Question

Please don't leave any questions to answer. If you are waiting for great feedback then that should be me. This questions are from surveying and spatial science

a). You are working in the Environmental Management section of a Local Government Council. Your Local Government area includes approximately 100 km of coastline, comprising a mix of beaches, coastal dunes, rocky shores and headlands. Some regions of the coastline are considered highly vulnerable to the effects of climate change. Along the coastline there are beachside communities, agricultural activities and natural bushland.

Carefully consider the environmental monitoring that may need to be undertaken in order for your Council to measure and manage the impacts of climate change. List and describe the surveying and spatial technologies and methods that might be required to collect, manage and analyse the spatial data required by the Council.

b). You have been employed by a company that provides services to the farming/agriculture sector: including grazing (cattle and sheep), broad acre cropping (grains), and intense, high-value horticulture (irrigated crops such as lettuce, cherries, stone fruit). The company has identified spatial technologies as an emerging opportunity to provide new services to their clients. List and describe four examples of how different spatial technologies can be applied to farming/agricultural business

Answer 1

Answer 1- surveying and spatial technologies and methods that might be required to collect, manage and analyse the spatial data required by the Council in coastal area are as follow-
As the intersection of land and sea, coastal zones are complex and variable. The traditional means of coastal zone research have certain limitations. Both the monitoring means and the monitoring intensity struggle to meet the demand of real-time monitoring due to coastal zone development, environmental changes, and disasters. Among the modern methods for monitoring terrestrial ecosystems, remote sensing is of primary importance due to its ability to provide synoptic information over wide areas with high acquisition frequencies .
1 . Remote sensing- Remote sensing has been used in resource development, the planning and management of the coastal zone, the monitoring of shoreline changes, and the understanding of physical processes in the coastal environment with geographic information systems (GIS )
Remote sensing technology acquires and records information without coming into direct contact with an object. Remote sensing was redefined as the science and technology of Earth observation, including space to Earth observation, aerial observation, and field monitoring. From this perspective, remote sensing data can be divided into data on a global scale, a regional scale, and a local scale.
Global-scale satellites include static meteorological satellites, such as the GOES-8, the GOES-10, and the GMS meteorological satellite. The GOES-8 and the GOES-10 are the stationary satellites of NOAA. Their purposes are daily weather observations. These satellites provide a variety of meteorological and nonmeteorological services and play an important role in the study of global climate change, weather forecasting, disaster prevention, and disaster reduction.
Regional-scale data are generally obtained from moderate-resolution remote sensing images, such as MODIS sensor and Landsat satellites. MODIS is an important sensor equipped with Terra and Aqua satellites. Its multispectral data can reflect the information of land surface condition, ocean colour, phytoplankton, biological geography, chemistry, atmospheric water vapour, aerosol and surface temperature, atmospheric temperature, and so on. Launched by the United States, Landsat satellites are primarily used to capture the remote sensing images of land, including soil organisms and plants. These satellites provide accurate and dynamic geographical information sources. Regional-scale data can be used in the macroscopic analysis of coastal zone changes.
Local-scale satellites are usually used for monitoring in a smaller scope with high resolutions, such as worldview satellite, airborne satellites, and unmanned aerial vehicles (UAVs). The benefits of high spatial and high spectral resolution data are their ability to match the rich spectral and spatial diversities observed in coastal systems. For example, The invasion of Spartina alterniflora was monitored using very high resolution UAV imagery in Beiha. The results showed that UAV imagery can provide details on the distribution, progress, and early detection of S. alterniflora invasion, and the total accuracy was 94.0%.
Based on the spectral types, satellites can be divided into optical satellites and microwave satellites. Hyperspectral and multispectral data provide more information for identifying targets. Unlike optical satellites, microwave satellites can penetrate through snow, soil, and forest. The benefit of combined optical and SAR-based approaches in improving classifications over some coastal habitats was demonstrated.
Currently, remote sensing data are used in various ways: they serve as input boundary conditions and validation data for numerical simulation models, they are combined with in situ measurements to draw sediment transport maps, and they are assimilated in 3D coastal sediment transport models and in the light forcing of an ecosystem model.
2 . Geographical information system- GIS is an important and specific spatial information system. It is the technical system for the collection, storage, management, operation, analysis, display, and description of geographic distribution data for the entirety or a part of the Earth’s surface (including the atmosphere) in support of computer hardware and software systems. GIS has spatial analysis capabilities, can store and manage vast amounts of complex spatial data and attribute data, and can use spatial databases for the comprehensive analysis of multiple factors with quantity, quality, and localization. However, it is difficult to achieve attribute data modelling relying solely on GIS software.
Combining GIS and mathematical models can make modelling easier by improving accuracy and solving problems effectively. For example, Anthony et al. used GIS with fuzzy learning vector quantization (FLVQ) for coastal vegetation classification. The classification accuracy of FLVQ was comparable to a conventional supervised multilayer perceptron, trained with backpropagation (KHAT accuracy: 82.82% and 84.66%, respectively; normalized accuracy: 74.60% and 75.85%, respectively), with no significant difference at the 95% confidence level .An eutrophication model for Bohai Bay based on a cellular automata-support vector machine (CA-SVM) was established in by applying the soft computing approach with a large quantity of remote sensing data to the marine environment. Their comparison between the optimized model and the basic model indicated that prediction accuracy was improved by the optimized model. The spatiotemporal patterns of phosphorus concentrations in a coastal bay were explored with machine learning models in . GIS spatial analysis methods, along with mathematical models, can be used to analyse the interaction between coastal zone changes and the effects of human activities. These methods are very effective for coastal zone monitoring.
Various Methods-
Landscape classification-
Remote sensing image classification is an important way to extract information. Different surface features have different spectral characteristics. Landscape classification is the clustering of the same or similar pixels and the assigning of value to each pixel class through the analysis of spectral characteristics based on satellite remote sensing images.
Landscape classification is a complex data processing task. Many factors, such as the spatial resolution of remote sensing images, different sources of data, and different classification methods, may influence the accuracy of landscape classification. The selection of the classification method is critical. Usually, classification methods can be divided into supervised classification approaches and unsupervised classification approaches. Supervised classification approaches identify the class of each pixel by selecting typical and representative training samples that the types are known already and training the classifier to classify the spectral data with the spectral characteristics of training samples. There are many classifiers, such as maximum likelihood, minimum distance, the SVM, the artificial neural network (ANN), and the decision tree. Unsupervised classification approaches are used to partition a spectral image into a number of spectral classes based on the statistical information inherent to the image. Unsupervised classification approaches merge spectral classes into meaningful classes based on the size of the similarity between pixels instead of training samples. The methods of unsupervised classification include, for example, the k-means algorithm, the iterative self-organizing data analysis method (ISODATA), and fuzzy clustering .

