Review of Related Literature
Review of Related Literature Geometric Characterization of Tree Crops
In the field of agriculture, the structural aspects of a canopy are crucial at different levels (i.e. individual tree, crops, forest and ecosystems). Phattaralerphong and Sinoquet (Phattaralerphong & Sinoquet, 2005) indicated that the space occupied by tree foliage determines the potential for resource capture and for exchanges with the atmosphere. Plant structure influences most biophysical processes, including photosynthesis, growth, CO2-sequestration, and evapotranspiration (Li, Cohen, Naor, Shaozong, & Erez, 2002; Pereira, Grenn, & Villanova, 2006), etc. At the forest level, structure plays a key role in processes involving exchanges of matter and energy between the atmosphere and terrestrial above ground carbon reserves (Van der Zande et al., 2006).
Most of the work conducted to date has been related to forest areas (Lefsky et al., 2002; Parker et al., 2004; Maas et al., 2008; Kushida et al., 2009). However, in the field of agriculture, obtaining three-dimensional (3D) models of trees and plantations open an immense and novel field of applications. However, the geometric characterization of trees is both a relevant and complex task (Sanz-Cortiella et al., 2011a,b). It is relevant because tree canopy geometric characteristics are directly related to tree growth and productivity, and hence can be indicators for tree biomass and growth estimations, yield prediction, water consumption estimation, health assessment, and long-term productivity monitoring (Lee and Ehsani, 2009). Canopy characteristics supply valuable information for tree-specific management reducing production costs and public concerns about environmental pollution. Thus, there is a whole range of key agricultural activities including pesticide treatments, irrigation, fertilization and crop training which depend largely on the structural and geometric properties of the visible part of trees.
It is a complex task because the thousands of elements that form trees (i.e. trunks, branches, leaves, flowers and fruits) are difficult to measure. There are essentially three reasons for this: (i) the large number of elements to consider, (ii) their location in a relatively small 3D space, which implies that some elements will always be partially or totally hidden, regardless of the view angle adopted and (iii) the geometric complexity of all these elements (Zheng and Moskal, 2009). At present a number of research groups are conducting research into a variety of non-destructive techniques for the measurement of the tree canopy structural characteristics, such as volume, foliage and leaf area index. This can be achieved by different detection approaches, such as image analysis techniques, digital stereoscopy photography, analysis of the light penetration in the canopy, ultrasonic sensors and laser scanning techniques, among others.
The capacity of LiDAR to quantify spatial variations, which is an important aspect of vegetation structure, is a significant advance over some previous methods. LiDAR systems can be used to quantify changes in canopy structure at various time scales. They can provide detailed assessments of canopy growth and allocation responses to field experiments including fertilization, irrigation, soil warming and fumigation. Laser technology offers unique options in terms of the viewing angle and distance information needed to model canopy structure; hence, there is an emerging technique to thoroughly investigate LiDAR structural applications (Van der Zande et al., 2006).
Most of the work carried out to date has focused on forestry. However, 3D models may also be valuable for agricultural landscapes, with some applications being similar to those used in forest areas and others being specific to agricultural subjects. Due to their different characteristics, some techniques suitable for agricultural crops are difficult to apply to forest plantations. One basic difference relates to the accessibility to the zones of study for people and vehicles. Forest areas are often difficult to access for people and especially for vehicles. On the other hand, the transit of both people and machinery within agricultural plantations is guaranteed in most cases. This is highly relevant as it largely determines the kinds of instrumentation that can be used in each case. This explains the use of 3D LiDAR sensors in ground-based laser studies for forest applications. The main advantage of using these sensors is that they provide a 3D point cloud of the object being measured. However, the high cost of these instruments limits their use (Rosell et al., 2009a).
