Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Abstract

Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management.

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…The benefits of precision agriculture [274], including through precision irrigation practices [1], result in higher production efficiencies and economic returns through site-specific crop management [275,276]. This approach has motivated the use of high-resolution imagery acquired from remote sensing to identify irrigation zones [99,277]. The first horticultural applications of UAS platforms for crop water status measurement were in orange and peach orchards where both thermal and multispectral-derived VIs, specifically the PRI, were shown to be well-correlated to crop water status [102].…”

Rampant Arch and Its Optimum Geometrical Generation

“…In order to analyze real existing cases, a centimetric precision survey would be needed. Although, currently, with the technology of unmanned aerial vehicles, it could be solved in a relatively economic way by realizing a photogrammetric survey [34]. In the literature, several examples can be found [35,36].…”

A Review on Applications of Remote Sensing and Geographic Information Systems (GIS) in Water Resources and Flood Risk Management

“…Water resources mapping and management Glaciers mapping Landsat, ASTER GDEM, GIS, TIN 3D model. [36] Soil moisture detection GPR, CMP, FO, GIS spatial analysis [16] Groundwater and subsidence analysis GIS spatial analysis, GPS [37] Irrigation planning UAV, HTM for video image classification, GIS visualization [32] Rainfall measurements and design storm…”

“…Therefore, knowing the turfgrass areas and growth conditions can help plan the water and treated sewage effluent needs exactly at a daily or weekly rate. Perea-Moreno et al [32] in this special issue utilized UAV video images to extract automatically the turfgrass areas and growth conditions by a Hierarchical Temporal Memory (HTM) algorithm, and further assess the water needs for turfgrass irrigation. The extracted turfgrass area from video imagery classification could achieve an accuracy of 98%.…”

“…Many drought indices were developed using MODIS reflectance data under different climate and land cover conditions, such as the Normalized Difference of Vegetation Index (NDVI), Normalized Difference of Water Index (NDWI), Visible Atmospherically Resistant Index (VARI), Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index Band 6 or Band7 (NDIIB6/7), and so on [29][30][31]. Emerging Unmanned Aerial Vehicles (UAV) provide a more flexible low-altitude platform to monitor vegetation growth, soil moisture conditions, flood inundation mapping and damage assessment [32]. Flood mapping from various data sources can greatly improve disaster response, e.g., for the widespread and sustained flood events in several river basins in Texas and Oklahoma of USA in late April and May 2015, a total 27,174 space-and airborne images were applied to monitor the daily variations of flood inundation extents [33].…”