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Rule-based Classification of Airborne Laser Scanner Data for Automatic Extraction of 3D Objects in the Urban Area

Bui Ngoc Quy, Le Dinh Hien, Duong Anh Quan, Nguyen Quoc Long

Inżynieria Mineralna

2021

no. 2

103--114

LiDAR technology has been widely adopted as a proper method for land cover classification. Recently with the development of technology, LiDAR systems can now capture high-resolution multispectral bands images with high-density LiDAR point cloud simultaneously. Therefore, it opens new opportunities for more precise automatic land-use classification methods by utilizing LiDAR data. This article introduces a combining technique of point cloud classification algorithms. The algorithms include ground detection, building detection, and close point classification - the classification is based on point clouds’ attributes. The main attributes are heigh, intensity, and NDVI index calculated from 4 bands of colors extracted from multispectral images for each point. Data of the Leica City Mapper LiDAR system in an area of 80 ha in Quang Xuong town, Thanh Hoa province, Vietnam was used to deploy the classification. The data is classified into eight different types of land use consist of asphalt road, other ground, low vegetation, medium vegetation, high vegetation, building, water, and other objects. The classification workflow was implemented in the TerraSolid suite, with the result of the automation process came out with 97% overall accuracy of classification points. The classified point cloud is used in a workflow to create a 3D city model LoD2 (Level of Detail) afterward.

Importance of grassland monitoring in European perspective of climate change – FINEGRASS project

Dabrowska-Zielinska K., Golinski P., Jørgensen M., Mølmann J., Taff G., Twardy S., Budzynska M., Czerwinski M., Kopacz M., Kurnicki R., Kowalik W., Tomaszewska M., Malinska A., Golinska B., Gatkowska M.

Geoinformation Issues

2016

Vol. 8, no. 1(8)

55--71

The objective of the study was an assessment of the effect of climatic changes on grassland growth, its water conditions and biomass. Forecasting the grassland biomass and indicating the influence of climate on grasslands growth could be an important tool in grassland management on the national, regional and field grassland scales. Remote sensing gives the possibility to estimate the grassland growth conditions what have been achieved applying NOAA.AVHRR and Terra.MODIS. Identification of grasslands throughout the country have been done using Corine Land Cover (CLC) database. Vegetation index (NDVI) and surface temperature (Ts) have been calculated from satellite data. Meteorological data for Poland have been collected: i) for years 1997–2015, ii) for the period 1985–2014 to find the trends of climatic changes. There have been found: significant trends in air temperature (Ta) in NUTS1 in Poland; significant trend of increasing grassland surface temperature (Ts) in Poland based on satellite data; comparable results between Ts – Ts_mean and Ta – Ta_mean for the years 1997–2015; significant positive relationship between the (April-September) standardized precipitation evapotranspiration index (SPEI) and grass yields in Poland. In Norway, a trend towards earlier growth start in spring and increasing temperature sum in the growing season for the last ten years have been found, however, with large variation between years. Also, trends towards warmer springs and autumns since 1991 in Northern Norway has been observed. Climate changes may affect positively and negatively the grassland productivity. Proposed new and efficient methods based on satellite observations, can be used to monitor the productivity of grasslands to understand trends and anomalies that are likely to continue into the future, can help in planning for agricultural practices, can be an efficient tool in decision support system of grassland management and offsetting financial risks on large scales.

Celem badań była ocena wpływu zmian klimatycznych na wzrost użytków zielonych, ich warunki wodne i biomasę. Prognozowanie biomasy i określanie wpływu klimatu na jej rozwój jest ważnym zadaniem w zarządzaniu użytkami zielonymi w skali kraju, regionu i pojedynczego gospodarstwa łąkarskiego. Metody teledetekcji umożliwiają ocenę warunków wzrostu roślin na podstawie danych satelitarnych NOAA. AVHRR i Terra.MODIS. Rozmieszczenie użytków zielonych w Polsce zostało pobrane z bazy Corine Land Cover (CLC). Wskaźnik zieleni NDVI oraz temperatura powierzchni czynnej Ts zostały obliczone z ww. danych satelitarnych. W celu określenia trendów zmian klimatycznych zebrano dane meteorologiczne dla obszaru Polski i) z lat 1997–2015 oraz ii) 1985–2014. W wyniku przeprowadzonych analiz otrzymano: znaczące trendy temperatury powietrza (Ta) dla regionów statystycznych (NUTS2); znaczący trend rosnący temperatury powierzchni czynnej (Ts). Dla lat 1997–2015 otrzymano porównywalne wyniki pomiędzy Ts i Ts_mean oraz Ta i Ta_mean oraz znaczącą zależność dodatnią pomiędzy wskaźnikiem (z okresu kwiecień-wrzesień) standaryzowanego klimatycznego bilansu wodnego (SPEI) a produkcyjnością użytków zielonych. Dla obszaru Norwegii otrzymano trend wcześniejszego startu wegetacji na wiosnę i rosnący trend sumy temperatur powietrza w sezonie wegetacyjnym w ostatnim dziesięcioleciu, z tym, że z dużymi wahaniami w poszczególnych latach. W północnej Norwegii zanotowano również od roku 1991 trend coraz cieplejszych wiosen i jesieni. Zmiany klimatyczne mogą wpływać zarówno pozytywnie jak i negatywnie na produkcyjność użytków zielonych. Proponowane nowe metody bazujące na danych satelitarnych mogą być zastosowane do monitorowania produkcyjności użytków zielonych w celu rozpoznania trendów i anomalii klimatycznych, które mogą się zdarzyć w przyszłości, jak również mogą pomóc w planowaniu produkcji rolniczej w celu uniknięcia strat finansowych na dużą skalę.

Interannual Variation of the Growing Season Maximum Normalized Difference Vegetation Index, MNDVI, and Its Relationship with Climatic Factors on the Tibetan Plateau

Wang S.-H., Sun W., Li S.-W., Shen Z.-X., Fu G.

Polish Journal of Ecology

2015

Vol. 63, nr 3

424--439

The effects of the growing season climatic factors (i.e., temperature, precipitation, vapor pressure and relative humidity) on the growing season maximum normalized difference vegetation index (MNDVI), which can mirror the aboveground net primary production and the vegetation maximum absorbed ability of photosynthetically active radiation, were examined during the period from 2000 to 2012 on the Tibetan Plateau. The effects of climatic factors on the MNDVI changed with vegetation types, which was probably due to the fact that the changes of climatic factors differed with the type of vegetation. There was a significant increasing trend for the spatially averaged MNDVI of the vegetation area over the entire plateau. Approximately 16 and 3% of the vegetation area demonstrated a significant MNDVI increasing and decreasing trend, respectively. The MNDVI was significantly affected by relative humidity and vapor pressure, but not affected by temperature and precipitation over the entire plateau. Our findings suggested that the environmental humidity played a predominant role in affecting the variation of MNDVI over the entire Tibetan Plateau.