In agricultural applications, it is, however, possible to use 2D terrestrial LiDAR sensors, which are much cheaper to use (Walklate et al., 2002; Palacin et al., 2007). 2D LiDAR sensors obtain a point cloud corresponding to a plane or section of the object of interest. The fact that these sensors only scan in one plane does not necessarily limit their scope to 2D perception (Rovira-Mas et al., 2006). Sensor position, when well-determined (for example, with a constant, known-speed, linear movement – that can be achieved easily in the case of agricultural plantations – or when using high precision GPS georeferencing), allows the recording of measurement results corresponding to different planes or cross sections of an object, generating a 3D point cloud. Rosell et al. (2009a,b) proposed the use of a 2D LiDAR scanner in agriculture to obtain 3D structural characteristics of plants. Their results, obtained for fruit orchards, citrus orchards and vineyards, showed that this technique could provide fast, reliable, and non-destructive estimates of 3D crop structure. They concluded that LiDAR systems were able to measure the geometric characteristics of plants with sufficient precision for most agriculture applications. The system developed made it possible to obtain 3D digitalized images of crops from which a large amount of plant information-such as height, width, volume, leaf area index and leaf area density-could be obtained (see Figure 4.1).
As regards to the accuracy of the measurement, Palacin et al. (2007), who carried out real-time tree-foliage surface estimations using a ground laser scanner, concluded that the relationship between the external volume of the tree and its foliage surface could be considered linear with an average relative error of less than 6% in estimations for a complete grove, though trunks tended to cause instantaneous relative errors of up to 93% in the lower parts of trees. The same authors (Pallejà et al., 2010) analyzed the sensitivity of the tree volume estimates in the spatial trajectory of a LiDAR relative to different error sources. They demonstrated that the estimation of the volume is very sensitive to errors in the determination of the distance from the LiDAR to the center of the trees (with errors up to 30% for an error of 50 mm) and in the determination of the angle of orientation of the LiDAR (with errors up to 30% for misalignments of 2%). They concluded that any experimental procedure for tree volume estimate based on a motorized terrestrial LiDAR scanner must include additional devices or procedures to control or estimate and correct these error sources. Wei and Salyani (2005) developed a laser scanner for measuring tree canopy characteristics and concluded that laser density measurements offered a good degree of repeatability, with an average coefficient of variation (CV) of less than 3% for three replications.
The information about the geometric properties of plants provided by these (LiDAR laser scanners and stereo vision systems) techniques has innumerable applications in agriculture. Some important agricultural tasks that can benefit from these plant-geometry characterization techniques are the application of pesticides, irrigation, fertilization and crop training. In the field of pesticide application, knowledge of the geometrical characteristics of plantations will permit a better adjustment of the dose of the product applied, improving the environmental and economic impact.
Multi-source Land Cover Classification
LiDAR generates spatial information dimension that provides explicit geometric information about the structure of the Earth’s surface and super-imposed objects. An example is an airborne survey conducted over a Mediterranean site south of Aix-en-Provence (Koetz et al., 2008). The covered site comprised typical Mediterranean vegetation intermixed with urban structures.
The airborne survey was organized to cover a region of about 13.6 km x 3.6 km in very high spatial resolution. In the presented pilot study of Koetz and coworkers (2008), only a subset is exploited (see Figure 4.2). However, as the subset is considered representative for the region in terms of main land cover types, thus, the land cover classification could be extended to the total surveyed area.
Assessment of Surface Patterns, Vegetation, and Habitats
Boreal mires encompass high diversity in species and habitats, many of which are endangered. Aerial LiDAR data, through their proven ability to probe the geometric and radiometric properties of all vegetation layers, could be used for retrieving the key taxonomic features of pristine mire site (Korpela et al., 2009). To illustrate this potential, Korpela and coworkers (2009) conducted experiments in a complex minerotrophic–ombrotrophic eccentric raised bog in southern Finland.
Lakkasuo mire is an eccentric raised bog in southern Finland (610470N, 24.18’E, 145–160 m a.s.l.). It is a diverse mire complex containing a large proportion of mire site types in southern Finland extending over an area of 1 km x 2 km. In 1961, 40% of Lakkasuo was drained for forestry. Ombrotrophic sites constitute 56% of the pristine part and the remainder is minerotrophic, fed by springs and subsurface runoff (Fig. 3). The pristine mire types include all three main types: treeless mires, genuine forested mire types and sparsely forested composite site types (Laine and Vasander, 1996). In all, 21 mire site types are found in Lakkasuo (Table 1 below).
