scholarly journals Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

2020 ◽  
Vol 12 (18) ◽  
pp. 3030
Author(s):  
Ram Avtar ◽  
Stanley Anak Suab ◽  
Mohd Shahrizan Syukur ◽  
Alexius Korom ◽  
Deha Agus Umarhadi ◽  
...  
Keyword(s):  
Oil Palm ◽  
Precision Agriculture ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Palm Tree ◽  
Flight Altitude ◽  
Biophysical Parameters ◽  
Oil Palm Plantation ◽  
Red Edge

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area.

scholarly journals UAV application to estimate oil palm trees health using Visible Atmospherically Resistant Index (VARI) (Case study of Cikabayan Research Farm, Bogor City)

2020 ◽  
Vol 211 ◽  
pp. 05001
Author(s):  
Medina Nur Anisa ◽  
Rokhmatuloh ◽  
Revi Hernina
Keyword(s):  
Oil Palm ◽  
Point Clouds ◽  
Surface Model ◽  
Palm Tree ◽  
Vector Form ◽  
Tree Health ◽  
Aerial Photos ◽  
Palm Trees ◽  
Dense Point

This article describes the making of an oil palm tree health map using aerial photos extracted from UAV DJI Phantom 4. A DJI Phantom 4 was flown at 100 meters height at the Cikabayan Research Farm, Bogor City. Raw aerial photos from DJI Phantom 4 were processed using Agisoft Photoscan software to generate dense point clouds. These points were computed to produce a digital surface model (DSM) and orthophotos with a spatial resolution of 2.73 cm/pixel. Red, green, and blue bands of the photos were computed to provide the Visible Atmospherically Resistant Index (VARI). Also, orthophotos containing oil palm trees were digitized to create points in vector form. VARI pixel values were added to each point and classified into four classes: Needs Inspection, Declining Health, Moderately health, and Healthy. Resulted oil palm tree health map reveals that most of the oil palm trees in the study location are classified as Declining Health and Needs Inspection. Profitably, plantation workers can directly inspect oil palm trees whose health are declining, based on information derived from oil palm tree health map. The information that comes from this study will significantly save time and effort in monitoring oil palm trees’ healthiness.

scholarly journals UNMANNED AERIAL VEHICLE (UAV) DERIVED NORMALISED DIFFERENCE VEGETATION INDEX (NDVI) AND CROWN PROJECTION AREA (CPA) TO DETECT HEALTH CONDITIONS OF YOUNG OIL PALM TREES FOR PRECISION AGRICULTURE

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 611-614
Author(s):  
S. A. Suab ◽  
M. S. Syukur ◽  
R. Avtar ◽  
A. Korom
Keyword(s):  
Oil Palm ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Point Clouds ◽  
Health Conditions ◽  
Projection Area ◽  
Biophysical Parameters ◽  
Palm Trees ◽  
Normalised Difference Vegetation Index ◽  
Oil Palms

Abstract. Malaysia currently is one of the biggest global producers and exporters of palm oil. The world’s expanding oil palm plantation areas contribute to climate change and in-return, climate is change also affecting the health of oil palms through a range of abiotic and biotic stresses. Current advancements in Precision Agriculture research using UAV gives an advantage to detect the health conditions of oil palm at early stages. Thus, remedial actions can be taken to prolong the life and increase oil palms productivity. This paper explores the use of UAV derived NDVI and CPA of young oil palm to detect the health conditions. NDVI of individual oil palm were extracted using ground masking layer from the dense point clouds and visual on-screen manual editing was done for removing trees other than oil palm in ENVI software. The classified individual crown NDVI were then processed to extract the mean NDVI also conversion to vector to obtain the individual crown outline. Extracted mean NDVI was classified into un-healthy and healthy trees while the CPA was classified into small, medium and big size classes. These classes of NDVI and CPA were analysed using GIS overlay method thus revealing the spatial patterns of individual oil palm trees and its health conditions. Overall, the majority of oil palm trees of the study area are healthy but average performing. However, few oil palm trees detected having health problems which has low NDVI and small CPA. This study demonstrates that biophysical parameters such as the CPA can be used to detect individual young oil palm trees health conditions and problems when combined with vegetation indices such as NDVI.

scholarly journals MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

2016 ◽  
Vol XLI-B3 ◽  
pp. 323-330 ◽  
Author(s):  
Leena Matikainen ◽  
Juha Hyyppä ◽  
Paula Litkey
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Sensor Technology ◽  
Classification Error ◽  
Surface Model ◽  
Airborne Laser ◽  
Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

scholarly journals Effect of oil palm (Elaeis guineensis Jacq.) plantations on individual-level diet variation of Hylaeamys megacephalus (G. Fisher 1814) (Rodentia, Cricetidae) in eastern Amazon Forest

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Simone Almeida Pena ◽  
Ana Cristina Mendes-Oliveira
Keyword(s):  
Palm Oil ◽  
Oil Palm ◽  
Elaeis Guineensis ◽  
Stomach Contents ◽  
Niche Overlap ◽  
Palm Tree ◽  
Amazon Forest ◽  
Tree Plantation ◽  
Forest Environment ◽  
Oil Palm Plantation

Abstract: In this study we described the diet of Hylaeamys megacephalus (G. Fisher, 1814) and investigated the degree of individual variation in the diet of this species among the Amazon Forest and the oil palm plantation. We analyzed the stomach contents of 36 individuals, of whom 11 were collected in the forest and 25 captured in the palm oil palm plantation. The H. megacephalus diet consisted of 18 food items, of which 12 were animal composition and eight were vegetable composition. The niche amplitude of the species was narrower in the forest area (Baforest = 0.013) compared to the palm tree plantation area (Bapalm = 0.478). This shows that individuals have greater niche overlap in forest areas, while in the plantation areas the animals expand their food niche. In addition, the values of the mean of the individual diet in relation to the diet of the entire population were lower in the palm oil palm plantation environment (ISpalm = 0.164) than in the Forest environment (ISforest = 0.357), indicating a high specialization in the palm oil plantation. These results indicate a population mechanism to reduce intraspecific competition in response to scarce resources.