Regional scale: Automatic classification and change detection
On a regional scale, the data most frequently used in landscape classification and change detection are moderate spatial resolution remote sensing images, such as land-resource satellite data. Some traditional landscape classification methods require considerable labor and prior knowledge, and accuracy cannot be guaranteed. To reduce human intervention to a minimum and to achieve accurate classification rapidly, An efficient automatic landscape classification approach using prior accurate land-cover data as the background experience was proposed in. This approach is distinguished from the previous semisupervised findings of landscape classifiers by adopting prior knowledge. It can be simply described in two steps: (1) detecting landscape changed pixels from satellite images compared with a prior landscape map and (2) classifying the landscape of changed pixels based on pattern recognition and changed rules.
Automatic collection of training samples
The first phase of this method is sampling, with a purpose of obtaining pure pixels of landscape classes. The selected training samples were used to ultimately convey the information to a three-dimensional feature space. The automatic collection of training samples is illustrated in Fig. 1.
Diagram of automatic collection of training samples.
Establishment of three-dimensional feature space
Principal component statistical analysis was used to process the data in all spectral bands of each landscape class, extracted by the region of interest. The first three principal components were selected for orthogonal decomposition to construct the three-dimensional feature space of different landscape classes. The three-dimensional spectral feature space of different landscape classes is established in the Fig 2.
Spectral feature spaces of typical landscape types.
Change detection and classification
For each landscape class, all extracted cell spectral data of the same landscape class were used to calculate the values of the corresponding feature space. The pixels outside the corresponding three-dimensional feature space were considered the changed areas. After the changed pixels of the landscape were obtained, the satellite images and the three-dimensional feature space were employed to classify them based on pattern recognition and changed rules.
Modification of post classification results
There is an inevitable problem that a few individual pixels are inconsistent with the surrounding landscape class. The modification of post classification results solved this problem by dilating operation and eroding operation on the classified results. The “salt-and-pepper” error is also a common problem. A moving window with a size of 3×3 pixels was defined to eliminate noise.
Accuracy assessment and time-consuming evaluation
A kappa coefficient and a time-consuming evaluation were adopted to evaluate the practicability of classification. A kappa value equal to or greater than 0.61 was considered to be in good agreement. According to the requirements, a less time-consuming combination of Pa [accumulation area threshold (Pa) index] and Pbuffer (area threshold for buffer analysis) or a combination with a time-lapse rate minimum would form the optimal combination model.