First, discrete-return LiDAR was tested for the modeling of mire surface patterns and the detection of hummocks and hollows, as well as the effect of mire plants on the Z accuracy of the surface echoes. Secondly, the response of different mire vegetation samples in LiDAR intensity was examined. Thirdly, area-based geometric and radiometric features in supervised classification of mire habitats to discover the meaningful variables were evaluated. The vertical accuracy of LiDAR in mire surface modeling was reported high: 0.05–0.10 m. A binary hummock-hollow model that was estimated from a LiDAR-based elevation model matched flawlessly in aerial images and had moderate explanatory power in habitat classification trials. The intensity of LiDAR in open-mire vegetation was mainly influenced by the surface moisture, and separation of vegetation classes spanning from ombrotrophic to mesotrophic vegetation proved to be difficult. Actual qualifying differences in the ground flora were unattainable in the LiDAR data, which resulted in inferior accuracy in the characterization of ecohydrological conditions and nutrient level of open mires and sparsely forested wet sites. Mire habitat classification accuracy with LiDAR surpassed earlier results with optical data. The results suggested that LiDAR constitutes an efficient aid for monitoring applications.
Significance of LiDAR Systems
Obtaining a precise geometrical characterization of a crop at any point during its production cycle by means of a new generation of affordable and easy-to- use detection systems, such as LIDAR and stereo vision systems, will help to establish precise estimations of crop water needs as well as valuable information that can be used to quantify its nutritional requirements.
If accurate, this can provide valuable information on which to base more sustainable irrigation and fertilizer dosages. These would be able to meet crop needs and could also be used as part of specific management systems, based on prescription maps, for the application of variable quantities of water and fertilizers.
In the field of agriculture, the availability of measurement tools that allow a precise geometric characterization of plantations, such as the LiDAR system, will facilitate and enhance research aimed at developing: (i) better crop training systems that ensure an optimal distribution of light within the treetops and higher fruit quality; (ii) better employment of different methods for the protection of crops against pests and diseases; (iv) proper scheduling and utilization of fertilizers; and (v) appropriate irrigation system. Thus, it is of vital importance to continue devoting major efforts to the development of increasingly accurate, robust and affordable systems capable of measuring the geometric characteristics of plantations, which support the development of the different areas of a sustainable and precision agriculture.
Applications for Pest and Disease Control
Despite of the recent advances in the employment of different methods for defending crops against pests and diseases, the use of plant protection products (PPP) continues to be an essential strategy for addressing the qualitative and quantitative demands of the food market. In recent years, growing environmental awareness, together with social concern to preserve the health of people and animals, has led to important legislative measures to minimize risks associated with the use of PPP. Adjusting the PPP dose to the structural and morphological characteristics of the vegetation is recognized at European level as an essential goal in the path towards reducing risks associated with the application of pesticides. The spraying equipment that is currently most used in fruit growing is hydraulic and air assisted. This offers greater product penetration into the vegetation and produces a uniform deposition within tree canopies. The use of new technologies allows us to detect the structural characteristics of vegetation and thereby to select and apply more appropriate broth volumes.
These techniques can also be used to achieve an acceptable control of air speed and flow and the most appropriate orientation of the air outputs, thereby reducing the risks associated with the use of PPP. Their application can also help to reduce the amount of product that reaches, and pollutes, ground, air and/or surface water. The development of automatic equipment capable of making a variable rate application, according to the characteristics of the vegetation, has proved a good solution for saving PPP and reducing the risk of environmental contamination. This requires the use of sensors capable of quickly, accurately and reliably identifying these characteristics, such as ultrasonic sensors (Giles et al., 1988; Escolà et al., 2001; Moltó et al., 2001; Solanelles et al., 2006; Llorens et al., 2010) or detection systems based on LIDAR sensors (Walklate et al., 1997, 2002; Sanz et al., 2004; Rosell et al., 2009a,b; Sanz-Cortiella et al., 2011a,b).
The choice of the most appropriate application doses of PPP is a fundamental consideration in modern agriculture. The value afforded to the environment today is not the same as it was several years ago. Choosing the dose to apply in each treatment is a difficult task because it is necessary to consider opposing interests. On the one hand, the dose must be sufficient to control the pest in all parts of the 22 plant and on the other it should be as small as possible so as to cause little or no environmental impact. The geometric characterization of trees provides fundamental data that can be used to minimize the environmental impact of the application of pesticides.
