area is divided into rows and columns, forming a regular grid structure. Raster data creates substantially complex imagery, such as satellite images and photographs.

Explain the advantage and disadvantages of raster and vector GIS models. Compare both the models.

  • Good representation.
  • Easy to manage data
  • Provides fast processing
  • Topology can be completely described
  • Produce accurate graphics
  • It is possible to update, retrieve and generalizes attributes graphics.
  • They have complex data structure
  • Overlaying creates difficulties in processing
  • It is difficult to do simulation
  • Plotting and displaying can can be expensive.
  • It is impossible to do filtering and spatial analysis within polygons are impossible
  • Simple data structures
  • It is easy to do overlays
  • Spatial analysis methods simple to perform
  • Easy to do simulation because cells have same shape and size
  • Technology used is cheap
  • Raster maps are less beautiful than line maps
  • It is difficult to establish network linkages
  • It is time consuming doing projection transformations.

Explain data structures in detail

Vector data structure stores lines, points and polygons .

Point data it is used to represent discrete data points and non-adjacent features. Points have zero dimensions. Examples of point data include schools and points of interest etc

Line data represent linear features such as streets, rivers, and roads.  Line features are one dimensional and therefore length can be measured. They have a start and end points.

Polygons They are used represent forest, city boundary or even lake. Polygons being two dimensional means the area and perimeter of a geographic feature can be measured.

Raster Data Structure- It is a method for storage, processing and displaying of spatial data. The data is represented using pixels that have same sizes. The pixels are arranged in row and columns.

Full raster coding -in this method raster data is stored row by row.

Run length coding-the neighbouring cells have similar values for example in the case of land use.

Quadtree coding -It stores information by subdividing a square region into quadrants, quadrants maybe subdivided further until cell contents have same values. 

Blockwide coding-This method is a generalization of run-length encoding to two dimensions. square blocks are counted instead of sequences of 0s or 1s. The contents of the pixels and size are stored for each square.  

1.Which of the following is not a spatial relation that can be used in spatial queries?

Distance-based relation

Spatial autocorrelation

Topological relation

irection relation

Spatial Autocorrection.

There is a GIS dataset of points of interest (POIs) in a region, and you would like to select only those located within a pre-defined study area. How will you translate it into a spatial query?

Raster and Vector GIS Models

Spatial query: List points of interest within 5 miles of downtown Minneapolis

What does the spatial information in a GIS mainly consist of?

It consist of  information about  features and object on the earth surface. A specific location is defined by using pair coordinates that is latitude and longitude . Spatial data refers to the shape, size and location of the feature.

Vector data stores lines, points and polygons.

Point data it is used to represent discrete data points and non-adjacent features. Points have zero dimensions. Examples of point data include schools and points of interest etc

Line data represent linear features such as streets, rivers, and roads.  Line features are one dimensional and therefore length can be measured. They have a start and end points.

Polygons They are used represent forest, city boundary or even lake. Polygons being two dimensional means the area and perimeter of a geographic feature can be measured.

Raster Data -It is a method for storage, processing and displaying of spatial data. The data is represented using pixels that have same sizes. The pixels are arranged in row and columns.

How can space related information be divided?

Spatial data is used to describe any data related to or containing information about a specific location on the Earth’s surface. Spatial data can exist in a variety of formats and contains more than just location specific information. Attributes provide more information other than location about a feature or an object in space (Gutierrez, 2020).

Vector data It is data which represent features such as roads, mountains, rivers, streets, cities and forests using points, lines and polygons. Vector data model can represent buildings using a point, rivers using lines and entire city represented by polygons.

Raster data Raster data creates substantially complex imagery, such as satellite images and photographs. The data is represented using pixels that have same sizes. The pixels are arranged in row and columns.

Which approaches can be used to formulate a query?

Thematic query-It selects all objects that meet the required conditions or attributed.

Geometric query: - It selects all objects that meet spatial conditions required.

Topological query: - It selects all objects that have spatial relationships.  (Paramá, 2017)

Describe the inputs which should be used to answer the following questions and what would the outputs look like:

"Find all buildings which are located on parcels with a minimal area of 1000 m2 and a distance of more than 250m to the highway"

Advantages and Disadvantages

Inputs 

Figure 2: Inputs

  • Land parcels
  • Buildings
  • Highway 

Distribution of buildings with minimal area of 1000m2 and are more than 250m away from the highway.

