MSc. (Geography) / Growing concerns with regards to the environmental and economic impacts related to the application of herbicides to control the spread and abundance of weeds in agricultural crops have created a need for the development of novel agricultural management systems that are less dependent on herbicide usage and tillage. Such concerns have given rise to the need for the variable spatial treatment of croplands aimed at the minimization of requirements for the application of herbicides and the subsequent minimization of excess materials released into the surrounding environment. Remote sensing provides an opportunity for the fast and cost-effective delineation of weed patches in croplands over large areas where traditional scouting techniques would be impractical. The differences in spectral reflectance from different plants at certain wavelengths due to species specific variations in biochemical and physical characteristics is what lays the basis for the distinction of vegetation species within remotely sensed images and ultimately the potential detection of weed-species in croplands. This study investigates the potential spectral characterisation of maize and commonly occurring weed-species by (1) making use of reflectance spectra collected at leaf-level to identify statistically significant differences in reflectance between individual species throughout the visible (VIS), Near-Infrared (NIR) and Shortwave-Infrared (SWIR) regions of the electromagnetic spectrum, determining the potential of the Red-Edge Position (REP) and slope for this particular application and testing the accuracy at which reflectance spectra may be classified according to vegetation species based on spectral reflectance at specific wavebands and REP as input predictor variables, (2) testing the potential effect of mixed spectral responses and soil-background interference through the analysis of reflectance spectra collected at canopy-level, and (3) determining the potential effect of the spectral generalisation associated with multispectral reflectance through the analysis of spectral responses resampled to the spectral band designations of representative high spatial resolution multispectral sensors. The results showed that maize may be spectrally distinguished from all of the weed-species included in the analysis based on leaf-level hyperspectral reflectance throughout the Visible-to-Near-Infrared (V-NIR) and SWIR-regions of the electromagnetic spectrum, however, the unique characterisation of weed-species is not possible for all species and where it is possible, it is highly wavelength-specific and would require high spectral resolution hyperspectral data. The wavelengths most suitable for the spectral characterisation of maize-crops and weed species in the study area were identified as: 432.1nm, 528.2nm, 700.7nm, 719.4nm, 1335.1nm, 1508.1nm, 2075.8nm, 2164.5nm and 2342.2nm. The output predictor model was able to classify reflectance spectra associated with maize crops and weeds in the study area at an overall accuracy of 89.7 per cent and it was shown that the inclusion of the REP as predictor variable did not improve the overall accuracy of the classification, however, may be used to improve the classification accuracies of certain species...

Cannabis sativa L. is a drug producing crop that is illegally cultivated in South Africa. The South African Police Service (SAPS) use aerial spotters on low flying fixed wing aircrafts to identify cannabis from other land cover. Cannabis is usually intercropped with maize to conceal it from law enforcement officers. Therefore the use of remote sensing in identifying and monitoring cannabis when intercropped with maize and other crops is imperative. This study aimed to investigate the potential of hyper spectral indices to discriminate cannabis from maize under different cropping methods, namely, monocropped and intercropped. Cannabis and maize were grown in a greenhouse. The spectral signatures were measured in a dark room environment. Green pigments (chlorophyll and carotenoid) from the treatments were also measured. These pigments were then compared with their respective indices. Photosynthetic reflective index (PRI) and Carotenoid Reflective Index (CRI) were two of the indices used to discriminate cannabis from maize using carotenoid content while the Red Edge Position (REP) and the narrow band Normalized Difference Vegetation Index (NDVI) used chlorophyll content and morphological differences respectively to discriminate the two plant species. CRI and NDVI proved to be capable of identifying cannabis under the two cropping conditions. NDVI showed a 25% spectral over lap for the monocropped treatments and 60% over lap for the intercropped treatments. CRI displayed 18% and 58% over lap for the monocropped and intercropped treatments, respectively. As a result CRI emerged as the most suitable index for discriminating cannabis from maize. With proper calibration of airborne or space borne imagery, the study offers potential to detect cannabis using remote sensing technology. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