The most common expression of the application dose that appears on the labels of existing products involves the amount of product applied per unit of ground area occupied by the crop (l·ha -1 ). This method is appropriate in the case of boom sprayers for the treatment of low-growing crops, where the target is uniform, parallel to the ground and located just below the boom. In contrast, the application of plant protection products to tree crops is made at the treetop level with the assistance of air. Under these conditions, the deposition of the product on trees, following the recommended dose given on the product label (RDPL), will vary according to tree size. To alleviate this problem and ensure the effectiveness of the product, manufacturers tend to increase the margin of error in the RDPL (Russell, 2004).
Precision agriculture is currently helping to extend the methods currently being used in relation to pesticide treatments. This raises the potential for developing more precise PPP applications that comply with the environmental guidelines set out by the European Union (COM, 2009) and a number of other countries. In this section we refer to various studies being conducted with ultrasonic sensors and LiDAR sensors, as they seem to be the most promising with respect to target-sensing pesticide application.
The performance of a prototype electronic sprayer was first tested by Giles et al. (1988). The system was based on ultrasonic range transducers mounted on an orchard air-blast sprayer. Subsequent applications focused on interrupting the spray output when there was no vegetation (Gil et al., 2007). In the field of variable application of pesticides in citrus orchards using ultrasounds, Moltó et al. (2001) designed a prototype machine that applied one of two different doses according to the shape of the trees concerned: a higher doses at the center of the tree, and lower doses to its outer parts. In this case, ultrasonic sensors determined the locations of these two zones (center and exterior).
Based on initial work by Rosell et al. (1996) and Escolà et al. (2001), Solanelles et al. (2006) developed a prototype for an electronic control system based on ultrasonic sensors and proportional solenoid valves. This system allowed the authors to constantly vary the pesticide doses applied to the tree in accordance with the size of the vegetation. The aim of this prototype was to precisely apply the required amount of spray liquid and to avoid over dosing. In recent trials with vineyards Llorens et al. (2010) achieved a mean saving of 58% in the volume applied with the variable rate method and achieved good leaf deposits. The main disadvantages of ultrasonic sensors are their low resolution and accuracy; this implies that many units are required to cover a common agricultural scene. The angle of divergence of LIDAR sensors is much smaller than that of ultrasonic sensors.
The higher resulting resolution means more measuring points which, in turn, provides a more accurate representation of the vegetation. It also implies a greater ability to penetrate vegetation. Measuring trees with LIDAR and ultrasonic sensors must take into account the impossibility of measuring distances to elements that are hidden behind others. In order to optimize PPP treatments, Walklate (1989) and Walklate et al. (1997) began a mathematical development to determine the structural parameters of tree crops based on data supplied by a LIDAR measurement system. Walklate et al. (2002) subsequently completed this mathematical development, enabling it to estimate the TAI and TAD, among other parameters. This whole mathematical development is based on measuring distances from one side of the row of trees using the LIDAR system.
Comparisons between different models were evaluated by measuring the deposition of product on the leaves of apple trees. The equipment used was a hydropneumatic sprayer (Model TC 1082 by Hardi International A/S) with 8 conical nozzles and an axial fan. Ten trials were conducted over a 3-year period (1997–1999) in plantations with small trees and medium and large plantation patterns. They were conducted with different rootstocks, at different planting densities, different ages, and at different vegetative stages.
Other models based on estimations of the surfaces of leaves, branches and fruits, using a model of light transmission that follows a local Poisson distribution gave better results (TAI, TAD and LIF). For the model that uses the TAI, defined as the entire surface of the tree projected in the direction of the laser beam divided by the total area of soil, the determination of a depends on the estimation of TAI from LIDAR data. For the model using the TAD, defined as the entire surface of the tree projected in the direction of the laser beam divided by the volume occupied, the determination of a (length value, m) depends on the estimation of TAI, the distance between rows and the cross-sectional area. For the model using the LIF, which is an optical analogy for the deposition of droplets on the crop, the determination of a depends on the estimation of LIF from LIDAR data. The paper concludes that TAD is the best parameter for determining the application doses for pesticide treatments on apple trees.
