Oil palm counting and age estimation from WorldView-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis

Copyright © 2018 Hossein Mojaddadi Rizeei et al. The current study proposes a new method for oil palm age estimation and counting from Worldview-3 satellite image and light detection and range (LiDAR) airborne imagery. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. The sensitivity analysis was conducted on four SVM kernel types with associated segmentation parameters to obtain the optimal crown coverage delineation. Extracting tree's crown was integrated with height model and multiregression methods to accurately estimate the age of trees. The multiregression model with multikernel sizes was examined to achieve the most optimized model for age estimation. Applied models were trained and examined over five different oil palm plantations. The results of oil palm counting had an overall accuracy of 98.80%, while the overall accuracy of age estimation showed 84.91%, over all blocks. The relationship between tree's height and age was significant which supports the polynomial regression function (PRF) model with a 3 × 3 kernel size for under 10-12-year-old oil palm trees, while exponential regression function (ERF) is more fitted for older trees (i.e., 22 years old). Overall, recent remote sensing dataset and machine learning techniques are useful in monitoring and detecting oil palm plantation to maximize productivity

Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis

The current study proposes a new method for oil palm age estimation and counting. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. It was integrated with height model and multiregression methods to accurately estimate the age of trees based on their heights in five different plantation blocks. Multiregression and multi-kernel size models were examined over five different oil palm plantation blocks to achieve the most optimized model for age estimation. The sensitivity analysis was conducted on four SVM kernel types (sigmoid (SIG), linear (LN), radial basis function (RBF), and polynomial (PL)) with associated parameters (threshold values, gamma γ, and penalty factor (c)) to obtain the optimal OBIA classification approaches for each plantation block. Very high-resolution imageries of WorldView-3 (WV-3) and light detection and range (LiDAR) were used for oil palm detection and age assessment. The results of oil palm detection had an overall accuracy of 98.27%, 99.48%, 99.28%, 99.49%, and 97.49% for blocks A, B, C, D, and E, respectively. Moreover, the accuracy of age estimation analysis showed 90.1% for 3-year-old, 87.9% for 4-year-old, 88.0% for 6-year-old, 87.6% for 8-year-old, 79.1% for 9-year-old, and 76.8% for 22-year-old trees. Overall, the study revealed that remote sensing techniques can be useful to monitor and detect oil palm plantation for sustainable agricultural management

Extraction and accuracy assessment of DTMs derived from remotely sensed and field surveying approaches in GIS framework

© Published under licence by IOP Publishing Ltd. Generating a high precision continuous surface is a key capability required in most geographic information system (GIS) applications. In fact the most commonly used surface type is a digital elevation model (DEM). Recently, there are some sources of remote sensing data that provide DEM information such as; LiDAR, InSAR and ASTER GDEM which ranged from very high to low spatial resolution. However, new methods of topographic field surveying still highly on demand e.g. Differential GPS and Total station devices. In both method of capturing the terrain elevation the post processing need to be applied to create a continuous surface from point clouds. Geostatistical analysis were used to interpolate the taken sample points from site into continuous surface. In current research, we examined the height accuracy of LiDAR point clouds and total station dataset with three non-adoptive interpolation models including, invers distance weightage (IDW), nearest neighbour (NN) and radial basis function (RBF) based on referenced DGPS points. RMSE and R square regression analysis were conducted to reveal the most accurate approaches in pilot study area. The results showed Lidar surveying (less than 0.5 meter RMSE) has higher height accuracy compared to Total station surveying (above 1 meter in RMSE) to extract DTM in flat area; while consumed less computational processing time. Moreover, IDW was the best and accurate interpolation model in both datasets to generate raster cautious terrain model

Enhanced faster region-based convolutional neural network for oil palm tree detection

