Remote Sensing of Vegetation

Olive trees native to the Middle East and Iraqi Kurdistan region, it can survive severe ecosystems including sandy and rocky soils with high calcite content and extreme temperature fluctuations between -7°C up to +45°C giving yields even with minimum rainfall requirement. Results show that olive is currently grown in a very limited area of less than 250 ha, under drip irrigation system, which mostly depended on supply of water from ground water in Sulaymaniyah governorate. In contrast, 90% of the world’s olive is dependent on precipitation. In addition, olive can grow and survive minimum rainfall of 300 mm and give satisfactory yields with rainfall of 500 mm without irrigation. The majority of olive fields were established in best fertile agricultural lands. Meanwhile, the
aforementioned lands are rich with montmorillonite, which swells when it is moisten and shrinks when it is dry. Therefore, resulting in deep cracks in the soil which causes breaking down of the roots and weakening of the trees. The total rainfall in Sulaymaniyah governorate, during the last 72 years (1941 to 2013), had dropped down below the minimum rainfall requirements for olive growing. This makes olive trees suitable and economic for utilizing sandy and rocky soils. Therefore, olive has a great role in the re-vegetation process of severe ecosystems and should be included in any program of renewing the Kurdistan region forest and developing green area which encourage the tourism in the Region and helping in beautifying and purifying the atmosphere from the poisonous
gases.

Resumen: Las imágenes de alta resolución espacial constituyen la principal fuente en los trabajos de fotointerpretación que producen los mapas de coberturas vegetales oficiales. Este elevado nivel de detalle-de gran utilidad en el análisis visual-resulta excesivo para el análisis digital basado en píxeles cuando nuestro objetivo es la diferenciación de objetos definidos por unos límites, sea por la variabilidad espectral de las cubiertas que lo caracterizan o por las escasas diferencias espectrales existentes entre objetos contiguos. Complementariamente, existen cualidades que permiten definir los objetos espaciales: escala, textura, forma, contexto. En el presente trabajo, proponemos una clasificación basada en objetos para la detección de formaciones vegetales ecológico-fisionómicamente homogéneas empleando la ortofotografía del Plan Nacional (PNOA). Estas imágenes tienen 50 centímetros de resolución espacial y ofrecen datos espectrales del visible e infrarrojo cercano. Realizamos una segmentación multiescala de las imágenes analizando componentes espectrales y espaciales. Definimos un juego de reglas de clasificación para calibrar los componentes de los objetos. Utilizamos datos de campo y validamos los resultados con regiones de interés. Los mapas derivados permiten discriminar formaciones vegetales ecológico-fisionómicamente homogéneas con un gran nivel de detalle, adecuado para estudios generales de vegetación, ecología de paisaje y modelos de erosión. Palabras clave: cartografía, formaciones vegetales, clasificación basada en objetos, OBIA, PNOA

Abstract: High-resolution spatial images are the main data source in photo-interpretation projects that produce official land cover and vegetation maps. High resolution aerial imagery – highly useful in visual analysis-is excessive for pixel-based analysis, when our goal is the differentiation of objects defined by limits. This is due to the fact that the huge spectral variability of cover or because of the few spectral difference between contiguous objects. In addition, beyond the pixel-based classification, there are qualities that can define space objects better: scale, texture, shape, and context. In this paper, we propose an object-based classification to detect physiognomic-ecological units using aerial orthophotographic imagery from the National Plan for Aerial Orthophotography (PNOA). These images have four bands (visible and near infra-red) with a 50 cm spatial resolution. We performed a multiscale image segmentation by analysing spectral and spatial components. We defined a set of classification rules to calibrate the components of the objects. We used field data and validated the results with regions of interest. The maps derived allow the differentiation of physiognomic-ecological units with a great level of detail, suitable for general studies of vegetation, landscape ecology and erosion models.
Keywords: cartography, vegetation, Object -Based Image Analysis, OBIA, PNOA

The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model-based NPP estimates (from five models of the TRENDY project) for the period 1982–2012, to study the role of terrestrial ecosystems in carbon cycle under the prevailing climate conditions. We found that 80% and 67% of the global land area showed positive NPP and NDVI values, respectively, for this period. The global NPP was estimated to be about 63 Pg C¨y´1 , with an increase of 0.214 Pg C¨y´1¨y´1. Similarly, the global mean NDVI was estimated to be 0.33, with an increasing trend of 0.00041 y ´1. The spatial patterns of NPP and NDVI demonstrated substantial variability, especially at the regional level, for most part of the globe. However, on temporal scale, both global NPP and NDVI showed a corresponding pattern of increase (decrease) for the duration of this study except for few years (e.g., 1990 and 1995–1998). Generally, the Northern Hemisphere showed stronger NDVI and NPP increasing trends over time compared to the Southern Hemisphere; however, NDVI showed larger trends in Temperate regions while NPP showed larger trends in Boreal regions. Among the five models, the maximum and minimum NPP were produced by JULES (72.4 Pg C¨y´1) and LPJ (53.72 Pg C¨y´1) models, respectively. At latitudinal level, the NDVI and NPP ranges were ~0.035 y ´1 to ~´0.016 y ´1 and ~0.10 Pg C¨y´1¨y´1 to ~´0.047 Pg C¨y´1¨y´1 , respectively. Overall, the results of this study suggest that the modeled NPP generally correspond to the NDVI trends in the temporal dimension. The significant variability in spatial patterns of NPP and NDVI trends points to a need for research to understand the causes of these discrepancies between molded and observed ecosystem dynamics, and the carbon cycle.

In the Mongolian Plateau, the desert steppe, mountains, and dry lake bed surfaces may affect the process of dust storm emissions. Among these three surface types, dry lake beds are considered to contribute a substantial amount of global dust emissions and to be responsible for " hot spots " of dust outbreaks. The land cover types in the study area were broadly divided into three types, namely desert steppe, mountains, and dry lake beds, by a classification based on Normalized Difference Water Index (NDWI) calculated from MODIS Terra satellite images, and Digital Elevation Model (DEM). This dry lake beds extracting method using remote sensing offers a new technique for identifying dust hot spots and potential untapped groundwater in the dry lands of the Gobi region. In the study area, frequencies of dry lake bed formation were calculated during the period of 2001 to 2014. The potential dry lake area corresponded well with the length of the river network based on hydrogeological characterization (R 2 = 0.77, p < 0.001). We suggest that the threshold between dry lake bed areas and the formation of ephemeral lakes in semi-arid regions is eight days of total precipitation.