Output  

Figure 3: Output  

Produce a map showing the centroids of each municipality in just the state of Säo Paulo, and add the outer boundary of Säo Paulo state.

SP Centroids

Figure 4: SP Centroids

Outer boundary of Säo Paulo state.

SP Boundary

Figure 5:SP Boundary

What is the mean Human Development Index of municipalities in each state of Brazil?

Mean HDI

Figure 6: Mean HDI

Produce a polygon/shapefile mapping the area of the municipality ‘Gaucha do Norte’ that is in the indigenous territory “Parque do Xingu”.

The shapefile didn’t open

In the state of Acre (AC), which two social housing (MCMV) projects are closest to each other? Create a 10km buffer around each housing project.

Distance Matrix

Distance Matrix

Figure 8: Distance matrix

10km buffer around each housing project

10KM Buffer

Figure 9: 10KM Buffer

Across Brazil, which municipalities have the lowest and highest number of MCMV housing units (UH) in its territory? Create a map of the distribution of total housing units by municipality.

  • ACARA-Lowest number of housing
  • RO DE JENEIRO-Highest number of housing

Map of the distribution of total housing units by municipality

Total households distribution per municipality

Figure 10:Total households distribution per municipality

1.Compare and contrast the advantages and limitations of different thematic map types.

choropleth map: they are thematic map that use colours to represent features uniformly (Vozenilek, 2020).

Advantages

  • The patterns generated are easy to read.
  • The map is easy to understand
  • Good representation of theme
  • Many audiences are familiar with this type of map
  • It can be used to represent continuous surface

Disadvantages

  • Exact values cannot be derived
  • Homogeneity may mislead the reader
  • Boundaries of data
  • Important details face risks of masking due to class numbers 

Dot Density map:  They are maps that place dots in proportion to the value being represented. The higher the size of the dot the higher the value represented.

Advantages

  • Pattern variations and distributions are represented
  • Visual impression easy to understand
  • It is easy to interpret
  • Used to represent discrete surfaces

Disadvantages

  • Does not permit representation of uniform distribution
  • The quantities cannot be read

Isoline- Type of maps where there is a line derived from interpolation and connects points of equal attribute value on the map.

Advantages

  • Magnitude arrangement effectively represented
  • Used for continuous surfaces

Disadvantages

  • Not possible to read exact values
  • They experience edge effect

proportional symbol map- They are types of maps where the size of the point is scaled proportionally to the value that’s being represented.

Advantages

  • Used to effectively represent data that have large values.
  • Individual point and enumerated data can be presented using this map
  • Used to represent discrete and abrupt surfaces

Disadvantages

  • Overlapping symbols leads to confusion
  • There is risk of under estimating symbol values
  • Exact values cannot be read

Dissymmetric map:  They are thematic maps that use additional data to generate new borders of enumeration units so as to improve spatial representation distribution.

Advantages.

They include variations in enumeration units.

Disadvantages

  • Exact values cannot be read.
  • Time consuming making the maps
  • Ancillary information is required 

Identify and discuss the best methods to represent population density in a thematic map.

Dot Density maps- They are map types that represent each data point with a dot. They provide great means of measuring density. Areas that have large dots are packed closely together and can be identified easily as areas of high density, while areas of low the dots are sparsely distributed. 

Data Structures

Choropleth Map- They are maps that use colours to represent statistics of attribute data proportional to the location. They are used frequently. Using colours Choropleths maps brings out good display of densities.

Graduated symbol maps- They are maps that show data using size of symbols that vary. Large symbols indicate high data concentration whereas small symbols show low data concentration.

Why should a choropleth map (almost) always show derived data? Provide a mapping example when the exception to this rule applies.

Because derived quantities are presented appropriately on choropleth maps. Examples include percent, density, rate and average. The exception to this rule applies when handling absolute data.

Describe why you should classify some thematic map types but not others.

Thematic maps provide understanding of spatial patterns; Thus, thematic map choice should be able to support such. Each type of thematic maps requires different method in data processing as well as employing different visual variables, this leads to discrete, continuous, smooth, or abrupt representations.

Other maps do not need classification because they do not cover variety of mapping solutions.