In the case of TAD, the following three points need to be considered: (i) the TAD is the result of a mathematical function which uses information obtained by LiDAR that has not been checked against actual measurements of vegetation (leaf, branch, and fruit surfaces). (ii) The TAD is derived from LIDAR data of only one side of the row of apple trees. There are already studies of geometric characterization of tree crops that use LiDAR information from both sides (Sanz-Cortiella et al., 2011b). (iii) The TAD is a mathematical function whose calculation requires a value for the volume occupied by the plants. This volume is not an objective parameter and therefore its value can vary considerably according to its definition. For example, in the case of an isolated tree, the volume obtained from a simple ellipsoidal model is much greater than that obtained by the immersion of the same tree in a water tank. However, the results of this study showed the importance of the density of the different elements that constitute a tree in determining application doses for PPP.
Continuing with the previous work and looking for easy solutions for the determination of pesticide doses for tree crops without the use of LiDAR sensors, Walklate et al. (2003) present a system to allow farmers to determine application doses for any vegetative stage of the tree. The first version was designed for apple plantations in the United Kingdom. The system is based on a set of pictograms, obtained with a LiDAR from various plantations. Each pictogram shows a homogeneous group of apple trees (5– 10 trees) with various different amounts of foliage. Each pictogram corresponds to a specific adjustment factor, CAF (Crop Adjustment Factor), which depends on the TAD calculated using LiDAR data (Walklate et al., 2002). The maximum value (1) of CAF is for orchards in full vegetative development, with maximum foliage and the maximum TAD. In plantations with the same separation between rows the pre-flowering stages typically have CAF values of between 1=4 and ½. In stages after flowering with leaves, values range from ½ to 1.With this system, the farmer has to derive the CAF factor from the pictogram that most closely resembles the situation corresponding to their apple plantation.
The product of the reference dose (the dose used with extreme leafiness) with the value of CAF obtained from the pictograms gives the dose to be applied to a specific plantation at the present stage. Walklate et al. (2006) state that it is necessary for companies trading in PPP to clearly inform about the reference crop and the reference 24 conditions in which the RDPL is effective. Standardizing these conditions would prove very useful for making dose adjustments. The system of pictograms is a major advance but it is not generic enough for the large number of different situations that can occur in orchards (different species and varieties, crop training systems and vegetative stages), so further work is required to find an equally simple but more generic system.
Despite the use of management and training systems that seek to establish an area or volume of vegetation which is as uniform as possible, the structure of modern fruit and citrus orchards and vineyards, etc. is often characterized by high degrees of heterogeneity. This, together with the presence of gaps (areas free from vegetation) of varying proportions, which depend on vegetative stage, greatly affects the quality and efficiency of PPP applications. Areas free from vegetation offer the most favorable paths along which the products applied can escape, with consequent increases in losses due to drift (Doruchowski and Holownicki, 2000). In some cases, the percentage of product that does not reach its target may be as high as 80% of the total product applied (Holownicki et al., 2000). This, together with the high cost of pesticide applications in relation to overall production costs (between 30% and 42% of production costs for olives and citrus in Spain, according to Moltó et al., 2001), has encouraged the development of systems to improve the efficiency of applications. The introduction of electronic systems in the development of new equipment has made it possible to reduce operational and environmental costs through an increase in quality (Llorens et al., 2010).
By using plant detection systems, variable dose application techniques continuously adjust the applied flow rate to the characteristics of specific crop areas. In the case of spraying with tunnel systems, product savings are the result of substantial product recovery (Planas et al., 2002). Variable applications may lead to significant savings by limiting the total quantity of product applied. It is necessary to improve our knowledge and use of systems capable of characterizing vegetation (depth, height, leaf area density, etc.) in order to adapt and modify application doses in line with detected changes and in real time (Gil, 2005). The objective pursued, whether using map-based systems, sensor systems working in real time, or both in conjunction, is to optimize the application of PPP in the area of vegetation being treated. This optimization must be both qualitative and quantitative and consists of continually adjusting the doses and the parameters that determine the quality of deposition, which include such factors as drop size and air flow (Escolà et al., 2001; Rosell et al., 2004; Gil et al., 2007).