Local scale: Object-oriented classification with high-resolution image data
With the improvements in satellite sensor resolution, high-resolution remote sensing imaging has played an important role in relevant application fields. High-resolution remote sensing images contain rich information, such as spectral information, shape information, and texture information. High-resolution remote sensing images can clearly capture fine features and changes, such as port construction, roads, ecosystem characteristics, and species distribution (e.g., mangroves and invasive plant distribution). Pixel-based classification approaches are primarily based on the spectral information of pixels to extract feature information, and they do not make full use of rich spatial information (shape information and texture information) in the process of classification. According to the characteristics of high-resolution images, an object-oriented classification method was proposed in, which makes full use of spectral, shape, and texture information and obtain a high precision of classification results. Object-oriented classification is based on image segmentation to obtain a homogeneous image object and then analyze the spectrum, shape, and texture features to classify and extract the feature information. The main phases of classification are shown in the Fig 3
Object-oriented classification.
Object-oriented remote sensing image classification includes two key steps: multiresolution segmentation and classification.
Multiresolution segmentation
The multiresolution segmentation region grows and merges the algorithm and minimizes the average heterogeneity of image objects. The multiresolution segmentation algorithm consecutively merges pixels or image objects and is thus a bottom-up segmentation algorithm based on a pairwise region-merging technique . Multiresolution segmentation algorithms include three factors: the band-weighting factor, the heterogeneity factor, and the segmentation scale. As the segmentation scale increases in size, the objects grow larger. Heterogeneity is composed of spectroscopy heterogeneity, shape heterogeneity, spectral weight, and the shape weight of four variables.
f=wl×hcolor+(1−wl)×hshapef=wl×hcolor+(1−wl)×hshape
where hcolor is spectroscopy heterogeneity, hshape is shape heterogeneity, wl is spectral weight, and 1-wl is shape weight.
The multiresolution segmentation is the process of merging image objects in several loops in pairs starting with a single image object of one pixel. In each loop, the single image object acts as a seed to find its best-fitting neighbor in four directions or eight directions, as shown in Fig 4. If the best neighbor’s best-fitting neighbor is the seed, then the two pixels are merged into an image object. If otherwise, the best neighbor as a new seed begins to look for its own best-fitting neighbor until a pair of best-fitting image objects are found. The loops continue until the heterogeneity of each pair of image objects is greater than the segmentation scale
Multiresolution segmentation
According to the extracted object, the proper parameters are chosen to achieve the optimal segmentation result with scale 75, shape 0.1, and compactness 0.5 as shown in Fig. 5.
Feature selection
Feature selection is to select the spectral features, shape, and texture features for classification through the analysis of feature information, see the analysis result of feature information in Fig. 6. According to the classification targets (landscapes in Table 4), some features were selected and proper thresholds were set, such as normalized difference vegetation index (NDVI), mean, shape index, and brightness.
Classification result
As shown in Fig. 7, the landscape mainly consists of aquafarms, water area, forest land, industrial land, cultivated land, residential area, and construction land. The classification result is reasonable through comparative analysis between the results and the original image.
Result of classification.
The object-oriented classification method we used in this study shows a good performance with the total accuracy of 89% and the kappa coefficient of 0.83. This method provides a high precision and a reasonable classification result.
Retrieval of eco-environmental parameters based on remote sensing technique
The parameters of land surface energy and water balance are important inputs for research on global climate change, crop yield assessment, and ecological environment evaluations. Remote sensing-derived radiation, temperature, and other data sets on a global scale have been used as standardized products in research and applications. Remote sensing information is convenient and easy to access over a large area at a low cost.
Energy- and water-balance parameters: solar radiation and Evapotranspiration (ET)
Solar radiation