scholarly journals Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications

2019 ◽  
Vol 7 (1) ◽  
pp. 1-20
Author(s):  
Fotis Giagkas ◽  
Petros Patias ◽  
Charalampos Georgiadis
Keyword(s):  
Vegetation Type ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Terrain Model ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Dense Point ◽  
Height Model

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

scholarly journals INLINING 3D RECONSTRUCTION, MULTI-SOURCE TEXTURE MAPPING AND SEMANTIC ANALYSIS USING OBLIQUE AERIAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 605-612
Author(s):  
D. Frommholz ◽  
M. Linkiewicz ◽  
A. M. Poznanska
Keyword(s):  
Semantic Analysis ◽  
Texture Mapping ◽  
Point Clouds ◽  
3D Models ◽  
Geometric Constraints ◽  
Aerial Images ◽  
Surface Model ◽  
Ray Casting ◽  
Real World Data ◽  
Visible Image

This paper proposes an in-line method for the simplified reconstruction of city buildings from nadir and oblique aerial images that at the same time are being used for multi-source texture mapping with minimal resampling. Further, the resulting unrectified texture atlases are analyzed for fac¸ade elements like windows to be reintegrated into the original 3D models. Tests on real-world data of Heligoland/ Germany comprising more than 800 buildings exposed a median positional deviation of 0.31 m at the fac¸ades compared to the cadastral map, a correctness of 67% for the detected windows and good visual quality when being rendered with GPU-based perspective correction. As part of the process building reconstruction takes the oriented input images and transforms them into dense point clouds by semi-global matching (SGM). The point sets undergo local RANSAC-based regression and topology analysis to detect adjacent planar surfaces and determine their semantics. Based on this information the roof, wall and ground surfaces found get intersected and limited in their extension to form a closed 3D building hull. For texture mapping the hull polygons are projected into each possible input bitmap to find suitable color sources regarding the coverage and resolution. Occlusions are detected by ray-casting a full-scale digital surface model (DSM) of the scene and stored in pixel-precise visibility maps. These maps are used to derive overlap statistics and radiometric adjustment coefficients to be applied when the visible image parts for each building polygon are being copied into a compact texture atlas without resampling whenever possible. The atlas bitmap is passed to a commercial object-based image analysis (OBIA) tool running a custom rule set to identify windows on the contained fac¸ade patches. Following multi-resolution segmentation and classification based on brightness and contrast differences potential window objects are evaluated against geometric constraints and conditionally grown, fused and filtered morphologically. The output polygons are vectorized and reintegrated into the previously reconstructed buildings by sparsely ray-tracing their vertices. Finally the enhanced 3D models get stored as textured geometry for visualization and semantically annotated ”LOD-2.5” CityGML objects for GIS applications.

scholarly journals MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

2016 ◽  
Vol XLI-B3 ◽  
pp. 323-330 ◽  
Author(s):  
Leena Matikainen ◽  
Juha Hyyppä ◽  
Paula Litkey
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Sensor Technology ◽  
Classification Error ◽  
Surface Model ◽  
Airborne Laser ◽  
Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

scholarly journals Assessing the distribution of total Fe, Cu, and Zn in tropical peat at an oil palm plantation and their relationship with several environmental factors

2022 ◽  
Vol 9 (2) ◽  
pp. 3349-3358
Author(s):  
Heru Bagus Pulunggono ◽  
Lina Lathifah Nurazizah ◽  
Moh Zulfajrin ◽  
Syaiful Anwar ◽  
Supiandi Sabiham
Keyword(s):  
Environmental Factors ◽  
Oil Palm ◽  
Principal Component ◽  
Soil Surface ◽  
Palm Tree ◽  
Sample Collection ◽  
Visual Interpretation ◽  
Oil Palm Plantation ◽  
Tropical Peatlands ◽  
Cu And Zn

Extensive utilization of fragile tropical peatlands ecosystem encourages a better understanding of spatiotemporal micronutrients distribution. The distribution of total Fe, Cu, and Zn in peat and their relationship with environmental factors were studied under oil palm plantation, Pangkalan Pisang, Koto Gasib, Riau, Indonesia. Peat samples were taken compositely inside the block using a combination of six factors, including a) the oil palm age (<6, 6-15, >15 years old), b) the peat thickness (< 3 and >3 m), c) season (rainy and dry), d) the distances from the secondary canal (10, 25, 50, 75, 100, and 150 m), e) the distances from an oil palm tree (1, 2, 3, and 4 m), and f) the depth of sample collection (0-20, 20-40, and 40-70 cm from the peat surface). Total Fe, Cu, and Zn were determined by the wet digestion method. These micronutrients observed in this study possessed high variability; however, they were within the expected range in tropical peatland. The entire micronutrients were statistically different by oil palm age, peat thickness, and distance from canal. Meanwhile, total Cu and Zn were also significantly different at each season. The oil palm age, peat thickness, and distance from the canal were the common factors controlling total Fe, Cu, and Zn in peat significantly. Moreover, total Cu and Zn were also dictated by season, distance from the oil palm tree, and depth of sample collection. Based on visual interpretation in PCA (principal component analysis), all micronutrients were categorized into two groups, separated by 2 m distance from the oil palm tree and 20 cm depth from the soil surface. Our study also highlights the dominance of the dilution over the enrichment process in peat, which requires further research to formulate micronutrients fertilization, especially for an extended cultivation time.

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