In recent years, different research groups have developed prototypes based on the variable application principle. Applying a crop adapted variable application system with ultrasonic sensors and proportional solenoid valves, Solanelles et al. (2006) reported liquid savings of 70%, 28% and 39% in comparison to conventional applications in olive, pear and apple orchard, respectively. Gil et al. (2007) and Llorens et al. (2010) with similar systems adapted to vineyards achieved average savings of 58% compared to the conventional constant rate application systems, with similar or even better PPP depositions on leaves. Escolà et al. (2007) boarded a LIDAR based electronic characterization system in a sprayer prototype in order to adjust the dose rate in a continuous variable rate real-time mode. Compared with conventional systems, the tests of the prototype resulted in PPP volume savings of 44.33%. Doruchowski et al. (2009) developed a spray application system for sustainable plant protection in fruit growing that can automatically adapt spray and air distribution according to the characteristics of the target, to the level of crop disease and to the environmental conditions. Their Crop Adapted Spray Application (CASA) system consists of three sub-systems: (i) Crop Health Sensor (CHS), based on a spectral sensor that analyzes light reflected from leaves in the bandwidth 400–1600 nm, (ii) Crop Identification System (CIS), based on a new ultrasonic sensor that delivers real time data on target characteristics such as tree canopy width and density, an 25 (iii) Environmentally Dependent Application System (EDAS), which identifies the environmental circumstances i.e. wind velocity/direction, orchard boundary, and sensitive areas such as surface water, sensitive crops, public areas, etc., and adjusts application parameters according to the wind situation and sprayer position in relation to sensitive areas. These developments have triggered the design of different systems for the adjustment of orchard sprayer air output in order to optimize the spray distribution and minimize spray losses (Pai et al., 2009).
Application to Irrigation
Water is a critical resource in agriculture and the need for irrigation at each point in the production cycle is essential for plant health and optimum productivity. A lack or excess of water causes problems. If there is insufficient water, water stress occurs, which affects productivity. On the other hand, an excess of water results in disease, nutritional disorders and/or root suffocation, etc. Calculations of irrigation needs must distinguish between two different scenarios: design and management. In the case of design, seasonal series should be studied to identify periods of peak demand in terms of probabilities of occurring. In the case of management, interest focuses on the need for water in real time (Vellidis et al., 2008). In 1950, it was estimated that fewer than 100 million hectares of cropland were irrigated throughout the world. This area is now about 260 million hectares.
This is equivalent to less than 17% of the total area of the Earth’s land surface, but 40% of the area dedicated to food and fiber production (Fereres and Evans, 2006). Irrigation is the largest consumer of fresh water on earth. Irrigation consumes an estimated 20% of total available freshwater and two thirds of the total volume intended for human use. In general, the increasing demand for water from all sectors (agricultural, municipal, industrial and recreational uses, etc.) means that significant improvement are required in the management of irrigation water in order to optimize the use of this limited resource that is essential for life. One proposed improvement implies changing the emphasis from maximizing production per unit area to maximizing production per unit of water consumed (Fereres and Evans, 2006).
The appropriate training of fruit trees is essential to ensure a suitable distribution of light within the treetops. This also helps to prevent the appearance of shady areas and areas with excessive radiation and helps to ensure fruit quality and quantity. There are many works that study the different training systems, but few are using 3D geometric characterization tools for conducting these studies.
A simplified method for building 3D mock-ups of peach trees is presented in Sonohat et al. (2006). The method combines partial digitizing of tree structure with reconstruction rules for non-digitized organs.Reconstruction rules make use of allometric relationships, random sampling of shoot attribute distribution and additional hypotheses (e.g. constant internode length). The method was quantitatively assessed for two training systems (tight goblet and wide-double- Y), at a range of spatial scales. For this purpose, light interception properties of reference and reconstructed mock-ups were compared. The proposed method could therefore be used to make 3D tree mock-ups usable for a range of some, but not all, light computations. Because the simplified method allows large time savings, it could be used in virtual experiments requiring large numbers of replicates, such as comparative studies of tree genotypes or training systems.
Potel et al. (2005) conducted a study on the effects of training. Through a method of measurement developed by the INRA (Centre of Clermont-Ferrand, France), it was possible to obtain an exact 3D 29 reproduction of the trees. Light was analyzed using the silhouette to total area ratio for each shoot,obtained by simulation, which characterized precisely the distribution of light in the tree. The results highlight the importance of the annual conditions in the evolution of leaf area.