What visual variables and symbol dimensionalities are used for each thematic map type and how do these differences impact their design and use?

Choropleth Maps are maps that use colours to represent statistics of attribute data proportional to the location. They are used frequently. Using colours Choropleths maps brings out good display of densities. Visual variables used include hue, saturation, colour, and value. Pattern and shading are sometimes used.

Isopleth Maps are like choropleth maps in that they typically use colour value to encode data values.

Proportional Symbol Maps They are types of maps where the size of the point is scaled proportionally to the value that’s being represented. Visual variables used are size, and colour.

Dot Maps They are map types that represent each data point with a dot. They provide great means of measuring density.  Areas that have large dots are packed closely together and can be identified easily as areas of high density, while areas of low the dots are sparsely distributed. Visual variables used include size, colour, and sometimes texture.

Create a choropleth map showing the unemployment rate within a selected country. Design an appropriate legend for the map. 

USA Unemployment rate

Figure 11:USA Unemployment rate  

Create a proportional symbol map and a graduated symbol map for the same dataset and discuss the differences between the maps. Design an appropriate legend for both maps.

Formative Assessment 2

Graduated Symbol

Graduated symbol

Figure 12: Graduated symbol

Proportional symbol

Figure 13: Proportional symbol

Sketch a legend for each of the following thematic map types: choropleth, proportional symbol, graduated symbol, isoline, dot density and dissymmetric. 

Choropleth

Proportional symbol

Dasymmetric

Graduated symbol

Isoline

Dot Density

Legends for various maps 

Figure 14: Legends for various maps   

Remote sensing is an important early warning tool and allow the agricultural communities to mitigate and counter potential problems that may arise before spreading and affecting negatively crop productivity. Recent advancement in data analytics, sensor technologies and data management has led to remote sensing technologies being adopted in agricultural sector to improve farming. However, Implementation of Remote sensing data is yet to fully take shape, this is attributed to knowledge gap on the technology use, techno economic feasibilities as well as appropriateness. The literature review was conducted between 2015 to 2020 and it focused on the Remote Sensing application in agriculture crop life cycle from preparation of fields, planting, in-season applications and harvesting, the objective was to scientifically understand the potential contribution of Remote Sensing technologies support in agricultural production decision-making process.

Agriculture is major driver of economic growth for several countries, Agriculture provides  food that is basic need of mankind (Awokuse & Xie, 2015).  Over the past century there has been technological changes including Green Revolution, which has changed face and perception of agriculture.  Agriculture witnessed improvements in crop varieties, pesticides, irrigation and use of fertilizers, during the 1950s–1970s, enhanced food security and crop productivity, mostly in developing countries (Say, Keskin, Sehri, & Sekerli, 2018). Therefore, it is inevitable that we need to come up with ways of increasing food production without affecting the land so that the demands of increasing population is met.

In 21st Century the important element of sustainable agriculture is precision agriculture, which is strategy that use data analysis techniques, information and communication in decision making process for example during pesticide application, fertilizer application, water among others. All this contribute to increased food production, reduce loss of nutrients, and protect against negative environmental effects. In addition to crop production, Precision Agriculture is implemented in horticulture, viticulture, livestock production management and pasture management (Bongiovanni & Lowenberg-DeBoer, 2017). Currently, agriculture under fourth revolution which is facilitated by advancement in communication and information technologies.  Remote remote sensing, Big Data analysis, geographic information systems (GIS), global positioning systems (GPS), and artificial intelligence (AI) are some of emerging technologies and are being adopted and optimised in agricultural operations and inputs with the aim of enhancing food production reducing yield losses and inputs.

Remote sensing systems while they use communication technologies and information, they generate large volumes of spectral data because they have high temporal, spatial, radiometric and spectral resolutions needed in precision Agriculture application. Artificial Intelligence, Big Data, and machine learning are emerging as data processing techniques being utilized to generate information for volumes of data. The purpose of this review is to primarily and comprehensively understand the background and knowledge on remote sensing data applications and technologies in agriculture, with the focus on precision agriculture.

The method that was used to produce the review was comprehensive literature review including, Peer reviewed journal articles, edited academic books and professional journal articles. The literature review focus was on the topic of interest and the data reviewed was from 2015 to 2020,so as to get the deeper insights on the spatial data analysis and its application in precision agriculture .