Oil palm trees are important economic crops in Malaysia. One of the audit procedures is to count the number of oil palm trees for plantation management, which helps the manager predict the plantation yield and the amount of fertilizer and labor force needed. However, the current counting method for oil palm tree plantation is manually counting using GIS software, which is tedious and inefficient for large scale plantation. To overcome this problem, researchers proposed automatic counting methods based on machine learning and image processing. However, traditional machine learning and image processing methods used handcrafted feature extraction methods. It can only extract low-middle level features from the image and lack of generalization ability. It’s applicable only for one application and will need reprogramming for other applications. The widely used feature extraction methods are local binary patterns (LBP), scale-invariant feature transform (SIFT), and the histogram of oriented gradients (HOG), which usually achieve low accuracy because of their limited feature representation ability and without generalization capability. Hence, this research aims to close the research gaps by exploring the deep learning-based object detection algorithm and the classical convolutional neural network (CNN) to build an automatic deep learning-based oil palm tree detection and counting framework. This study proposed a new deep learning method based on Faster RCNN for oil palm tree detection and counting. To reduce the overfitting problem during the training, this study uses the image processing method to augment the training dataset by random flipping the image and to increase the data’s contrast and brightness. The transfer learning model of ResNet50 was used as the CNN backbone and the Faster RCNN network was retrained to get the weight for automatic oil palm tree counting. To improve the performance of Faster RCNN, feature concatation method was used to integrate the high-level and low-level feature from ResNet50. The proposed model validated the testing dataset of three palm tree regions with mature, young, and mixed mature and young palm trees. The detection results were compared with two machine learning methods of ANN, SVM, image processing-based TM method, and the original Faster RCNN model respectively. The proposed enhanced Faster RCNN model shows a promising result of oil palm tree detection and counting. It achieved an overall accuracy of 97% in the testing dataset, 97.2% in the mixed palm tree region, and 96.9% in the mature and young palm tree region, while the traditional ANN, SVM, and TM methods are less than 90%. The accuracy of comparison reveals that the proposed EFRCNN model outperforms the Faster RCNN and the traditional ANN, SVM, and TM methods. It has the potential to apply in counting a large area of oil palm tree plantation

PEMETAAN SUMBER PENCEMAR SUB DAS CILEUNGSI MENGGUNAKAN CITRA SATELIT SPOT-7 DAN METODE KLASIFIKASI OBIA (OBJECT-BASED IMAGE ANALYSIS)

Sub DAS Cileungsi rentan terhadap pencemaran akibat limbah dari suatu kegiatan yang masuk ke sungai. Pencemaran sangat erat kaitannya dengan kondisi penggunaan lahan pada suatu Sub DAS. Pencemaran yang tidak diketahui jelas sumbernya akan menjadi permasalahan yang terus berlanjut. Informasi mengenai sumber pencemar sangat penting diketahui untuk menetapkan kebijakan pengelolaan sungai secara terpadu. Penelitian ini bertujuan untuk menganalisis: 1) sumber pencemar berdasarkan jenis kegiatan di Sub DAS Cileungsi menggunakan citra satelit SPOT-7 dan metode klasifikasi OBIA (Object Based Image Analysis) dan 2) menguji akurasi hasil identifikasi sumber pencemar di Sub DAS Cileungsi menggunakan citra satelit SPOT-7 dan metode klasifikasi OBIA (Object Based Image Analysis). Metodologi yang digunakan dalam penelitian ini adalah metode penginderaan jauh dengan dengan pendekatan keruangan analisis asosiasi keruangan. Berdasarkan hasil yang diperoleh, identifikasi sumber pencemar berdasarkan jenis kegiatan di Sub DAS Cileungsi dengan menggunakan Citra Satelit SPOT-7 dan Metode Klasifikasi OBIA menunjukan bahwa terdapat sembilan kelas penggunaan lahan yang mencakup dua sumber pencemar domestik dan tujuh sumber non domestik. Sumber pencemar pada Sub DAS Cileungsi didominasi dengan sumber pencemar jenis kegiatan domestik dengan jumlah 7 sumber, sedangkan sumber pencemar jenis kegiatan non domestik berjumlah 6 sumber. Uji akurasi identifikasi sumber pencemar Sub DAS Cileungsi menggunakan Citra Satelit SPOT-7 dan Metode Klasifikasi OBIA dilakukan dengan matriks konfusi. Hasil uji akurasi menunjukan bahwa hasil klasifikasi OBIA untuk identifikasi sumber pencemar dengan penggunaan lahan adalah 86.66% akurasi keseluruhan (overall accuracy) dan 85% akurasi kappa (kappa accuracy). Berdasarkan hasil yang diperoleh, nilai akurasi menunjukkan bahwa hasil klasifikasi OBIA dianggap benar dan dapat diterima

Oil palm detection and delineation using local maxima, template matching and seeded region growing