Subtropical deforestation in Latin America is thought to be driven by demand for agricultural land, particularly to grow soybeans. However, existing remote sensing methods that can differentiate crop types to verify this hypothesis require high spatial or spectral resolution data, or extensive ground truth information to develop training sites, none of which are freely available for much of the world. I developed a new method of crop classification based on the phenological signatures of crops extracted from multi-temporal MODIS vegetation indices. I tested and refined this method using the USDA Cropland Data Layer from Kansas, USA as a reference. I then applied the method to classify crop types for a study site in Pellegrini, Santiago Del Estero, Argentina. The results show that this method is unable to effectively separate summer crops in Pellegrini, but can differentiate summer crops and non-summer crops. Unmet assumptions about agricultural practices are primarily responsible for the ineffective summer crop classification, underlining the need for researchers to have a complete understanding of ground conditions when designing a remote sensing analysis.

Remote sensing and Geographic Information System (GIS) - an effective tool for managing naturalresources, is quite common application in establishing thematic maps. However, the application of this modern technology in natural resource management has not yet been popular in Vietnam, particularly mapping the land use/cover. Currently, land use/cover map is constructed as traditional methods and gets limitations of management counting due to time-consuming for mapping andsynthesis the status of land use/cover. Hence, information on the map is often outdated and inaccurate.The main objective of this study is to upgrade the accuracies in mapping current perennialcrops in Chu Se District, Gia Lai Province in Vietnam by interpreted NDVI index (Normalized Difference Vegetation Index) from Landsat 8-OLI (Operational Land Imager). The results of studyis satisfied the urgent of practical requirement and scientific research. There are 3 types of perennial industrial plants in the study area including rubber, coffee, and pepper, in which most coffee isgrown, with an area of over 10,000 hectares. The results also show that integration of remote sensing and GIS technology enables to map current management and distribution of perennial industrialplants timely and accurately. This application is fully consistent with the trend of the world, and in accordance with regulations of established land use/cover map, and the process could be appliedat other districts /towns or in higher administrative units. / Viễn thám và hệ thông tin địa lý (GIS) là công cụ hữu hiệu để quản lý tài nguyên thiên nhiên, được ứng dụng khá phổ biến để thành lập các loại bản đồ. Tuy nhiên, việc áp dụng công nghệ hiện đại này trong lĩnh vực quản lý tài nguyên thiên nhiên ở Việt Nam chưa phổ biến, nhất là công tác xây dựng bản đồ hiện trạng sử dụng/độ phủ đất. Việc xây dựng bản đồ hiện trạng hiện nay vẫn theo phương pháp truyền thống, thường gặp nhiều hạn chế do thời gian tổng hợp và xây dựng bản đồ hiện trạng kéo dài, dẫn đến thông tin trên bản đồ bị lạc hậu và không chính xác. Mục tiêu chính của nghiên cứu này là nâng cao độ chính xác kết quả giải đoán ảnh viễn thám Landsat 8 bằng chỉ số NDVI (chỉ số khác biệt thực vật) để thành lập bản đồ hiện trạng sử dụng đất cây công nghiệp lâu năm ở huyện Chư Sê, tỉnh Gia Lai, Việt Nam. Từ đó quản lý hiện trạng sử dụng loại đất này phù hợp yêu cầu cấp bách thực tiễn sản xuất và nghiên cứu khoa học. Kết quả của nghiên cứu cho thấy có 3 loại hình cây công nghiệp trên địa bàn nghiên cứu gồm cây cao su, cà phê và hồ tiêu, trong đó cây cà phê được trồng nhiều nhất, với diện tích hơn 10.000 ha. Nghiên cứu cũng cho thấy, tích hợp công nghệ viễn thám và GIS cho phép quản lý hiện trạng và phân bố cây công nghiệp trong không gian một cách hiệu quả và nhanh chóng. Ứng dụng này hoàn toàn phù hợp với xu hướng của thế giới, đồng thời theo đúng quy định thành lập bản đồ hiện trạng sử dụng đất, và quy trình này có thể thực hiện được ở cấp huyện/thị xã hoặc đơn vị hành chính cấp cao hơn.