It describe the integration of Geographic Information System (GIS) and Geospatial Positioning System (GPS) tools to provide comprehensive information on soil variability, crop growth, nutrient levels, crop health, water absorption, crop yield and topography (Gebbers & Adamchuk, 2010). It establishes precisely operations including sowing, harvesting, fertilizer application, and best tillage, it converts the traditional production methods to space variable data. Precision Agriculture ensure proper utilization of resource, reduced environmental pollution, promotes economic and social efficiency, and makes the farm products to be controllable and production in standards needed (Weiss, Jacob, & Duveillerc, 2020). Precision Agriculture ensures that profits are maximised, increased production, reduced variable costs, reduced soil erosion, reduced environmental impacts and pollution, large farms management, and proper monitoring and tracking of use of chemicals (Angelopoulou, Tziolas, Balafoutis, Zalidis, & Bochtis, 2019).

There are three components of precision agriculture: information, technology, and management

Information this entails information about the crop characteristics, soil fertility status, topography, soil texture, soil moisture content and retention, salinity, and waterlogging, insects’ diseases and weeds. Information also relating to climatic conditions, abiotic and biotic stresses, marketing, plant growth response, farmers socio economic condition, harvest, and postharvest handling. Information collected can be used create different farm or region maps, the maps can represent information on soil characteristics, pest incidence, topography, groundwater, weed distribution, and environmental pollution, the information help farmers utilize the information to make decision that ensure productivity and minimal environmental pollution and low farm yields.

Technologies- As technology evolve, farmers need to keep abreast with the changes to ensure that they continue to get maximum returns from their agricultural activities. The adoption and use of remote sensing can come in handy in ensuring that agricultural productivity and profitability is maintained by the farmers.

Management- efficient management of information and technologies are of great importance. Management ensures that there is proper utilisation of resources, cost reduction, increased productivity, efficiency in delivering of services and reduced environmental harm. Without proper management farmers will experience, poor crop yields, damage of land sue to excess fertilizers, increased weed infestation and overall low productivity. Thus, precision Agriculture is combination of components working together in synch to achieve maximum productivity.

  • It ensures better utilisation and resource management and reduction of wastages.
  • It minimises environmental risk, reduced contamination of ground water due to leaching of nitrates.
  • Farms and fields can be monitored and surveyed easily.
  • Soil characteristics and yields can be mapped and monitored.
  • It leads to increased agricultural productivity.
  • It prevents soil erosion and degradation.
  • Ensures reduced application of chemicals in crop production
  • It ensure water resources are used efficiently
  • Improved quality production
  • It leads to reduced costs of production.
  • The capital required to purchase and use technologies is high.
  • Data collection and analysis is a tedious work and time consuming.
  • Takes years to get sufficient data for system implementation.
  • New technology proliferation is slow.
  • Less compatible with people with less technological skills.
  • Precision Agriculture requires accurate work, information and attention.

Geographic information data can be divided into vector and raster, Raster data is one that is made up of pixels also referred to as grid cells. The pixels in most cases are square and spaced regularly. Rasters models, each pixel has its own value thus sometimes looks pixelated. Vector data model consist of points, lines or arcs, and polygons, lines have end points, which meet at nodes (Gisgeography.Com, 2021).

Remote sensing refers to process of collecting information about a feature or an object on the earth surface from far distance using aircraft or satellite through by measuring radiations emitted or reflected from the object (Borgogno-Mondino, Lessio, Tarricone, Novello, & Palma, 2018). Special cameras are fitted on the aircraft, and as the aircraft moves, the camera sense and collects information about the object of interest on earth. Ocean floor information can be collected using sonar systems fitted on the ships without physical collection, satellited fitted with cameras generate temperature images regarding ocean changes.

Spatial resolution, temporal and spectral are properties of remote sensing data. Spectral resolution  describes the width and the number of spectral bands in a sensor system.spectral resolution is divided into two, panchromatic that is made up of one band and multispectral that is made up of more than one band (Rajendra P. Sishodia, 2020).

Temporal resolution describes the frequency of data acquisition. It can be 24hours 16 days or more. Illuminations and atmospheric conditions indirectly influence the sensor platform, which in turn influence the signal outcome.