Simple models of light interception are useful to identify the key structural parameters involved in light capture. Sinoquet et al. (2007) developed such models for isolated trees and tested them with virtual experiments. Light interception was decomposed into the projection of the crown envelope and the crown porosity. The latter was related to tree structure parameters. Virtual experiments were conducted with 3D digitized apple trees grown in Lebanon and Switzerland, with different cultivars and training. The digitized trees allowed actual values of canopy structure (total leaf area, crown volume, foliage inclination angle, variance of leaf area density) and light interception properties (projected leaf area, silhouette to total area ratio, porosity, dispersion parameters) to be computed, and relationships between structure and interception variables to be derived.
The projected envelope area was related to crown volume with a power function of exponent 2/3. Crown porosity was a negative exponential function of mean optical density, that is, the ratio between total leaf area and the projected envelope area. The leaf dispersion parameter was a negative linear function of the relative variance of leaf area density in the crown volume. The resulting models were expressed as two single equations. After calibration, model outputs were very close to values computed from the 3D digitized databases.
These studies indicate that the availability of measurement tools that allow a precise geometric characterization of the plant material will facilitate and enhance the work of researchers in tree crop training systems.
While waiting for the LiDAR data sets, the USC Phil LiDAR 2 PARMap Component was engaged in training and secondary data collection. The most immediate government agency where we can find training personnel and secondary data is the Department of Agriculture (DA). The DA Secretary gave a nationwide endorsement for the Phil LiDAR 2 Project while the USC Phil LiDAR 2 Team was working with the regional offices of these agencies and the local government units (LGU). The first meeting with the Department of Agriculture Regional Office 7 (DA 7) was held on July 7, 2014 (see Figure 4.4).
In attendance were Dr. Antonio Du and Mr. Elvin Milleza from DA 7 and Dr. Roland Otadoy, Dr. Marlowe Figure 4.4 First meeting with the Department of Agriculture Regional Office 7 (DA 7). 30 Burce, Dr. Michael Loretero, Engr. Philip Virgil Astillo, and Engr. Linda Saavedra from USC. Training on the use of GPS and Quantum GIS (QGIS) was held last August 13-14, 2014 at DA 7. The Bureau of Soil and Water Management through Mr. Andy Evangelista also conducted training on soil taxonomy and soil sampling. Finally, the UP-Diliman Phil LiDAR 2 PARMap Component conducted training on atmospheric correction, image transformation, and pixel-based classification using ENVI and object-based classification using eCognition on September 23-24, 2014.
Secondary Data Collection
DA 7 provided lots of agricultural data. Data on crop volume production was collected through the Bureau of Agricultural Statistics (BAS). These data were collected to identify which crops will be prioritized in generating map with level 3 classification and for field validation.
Table 42 shows the crop production of Bohol. The first five crops according to the average volume of production are
1) Palay, regardless of the type;
2) Coconut (with husk);
3) Banana; 4) Cassava; and
The crop production in Cebu is shown in Table 43. The top five crops produced in the province are
3) banana, regardless of its type,
4) coconut (with husk);
Table 42 Crop production in Bohol (Philippine Statistics Authority).
Table 4 3 Crop production in Siquijor (Philippine Statistics Authority)
Crop production in Negros Oriental and Siquijor are shown in Table 44 and Table 45, respectively. The top crops produced in Negros Oriental are 1) sugarcane; 2) coconut (with husk); 3) banana, regardless of the type; 4) mango; and 5) cassava while the top five crops produced in Siquijor are 1) banana, regardless of its type; 2) coconut (with husk); 3) corn; 4) cassava; 3) and 5) camote.
Table 44 Crop production in Negros Oriental (Philippine Statistics Authority)
Table 45 Crop production in Siquijor (Philippine Statistics Authority)
Table 46 Land area of Region 7 by province.
The total land area Region 7 is shown in Table 4 6. According to BAS only 34.47% of the area is devoted to agriculture. BAS classified the agricultural area into six, namely Temporary Crop Land, Idle Land, Permanent Crop Land, Meadows/Pasture, Forest Land, and Other Land as shown in Figure 4.5.
The Department of Agriculture classifies crop land into rice crop pattern, crop land mixed with coconut plantation, cultivated area mixed with brushland/grassland, arable land, crops mainly cereals and sugarcane, mangrove vegetation, and coconut plantations. This classification for the different provinces in Central Visayas (Bohol, Cebu, Negros Oriental, and Siquijor) is shown, respectively, in Figure 4.6, Figure 4.7, Figure 4.8, and Figure 4.9.