Protection and maintenance of permanent pastures

peer reviewedAll farmers receiving direct payments are subject to compulsory cross-compliance which includes standards related to the maintenance and protection of permanent pastures. Questionnaire techniques and spatio-temporal analyses demonstrated that the ratio of permanent pasture area to agricultural land provides a simple tool for monitoring and controlling the protection of permanent pastures at the regional to Member State level. Huge variations in the ratio across Europe were related to the importance of permanent pastures, the interpretation of definitions, sources of information used, differences in calculation, and the presence of protective and/or sensitive zones. Precautionary or complementary measures are in place in most Member States in order to prevent decreases in the ratio. The implementation of GAEC standards related to permanent pastures overlaps with the standard management requirements, national legislation and current agri-environmental programmes. The study advocates the establishment of a comprehensive geo-information platform consisting of a topologically correct inventory of all permanent pasture parcels in a 1:1 geo-referenced relation between IACS and LPIS; ancillary spatially explicit data such as orthophotos, remote sensing images and other thematic geo-databases; and, geodatabases with parcel information compiled for other monitoring purposes such as those within the framework of the Nitrates Directive or 2nd pillar support

Estimation and Mapping the Rubber Trees Growth Distribution using Multi Sensor Imagery With Remote Sensing and GIS Analysis

The plantation of rubber tree in different countries throughout the world are expanded rapidly in areas that are not known before in planting such as these vegetation species. Estimating and mapping the distribution of rubber trees stand ages in these regions is very necessary to get better understanding of the effects of the changes of land cover on the Carbon and Water Cycle and also the productivity of the latex in different ages. Many remote sensing techniques that have been used to estimate the land cover / land use for mapping and monitoring the distribution of rubber trees growth based on different remote sensing classification algorithms (Maximum likelihood, SAM classification, Decision Tree and Mahalanobis Distance) with different types of data (Multispectral, Hyperspectral or statistical) by using many sensor

TRENDS IN URBAN MORPHOLOGICAL DATA CAPTURE: A REVIEW OF THEORETICAL PERSPECTIVES ON UTILITY OF GEOSPATIAL TECHNOLOGY

Aim: The purpose of this paper is to review the trends in the application of geospatial technology in urban morphology data capture and studies. Methodology and Results: This review was guided by critical thinking research approach, which involves analysis of relevant literature on a phenomenon to enable to draw conclusion(s) on whether a claim is true, sometimes true, partly true or false and using methods to applied in critical thinking include broad intellectual criteria such as clarity, credibility, accuracy, precision, relevance, depth, significance and fairness. The results show that increasing urbanization and sub-optimal locations of urban amenities and utilities has resulted in many cities facing environmental, land use and socio-economic challenges. This can be mitigated by the implementation of cost-effective urban development plans and policies together with an in-depth understanding of the interactions existing between urban natural and human systems, an undertaking reliably aided by geospatial technologies notably Remote Sensing, Geographical Information Systems, Global Positioning System and Photogrammetry. Conclusion, significance, and impact study: This paper is therefore anchored on an understanding of urban morphology, factors determining its changes over time and demonstrated achievements of the utility of geospatial technology in the study of the same with policy implications for the applications of the geospatial technology in urban studies

Digital surface modelling and 3D information extraction from spaceborne very high resolution stereo pairs

This report discusses the potentials of VHR stereo imagery for automatic digital surface modelling (DSM) and 3D information extraction on large metropolitan cities. Stereo images acquired by GeoEye-1 on Dakar and Guatemala City and by WorldView-2 on Panama City, Constitucion (Chile), Kabul, Teheran, Kathmandu and San Salvador were processed following a rigorous photogrammetric approach. The work focusing on evaluating the quality of the DSMs in relation to the image and terrain characteristics and, among the possible DSM’s application, present a solution for buildings height estimation. The size of the datasets, the variety of case studies and the complexity of the scenarios allow to critically analyzing the potentials of VHR stereo imagery for 3D landscape modeling for natural hazards assessment.JRC.G.2-Global security and crisis managemen

Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

Utilisation de la télédétection pour l’analyse de la dynamique de la biomasse aérienne sèche totale des forêts et des palmiers à huile d’une plantation mature dans le Bassin du Congo

Le stockage de la biomasse aérienne (BA) sèche totale des forêts est indispensable à la lutte contre les changements climatiques. Depuis quelques décennies, il y a une tendance à l’introduction de cultures agro-industrielles, comme les plantations de palmiers à huile, dans les forêts tropicales dans le Bassin du Congo. Ces conversions participent à l’augmentation ou à la diminution des émissions ou absorptions de dioxyde de carbone (CO2) dans l’atmosphère, tout en occasionnant des changements climatiques. Dans cette région, la disponibilité des données de terrain et de télédétection est relativement limitée pour évaluer la BA. L’estimation de la BA des palmiers à huile n’est également pas maitrisée dans le Bassin du Congo. Les incertitudes rapportées dans les études précédentes utilisant la télédétection demeurent encore élevées. Plusieurs approches à fort potentiel restent encore à développer ou à évaluer. À titre d’exemple, l’approche MARS (régressions multivariées par spline adaptative) pour estimer la BA n’a pas encore été testée, notamment avec des données combinées optiques, LiDAR et radar. Les pertes et les gains de la BA dus aux changements des forêts en palmiers à huile dans le Bassin du Congo, particulièrement au Gabon, n’ont pas encore été quantifiés. La présente étude vise alors à contribuer au développement des méthodes d’estimation de la BA par l’utilisation de la télédétection pour comprendre l’impact des plantations des palmiers à huile sur les variations de la BA des forêts. Au cours de la présente étude, nous avons développé les premiers modèles allométriques d’estimation de la BA des palmiers à huile à l’aide de mesures in situ originales, que nous avons acquises dans le Bassin du Congo. Des modèles de BA des palmiers à huile ont également été établis avec MARS et les régressions linéaires multiples (RLM) en utilisant des indices dérivés de la transformée de Fourier (indices FOTO) à partir d’images satellitaires FORMOSAT-2 et PlanetScope. Finalement, cette thèse propose aussi des modèles MARS qui combinent des données de télédétection optiques (SPOT 7), LiDAR et radar polarimétrique interférométrique (PolInSAR) pour estimer la BA des forêts tropicales. À l’aide des estimations fournies par les modèles construits, la dynamique des BA des forêts et des plantations de palmiers à huile a été analysée. Les résultats ont montré que le modèle allométrique local de BA, utilisant la variable composée formée par le diamètre à hauteur de poitrine, la hauteur et la densité, ou le nombre de feuilles, permettait d’avoir les meilleures estimations (erreur quadratique moyenne relative (%RMSE) = 5,1 %). Un modèle allométrique de BA relativement performant a également été construit en utilisant seulement le diamètre et la hauteur (%RMSE = 8,2 %). Pour l’estimation des BA des palmiers à partir d’images FORMOSAT-2 et PlanetScope, les résultats démontrent que l’approche MARS permet les évaluations les plus précises (%RMSE ≤ 9,5 %). Cela est particulièrement vrai lorsque les images FORMOSAT-2 sont considérées (%RMSE ≤ 6,4 %). Les modèles de régression linéaire multiple donnent aussi des résultats avec des erreurs faibles, mais n’atteignent pas l’approche MARS (%RMSE ≥ 6,6 %). Cette dernière a été utilisée pour développer une série de modèles afin d’estimer les BA des forêts de la région d’étude. Les résultats montrent que le modèle utilisant la variable individuelle de la hauteur médiane de la canopée (RH50) dérivée des données LiDAR a estimé la biomasse avec plus de précision (%RMSE = 28 %). La combinaison de données de télédétection (optique, LiDAR et radar) a réduit de près de 4 % les erreurs d’estimation de la biomasse du modèle exploitant la variable individuelle (RH50). Les analyses de la dynamique de BA due aux remplacements des forêts en palmeraies ont enfin permis de constater que les forêts sont plus des vecteurs de gains de BA que les palmeraies particulièrement pour les forêts matures (512 t ha-1 de plus de BA que les palmeraies, soit un surplus de 88 %). Ce constat est identique pour les forêts secondaires vieilles (168 t ha-1, soit 70 % de surplus de BA que les palmeraies) et les forêts secondaires jeunes-adultes ou inondables (74 t ha-1 de plus que les palmeraies, soit un excédent de 51 %). En revanche, l’installation de plantations de palmiers à huile dans les zones de sols nus ou forêts en repousse pourrait être gagnante en termes de BA, car celles-ci ne présentent que 72 t ha-1 de BA (100 % moins que les palmiers). C’est le cas aussi dans les zones occupées par les forêts secondaires jeunes-adultes avec des BA minimales et des sols nus ou des forêts en repousse avec des BA maximales de 52 t ha-1 (20 t ha-1, soit 38 % de BA de moins que les palmeraies)