Remote sensing is an important tool in the society through various ways explained below, it’s evident that adopting remote sensing technique in farming can improve crop productivity and contribute economically stable societies (Candiago, Remondino, De Giglio, Dubbini, & Gattelli, 2015).

Crop growth and yield Monitoring – Understanding how crop respond to agronomic and environment practices are necessary so that the management can plan and come up with remedies early before destruction takes place. Using hyperspectral images crop residue and map tillage can be mapped, with the information the farmer can know the management plans specific to the activity, which includes, nutrient content needed for growth, variable water, and proper application of pesticides so that productivity is increased as well as efficient management (Chlingaryan, Sukkarieh, & Whelan, 2018).

Spatial variability knowledge in precision agriculture and crop yield information is important in understanding response of crops to environmental stress (Huang, Chen, Yu, Huang, & Gu, 2018). Remote sensing derived vegetation indices shows strong correlation with observed crop biomass and crop yield and biomass, this indicates the potential of remote sensing data in yield estimation (Rokhmana, 2018).

Weed Management -Weed management involves uniform application of herbicides to decrease weed infestation and decrease risk of pesticide losses. When herbicides are applied at variable rate needed, it leads to efficient treatment, reducing input costs as well as pollution to the environment. Remote sensing is used in mapping weed patches in crop fields, weeds and crops are differentiated based on their different spectral signature reflectance.

Disease Management -Crop diseases lead to significant low crop productivity and reduced farm profits. When plan diseases are detected early and their spatial extend identified, it helps in managing and mitigating spread of diseases which in turn reduce losses in production (Nagasubramanian, Jones, Singh, Sarkar, & Singh, 2019).

Traditional methos of diseases detection and management including field scouting is labour intensive, prone to errors and time consuming. It is also difficult to identify the crop diseases at early stages, because some diseases don’t show symptoms until later, when damage has been don, which at this stage difficult to manage (Shin & Mohanty, 2013). Spatial extent and disease severity is difficult to be measured using field scouting traditional. Regarding above, Remote sensing can be important tool in disease management. Remote sensing can efficiently monitor the disease during the early stage, when it is difficult to detect using field scouting. Remote sensing techniques that have been to identify disease in crops include red, blue and green bands (RGB) (Mulla, 2021), thermal, multi-spectral, fluorescence imaging and hyperspectral.

Nutrient Management Timely application of fertilizers is important in optimizing crop growth and crop yields while reducing environmental pollution attributed to nutrient release to groundwater. Recommended fertilizer application is at planting and growth stage. Mapping variabilities such as soil type, hydrology, topography and weather is difficult with use of traditional methods thus remote sensing is used to ensure that such variabilities are measures and nutrients are applied to crops uniformly.

Soil Moisture Remote sensing acquire data in multiple bands, including thermal, microwave and optical and they can be used to determine soil moisture. Soil moisture is estimated based on the interpretation of the pixel distribution collected in space (Zhou, Chen, Chen, & Xing, 2016).

Evapotranspiration (ET) Remote sensing is used to estimating Evapotranspiration, needed in determining the crop water requirements and schedule irrigation. Based on remote sensing data Evapotranspiration estimation approaches scan be grouped into; Penman–Monteith method ,surface and  crop coefficient (Courault, Seguin, & Olioso, 2011).

Water Stress According to (Maes & Steppe, 2012)remote sensing is used in testing indices for water management, using optical and thermal bands.

Irrigation Water Management Irrigation rate and application time is important in water stress mitigation in optimising crop growth and crop yield (Mendes, Araújo, Dutta, & Heeren, 2019).  Irrigation practices used by farmer is dependent factors including farm infrastructure, water availability, local and regional water laws,  farm size, economic status, farmers knowledge on irrigation type, cost among others. 

Conclusion

In conclusion, prior work review has provided great insights and extensive overview of the trend of remote sensing application in precision agriculture and crop production stages both temporally and spatially in the world. The review showed that studies majorly focused on satellite technology developed in China and Europe. The review has shown that Remote sensing technology is growingly being used to support management in making decisions in relation to, crop monitoring, stress monitoring, moisture content, fertilizers needed etc.

From review done, application of remote sensing in grain monitoring and soil compaction has not been explored much and calls for further investigation and work around the areas. Computer analytics and vision algorithms have provided high resolution imagery that provides opportunities of quantifying.