Solar radiation is the Earth surface’s most basic and important source of energy as well as the main driving factor of plant photosynthesis, transpiration, and soil evaporation land-surface processes . Solar radiation is responsible for the formation and evolution of important climate driving forces. Changes in solar radiation change the temperature, humidity, precipitation, atmospheric circulation, the hydrological cycle, and other processes. Solar radiation is an important physical and ecological parameter in the land surface and atmospheric energy exchange process. Accurate solar radiation data retrieved from satellite data help improve net radiation, ET, and other precision products.
Geostationary meteorological satellite data have often been adopted as the data source for solar radiation and ET retrieval. GMS-5 data are easily acquired with a relatively high temporal resolution (1 h). GMS-5 data consist of three types of bands: (1) the visible (VIS) band with a spatial resolution of 1.25 km and a spectrum range from 0.55 to 1.05 μm, (2) the thermal infrared (TIR) band with a spatial resolution of 5 km and a spectrum range from 10.5 to 12.5 μm, and (3) the water vapor (WV) band with a spatial resolution of 5 km and a spectrum range from 6.2 to 7.6 μm. The VIS and TIR bands were employed for ET retrieval, and the WV band was used for calibration and validation.
Net radiation was calculated as the net result of the short-wave (solar) and long-wave (terrestrial) radiative fluxes, and it is expressed as a daily average:
In=(1−a)Ig+LnIn=(1−a)Ig+Ln
where a is a surface albedo, which could be derived from the VIS data of the GMS-5; Ig is the daily average solar irradiation at the Earth’s surface; and Ln is the net long-wave (thermal) radiation loss.
Figures 8 and 9 show the spatial distribution of the global and net solar radiation of Asia in January, April, July, and October 2004, respectively.
Solar radiation is an important factor in maintaining the Earth’s climate system and the ecosystem’s energy balance and is the main source of energy in the ecosystem, which plays an important role in the process of human development. Many environmental changes are related to solar radiation . The use of satellite remote sensing data, particularly the stationary meteorological satellite data for observing a fixed surface area with the temporal resolution of 1 h, can greatly compensate for the lack of ground observation data. It is important to obtain long and continuous region surface solar radiation to analyze solar radiation’s spatial and temporal variation of the surface area. The analysis of surface solar radiation and climate change research will thereby be greatly facilitated.
ET
Evapotranspiration (ET) is usually understood to be the sum of soil evaporation and plant transpiration, which is a soil-plant-atmosphere continuum system and an important process (SPAC) in water movement. ET is essential to crop growth and the development of water and energy sources. Ecological systems are an important link between land surface and hydrological processes, which are closely related to the strength and size of the underlying surface of plants. ET can be said to be both a surface heat-balance component and an important part of water balance. ET is highly variable across space due to changes in precipitation, soil hydrological parameters, and vegetation type and density. Its strong spatial variability across time is due to climate changes at different times.
Calculation of the sensible heat flux: The sensible heat flux into the atmosphere is proportional to the temperature difference across the atmospheric boundary layer (T0-Ta). The simple formulation is:
H=(ac+ar)(T0−Ta)H=(ac+ar)(T0−Ta)
where ac stands for surface resistance and ar stands for the resistance of the atmosphere.
Having determined the net radiation (In) and the sensible heat flux (H), the latent heat flux (i.e., the actual ET in energy units) can be obtained based on regional energy and water balance:
LE=In−H−GLE=In−H−G
Item G is the heat flux into the soil, which is very small on the daily time scale, and may be considered a constant.
Figure 10 shows the spatial distribution of the ET of Asia in January, April, July, and October 2004 and reveals the significant differences between the distributions of different months of relative ET space. Relative ET was higher at low latitudes than at high latitudes and varied across months. Due to the lush plant growth in July, relative ET is generally high in most of Asia. In January, in most of Asia, including the South and Southeast Asian countries, there are low temperatures and generally low relative ET.
Many ecosystem processes, such as soil moisture changes, vegetation growth, and nutrient cycling, are closely linked to the ET process. The ET process is affected by climate, soil characteristics, and vegetation growth status . Therefore, the calculation of ET reveals the changes in the time of ET and its impact factors to quantify the contribution of plant transpiration and soil evaporation to ET. ET can not only reveal various land-surface (vegetation, particularly surface) water consumptions [33] but also help us understand the effect of global warming on the actual ET and water balance, land surface ecology, and environment. ET can also improve climate models and accurately simulate climate change, which is important.