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

Enhancing spatial resolution of remotely sensed data for mapping freshwater environments

Freshwater environments are important for ecosystem services and biodiversity. These environments are subject to many natural and anthropogenic changes, which influence their quality; therefore, regular monitoring is required for their effective management. High biotic heterogeneity, elongated land/water interaction zones, and logistic difficulties with access make field based monitoring on a large scale expensive, inconsistent and often impractical. Remote sensing (RS) is an established mapping tool that overcomes these barriers. However, complex and heterogeneous vegetation and spectral variability due to water make freshwater environments challenging to map using remote sensing technology. Satellite images available for New Zealand were reviewed, in terms of cost, and spectral and spatial resolution. Particularly promising image data sets for freshwater mapping include the QuickBird and SPOT-5. However, for mapping freshwater environments a combination of images is required to obtain high spatial, spectral, radiometric, and temporal resolution. Data fusion (DF) is a framework of data processing tools and algorithms that combines images to improve spectral and spatial qualities. A range of DF techniques were reviewed and tested for performance using panchromatic and multispectral QB images of a semi-aquatic environment, on the southern shores of Lake Taupo, New Zealand. In order to discuss the mechanics of different DF techniques a classification consisting of three groups was used - (i) spatially-centric (ii) spectrally-centric and (iii) hybrid. Subtract resolution merge (SRM) is a hybrid technique and this research demonstrated that for a semi aquatic QuickBird image it out performed Brovey transformation (BT), principal component substitution (PCS), local mean and variance matching (LMVM), and optimised high pass filter addition (OHPFA). However some limitations were identified with SRM, which included the requirement for predetermined band weights, and the over-representation of the spatial edges in the NIR bands due to their high spectral variance. This research developed three modifications to the SRM technique that addressed these limitations. These were tested on QuickBird (QB), SPOT-5, and Vexcel aerial digital images, as well as a scanned coloured aerial photograph. A visual qualitative assessment and a range of spectral and spatial quantitative metrics were used to evaluate these modifications. These included spectral correlation and root mean squared error (RMSE), Sobel filter based spatial edges RMSE, and unsupervised classification. The first modification addressed the issue of predetermined spectral weights and explored two alternative regression methods (Least Absolute Deviation, and Ordinary Least Squares) to derive image-specific band weights for use in SRM. Both methods were found equally effective; however, OLS was preferred as it was more efficient in processing band weights compared to LAD. The second modification used a pixel block averaging function on high resolution panchromatic images to derive spatial edges for data fusion. This eliminated the need for spectral band weights, minimised spectral infidelity, and enabled the fusion of multi-platform data. The third modification addressed the issue of over-represented spatial edges by introducing a sophisticated contrast and luminance index to develop a new normalising function. This improved the spatial representation of the NIR band, which is particularly important for mapping vegetation. A combination of the second and third modification of SRM was effective in simultaneously minimising the overall spectral infidelity and undesired spatial errors for the NIR band of the fused image. This new method has been labelled Contrast and Luminance Normalised (CLN) data fusion, and has been demonstrated to make a significant contribution in fusing multi-platform, multi-sensor, multi-resolution, and multi-temporal data. This contributes to improvements in the classification and monitoring of fresh water environments using remote sensing