  • Crop spacing and emergence
  • Important crop features
  • Weeds identification
  • Weeds classification
  • Crop diseases identification

Existing remote sensing studies on yield assessment and nitrogen stress are based on empirical approaches. Therefore, further studies need to focus on anchoring remote sensing data with crop modelling so as to understand and make accurate crop dynamics forecasts. Thermal remote sensing is advantageous in detecting crop diseases early as compared to multispectral remote sensing.

With availability of remote sensors and platforms, it is inevitable that farmers need to develop their understanding of opportunities associated with adopting remote sensing in precision agriculture, so they ensure maximum farm productivity while reducing costs and issues related to farm data collection. Implementing remote sensing the farmers can quantify and identify the health of the crops, this will support them in taking timely actions and reduce problems affecting the crops, this in turn ensure increased farm profits. The hypothesis from the review is that precision agriculture technology is largely dependent on size of the farms, level of education of the farmers, the age of the farmers and the cost implications in adoption. The opinions of non-precision and precision farmers regarding the pros and cons of adopting precision agriculture can be distinguished clearly. 

References

Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., & Bochtis, D. (2019). Remote sensing techniques for soil organic carbon estimation:.

Awokuse, T., & Xie, R. (2015). Does agriculture really matter for economic growth in developing countries?

Bongiovanni, R., & Lowenberg-DeBoer, J. (2017). Precision agriculture and sustainability.

Borgogno-Mondino, E., Lessio, A., Tarricone, L., Novello, V., & Palma, D. (2018). A comparison between multispectral aerial and satellite imagery in precision viticulture.

Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 7, 4026–4047. .

Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture.

Courault, D., Seguin, B., & Olioso. (2011). A. Review on estimation of evapotranspiration from remote sensing data:.

Curtin, K. M. (2017). Network Analysis: Comprehensive Geographic Information Systems.

Curtin, K. M. (n.d.). Network analysis in geographic information science: Review, assessment, and projections. 2018.

Gebbers, R., & Adamchuk, V. (2010). Precision agriculture and food security.

Gisgeography.Com. (2021). Vector vs Raster: What’s the Difference Between GIS Spatial Data Types?

Goodchild, M. F. (2018). Reimagining the history of GIS: Annals of GIS.

Gutierrez, F. S.-C. (2020). Efficient processing of raster and vector data; journal.pone.0226943.

Huang, Y., Chen, Z., Yu, T., Huang, X., & Gu, X. (2018). Agricultural remote sensing big data: Management and applications.

Maes, W., & Steppe, K. (2012). Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture.

Mendes, R., Araújo, F., Dutta, R., & Heeren, D. (2019). Fuzzy control system for variable rate irrigation using remote sensing. Expert Syst.

Mulla, D. (2021). Trends in Satellite Remote Sensing for Precision Agriculture; Crops & Soils.

Nagasubramanian, K., Jones, S., Singh, A., Sarkar, S., & Singh, A. (2019). Ganapathysubramanian, B. Plant disease identifcation using explainable 3D deep learning on hyperspectral images.

Paramá, N. R. (2017). Efficiently Querying Vector and Raster Data.

Pilouk, S. Z. (2009). Trends in 3D GIS Development.

Rajendra P. Sishodia, R. L. (2020). Applications of remote sensing in precision agriculture: A review.

Roberts, C. R. (2020). An integrated environmental analytics system (IDEAS) based on a DGGS ;ISPRS Journal of Photogrammetry and Remote Sensing.

Rokhmana, C. (2018). The potential of UAV-based remote sensing for supporting precision agriculture in indonesia.

Say, M., Keskin, M., Sehri, M., & Sekerli, Y. A. (2018). Adoption of precision agriculture technologies in developed and developing countries.

Shin, Y., & Mohanty, B. (2013). Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications. W.

Vozenilek, M. B. (2020). Differences in thematic map reading by students and their geography teacher; SPRS International Journal of Geo-Information.

Weiss, M., Jacob, F., & Duveillerc, G. (2020). Remote sensing for agricultural applications:.

Zhou, L., Chen, N., Chen, Z., & Xing, C. R. (2016). An efficient remote sensing observation-sharing method based on cloud computing for soil moisture mapping in precision agriculture.

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