Factors of coastal environment analysis
Coastal zones and shelf seas are influenced by both natural and socioeconomic conditions. An integrated evaluation criteria system was set up, which contained nine factors belonging to three categories: (1) environmental background factors, including elevation, slope, geomorphological types, accumulated temperature, and a wetness index; (2) water/land resources, including precipitation, river density, and land use; and (3) socioeconomic factors, including railway density, road density, and population density. However, human life and development may also directly or indirectly affect costal zones and shelf areas, including mariculture and marine fishing production, sea pollution, and others (e.g., building, sports, and travel).

Answer 2 - Different spatial technologies which can be applied to farming or agriculture business are as follows
Global Positioning Systems
Geographic Information Systems
Variable-rate Application
Remote Sensing

1 . Global positioning system- Global positioning systems (GPS) use 24 satellite signals to define positions on the earth. The satellites circle the earth twice a day in six orbital paths. GPS can be used to georeference soil samples, tillage, planting, spraying, scouting, and harvesting. For example, GPS is used to georeference soil sampling and for tractor navigation. The hardware required for GPS includes a GPS receiver and antenna, a differential correction signal receiver and antenna, and an interface.
Soil sampling- Soil sampling is another important part of site-specific farming. soil properties, such as texture, organic matter content, and landscape geomorphology, have a considerable influence on the productivity of soils. A soil inventory needs only to be taken once for the variation assessment of these time constant properties. This soil inventory can be done properly by soil surveying, which combines GPS with the human sensory capability. Self-surveying is remarkably appropriate for getting the basic soil information needed at a low cost.
2 . Geographic information system- Geographic information systems (GIS) are used to input, store, retrieve, analyze,and display geographic data. The hardware needed for GIS includes a computer, high-resolution display monitor, digitizer/scanner, plotter/colour printer, and a floppy disk drive. The software consists of a user interface, data input, data storage, data output, and data transformation.
maps generated from using GIScan be used to evaluate site-specific farming applications. Provincial Survey aerial photographs and GIS spectrographic software to generate maps to delineate management zones according to soil colour. Provincial Survey aerial photographs and GIS spectrographic software can be used to generate maps to delineate management zones according to soil colour. areas with low fertility and poor drainage, could be identified and predictions could be made about where problems are likely to occur so solutions could be found.
3 . Variable rate application- Variable-rate application (VRA) requires controllers, which change the application rate on the go, and actuators, which respond to the controller to regulate application. Variable-rate applications include applications of fertilizer, pesticides, and seed. The two methods of VRA are map-based and sensor-based. Map-based VRA requires GPS, GIS, and software for producing the maps.
Snyder, et al. (1999) examined the economics of site-specific nitrogen management for irrigated corn in Kansas. Comparisons were made of uniform and variable nitrogen management using both soil sampling and detailed yield maps. Using data from both uniform and variable rate experiments, a quadratic function was estimated for each site and each year. Results indicated that there is potential for profitable use of precision nitrogen management. Variable rate technology for nitrogen application used less nitrogen than the uniform management. For that reason, it was determined to be more profitable in some years while not in others.
4 . Remote Sensing- Remote sensing measures energy reflected and emitted from objects without actually coming in contact with the objects. Four properties of remote sensing data that should be considered when using remote sensing systems include spatial resolution, spectral response, spectral resolution, and frequency of coverage. Spatial resolution refers to the size of the smallest grid cell of the imagery. Spectral response is the sensing system’s ability to collect and respond to radiation in a spectral band. Spectral resolution is the ability of a sensing system to distinguish between different wavelength’s electromagnetic radiation. Frequency of coverage, also known as temporal resolution, refers to how often a sensing system is available for data collection at a particular ground site.
The two main remote sensing platforms are aircraft-based and satellite-based. Aircraft-based systems provide images with higher spatial and spectral resolution than data from satellite-based systems. They are also easier to maintain and have the ability to provide images with a higher resolution. They can sense areas when conditions are optimal. In contrast, satellite-based platforms are in fixed orbits. Cost varies according to image type, image size, and level of processing.
Remote data are collected by using sensors on airplanes or satellites to detect electromagnetic radiation emitted or reflected from objects on the ground. More formally defined, remote sensing involves obtaining physical data about an object without being in contact with the object. By using the data acquired through remote sensing, farmers are able to obtain detailed spatial information about features in their fields to assist management decisions.
Remote sensing can be used to measure soil and crop characteristics. In particular, images produced from remote sensing can help farmers with planting, fertilizing, irrigating, as well as other management decisions in the field.