Oil palm (Elaeis guineensis Jacq.) is recognized as a golden crop and it contributes significantly to the economic development of Malaysia. Oil palm detection and delineation are important stepping stones for the practice of precision agriculture in the oil palm industry and it could be done so with remote sensing applications. This research aims to develop a semi-automatic, streamlined approach of oil palm detection and delineation using a combination of template matching, local maxima and seeded region growing with Worldview-2 data. The performance of the proposed methods was assessed in various aspects while taking into consideration the different planting conditions, age, and height. The proposed methods of oil palm detection managed to achieve high accuracy with overall precision and recall rate of 83% and 90% respectively and planimetric accuracy of 0.84 m root mean square error. The overall accuracy index is recorded at 71.2%. It was found that different planting conditions affect the detection accuracy to a certain degree where oil palms in optimal planting conditions are the most accurately detected with an accuracy index of 89.5%. Meanwhile, the parameters of age and height were found to have no significant effect on the planimetric accuracy or its positional accuracy. Oil palm delineation scored a high segmentation accuracy with only a 25% error rate. The proposed methods are feasible for oil palm detection with their simple, streamlined and user-friendly features and the application of this approach can be extended to other regions of oil palms with similar conditions

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data

Sustainable bio-economy that delivers the environment-food-energy-water nexus objectives::the current status in Malaysia

Biomass is a promising resource in Malaysia for energy, fuels, and high value-added products. However, regards to biomass value chains, the numerous restrictions and challenges related to the economic and environmental features must be considered. The major concerns regarding the enlargement of biomass plantation is that it requires large amounts of land and environmental resources such as water and soil that arises the danger of creating severe damages to the ecosystem (e.g. deforestation, water pollution, soil depletion etc.). Regarded concerns can be diminished when all aspects associated with palm biomass conversion and utilization linked with environment, food, energy and water (EFEW) nexus to meet the standard requirement and to consider the potential impact on the nexus as a whole. Therefore, it is crucial to understand the detail interactions between all the components in the nexus once intended to look for the best solution to exploit the great potential of biomass. This paper offers an overview regarding the present potential biomass availability for energy production, technology readiness, feasibility study on the techno-economic analyses of the biomass utilization and the impact of this nexus on value chains. The agro-biomass resources potential and land suitability for different crops has been overviewed using satellite imageries and the outcomes of the nexus interactions should be incorporated in developmental policies on biomass. The paper finally discussed an insight of digitization of the agriculture industry as future strategy to modernize agriculture in Malaysia. Hence, this paper provides holistic overview of biomass competitiveness for sustainable bio-economy in Malaysia

Spatial-Intelligent Decision Support System for Sustainable Downstream Palm Oil Based Agroindustry within the Supply Chain Network: A Systematic Literature Review and Future Research

Oil palm plantations as one of the sexiest commodities; produce a high yield of oil and fat that can be used in various sectors. The prospect of oil palm and its derivative products is good, but there are obstacles and problems faced that are mainly related to sustainability issues in oil palm plantations and its downstream process. Therefore, it is important to study the decision-making process that are needed to develop sustainable palm oil agroindustry. This paper aims at providing a comprehensive literature review for decision support system for sustainable agroindustry. Totally, 186 scientific publication articles from 2005 to 2019 were reviewed and synthesized. The reviewed articles were categorize based on the keywords of palm oil sustainability, geographic information system (GIS), and decision support system (DSS). The research gap and pointers for future research that are identified is the lack of sustainability aspect inclusion on decision-making process. We also identified the lack discussion of integrated spatial and intelligent tools through DSS for better, faster, and smarter decision-making process. In the end part of the paper, a pointer for possible future research was develop in terms of combination through spatial-intelligent system applying business analytics for sustainable agroindustry

Forestry and Arboriculture Applications Using High-Resolution Imagery from Unmanned Aerial Vehicles (UAV)

Forests cover over one-third of the planet and provide unmeasurable benefits to the ecosystem. Forest managers have collected and processed countless amounts of data for use in studying, planning, and management of these forests. Data collection has evolved from completely manual operations to the incorporation of technology that has increased the efficiency of data collection and decreased overall costs. Many technological advances have been made that can be incorporated into natural resources disciplines. Laser measuring devices, handheld data collectors and more recently, unmanned aerial vehicles, are just a few items that are playing a major role in the way data is managed and collected. Field hardware has also been aided with new and improved mobile and computer software. Over the course of this study, field technology along with computer advancements have been utilized to aid in forestry and arboricultural applications. Three-dimensional point cloud data that represent tree shape and height were extracted and examined for accuracy. Traditional fieldwork collection (tree height, tree diameter and canopy metrics) was derived from remotely sensed data by using new modeling techniques which will result in time and cost savings. Using high resolution aerial photography, individual tree species are classified to support tree inventory development. Point clouds were used to create digital elevation models (DEM) which can further be used in hydrology analysis, slope, aspect, and hillshades. Digital terrain models (DTM) are in geographic information system (GIS), and along with DEMs, used to create canopy height models (CHM). The results of this study can enhance how the data are utilized and prompt further research and new initiatives that will improve and garner new insight for the use of remotely sensed data in forest management