Use of multispectral data to identify farm intensification levels by applying emergent computing techniques

Concern about feeding an ever increasing population has long been one of humankind’s most pressing problems. This has been addressed throughout history by introducing into farming systems changes allowing them to produce more per unit of land area. However, these changes have also been linked to negative effects on the socio economic and environmental sphere, that have created the need for an integral understanding of this phenomenon. This thesis describes the application of learning machine methods to induct a relationship between the spectral response of farms’ land cover and their intensification levels from a sample of farming of Urdaneta municipality, Aragua state of Venezuela. Data collection like this is a necessary first steep to implement cost-effective methods that can help policymakers to conduct succesful planing tasks, especially in countries such as Venezuela where, in spite of there being areas capable of agricultural production, nearly 50% of the internal food requirements of recent years have been satisfied by importations. In this work, farm intensification levels are investigated through a sample of farms of Urdaneta Municipality, Aragua state of Venezuela. This area is characterised by a wide diversity of farming systems ranging from crop to crop-livestock systems and an increasing population density in regions capable of livestock and arable farming, making it a representative case of the main tropical rural zones. The methodology applied can be divided into two main phases. First an unsupervised classification was performed by applying principal component analysis and agglomerative cluster methods to a set of land use and land management indicators, with the aim to segregate farms into homogeneous groups from the intensification point of view. This procedure resulted in three clusters which were named extensive, semi-intensive and intensive. The land use indicators included the percentage area within each farm devoted to annual crops, orchard and pasture, while the land management indicators were percentage of cultivated land under irrigation, stocking rate, machinery and equipment index and permanent and temporary staff ratio, all of them built from data held on the 1996- 1997 venezuelan agricultural census. The previous clusters reached were compared to the ones obtained by applying the learning machine method known as self-organizing map, which is also an unsupervised classification technique, as a way to confirm the groups’ existence. In the second stage, the learning machine known as kernel adatron algorithm was implemented seeking to identify the intensification level of Urdaneta farms from a landsat image, which consisted of two sequential steps: namely training and validation. In the training step, a predetermined number of instances randomly selected from the data set were analysed looking for a pattern to establish a relationship between the label and the spectral response in an iterative process which was concluded when the machine found a linear function capable of separating the two classes with a maximum margin. The supervised classification finishes with the validation in which the kernel adatron classifies the unseen samples by using a generalisation of the relationships learned while training. Results suggest that farm intensification levels can be effectively derived from multi-spectral data by adopting a machine learning approach like the one described

Spatial pattern recognition for crop-livestock systems using multispectral data

Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the problem complexity is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface process heterogeneity, remote sensing effects and multispectral features. The present research describes the application of learning machine methods to accomplish the above task by inducting a relationship between the spectral response of farms’ land cover, and their farming system typology from a representative set of instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless, this study shows that its application leads to simple and theoretically robust classification models. The study has covered the following phases: a)geovisualization of crop-livestock systems; b)feature extraction of both multispectral and attributive data and; c)supervised farm classification. The first is a complementary methodology to represent the spatial feature intensity of farming systems in the geographical space. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions. In this research the performance of various kernel methods applied to the representation and classification of crop-livestock systems described by multispectral response is studied and compared. The data from those systems include linear and nonlinearly separable groups that were labelled using multidimensional attributive data. Geovisualization findings show the existence of two well-defined farm populations within the whole study area; and three subgroups in relation to the Guarico section. The existence of these groups was confirmed by both hierarchical and kernel clustering methods, and crop-livestock systems instances were segmented and labeled into farm typologies based on: a)milk and meat production; b)reproductive management; c)stocking rate; and d)crop-forage-forest land use. The minimum set of labeled examples to properly train the kernel machine was 20 instances. Models inducted by training data sets using kernel machines were in general terms better than those from hierarchical clustering methodologies. However, the size of the training data set represents one of the main difficulties to be overcome in permitting the more general application of this technique in farming system studies. These results attain important implications for large scale monitoring of crop-livestock system; particularly to the establishment of balanced policy decision, intervention plans formulation, and a proper description of target typologies to enable investment efforts to be more focused at local issues