The Use of Remote Sensing and Geographic Information System to Determine the Spatial Distribution of Melaleuca cajuputi as a Major Bee Plant in Marang, Terengganu

In Malaysia, honey is chiefly obtained from species of honeybees known as Apis dorsata and to a lesser extent Apis cerana. Honey from Apis dorsata is a supplementary source of income to many rural poor in the district of Marang, Terengganu. The colonies of A. dorsata are found to nest in aggregates on tall bee trees (tree emergent) in the open, as well as, nesting singly in concealed locations when nesting low, especially in the submerged forest of Melaleuca cajuputi as in the vast hectare (> 200,000 hectares) of Melalueca forest along the coastal areas of Terengganu. So, Melaleuca forest mapping and flower mapping can be reliable methods for determining this species distribution as the main source of nectar and pollen for these aforementioned honey bees. In ecology, biomass can be defined as accumulation of living matter which is useful as a biophysical index for mapping of flower in forest. In this study, we used SPOT-5 and RADARSAT-1 for inventory of Melaleuca forest in Marang and developed Above Ground Biomass (AGB) estimation model as indirect index for obtaining and producing distribution of Melaleuca cajuputi flowers. Also, Apis dorsata colonies distribution and motorbike parking points of honey hunters were collected using GPS in field survey to determine distribution of colonies and improve searching ability in Apis dorsata colonies harvesting by honey hunters in the study area. The Melaleuca forest, located in Marang, Terengganu, Malaysia which is lying in upper left latitude 5°17'15.473"N, and longitude 103°05'25.021"E and lower right latitude 4°37'55.236" N, longitude 103°45'47.568"E was chosen for this research. SPOT-5 was enhanced, classified and vectorized using image processing software for the purpose of Melaleuca forest mapping. Based on the image analysis of the SPOT-5 image the Melaleuca forest were classified as five classes Melaleuca Cajuputi, Acacia auriculiformis, non-vegetation, water bodies and Cloud/haze/Shadow. The analysis showed that Melaleuca cajuputi covered 76,061.73ha (61.72%), Acacia auriculiformis 24,484.32ha (19.88%), non-vegetation 9,991.76ha (8.11%), water bodies 2,203.47ha (1.79%) and Cloud/Haze/Shadow 10,491.86ha (8.51%) with an overall classification accuracy of 91.79% while the statistics value obtained from kappa coefficient was more than 0.86 which is relatively quite good results for image processing. Based on Melaleuca forest inventory, 10 plots of 10

Comparison of fusion of different algorithms in mapping of Melaleuca forest in Marang district, Malaysia

Melaleuca cajuputi and Acacia auriculiformis trees are major sources of nectar and pollen for Apis dorsata and the colonies are a major source of honey to the rural poor, honey hunters. Honey is a supplementary income to many of these people (including school children) in the Marang district, Terengganu. In this study, Marang area with 270 square kilometers was chosen as pilot study area in Terengganu state for mapping M. cajuputi and A. auriculiformis as two dominant species in low land secondary forest in Terengganu state. To inventory and produce land use map of Melaleuca forest in Marang area, in this study SPOT-5 satellite image in multispectral mode with 10 meter resolution which is acquired in 2007 as optical satellite was utilized. Most images from optical satellites have some null data from ground because of clouds and shadow of clouds. To solve this problem, Hue, Saturation and value (HSV) and Principal Component Analysis (PCA) were used as fusion techniques to replace null data with microwave data which taken from Radarsat-1 image in C-band with 25 meter resolution image. Accordingly, fusion technique which was used in this research not only was a technique to improve information but also caused the accuracy increasing than land use map by just only SPOT-5 image. Also between two different fusion techniques, PCA shows the better result than HSV as two different fusion techniques

A review of optical methods for assessing nitrogen contents during rice growth

Concerns over the use of nitrogen have been increasing due to the high cost of fertilizers and environmental pollution caused by excess nitrogen applications in paddy fields. Several methods are available to assess the amount of nitrogen in crops. However, they are either expensive, time consuming, inaccurate, and/or require specialists to operate the tools. Researchers have recently suggested remote sensing of chlorophyll content in crop canopies as a low-cost alternative to determine plant nitrogen status. This article describes the most recent technologies and the suitability of different remote sensing platforms for determining the status of chlorophyll content and nitrogen in crops. Finally, the role of vegetation indices in nutrient assessment is explained. Among different remote sensing platforms, a low altitude remote sensing system using digital cameras, which record data in visible bands can be used to determine the status of nitrogen and chlorophyll content. However, the vegetation indices need to be correctly chosen for best results

Multi-Spectral Images Tetracam agriculture Digital Camera to Estimate Nitrogen and Grain Yield of Rice at difference Growth Stages

Several methods are available to monitor the nitrogen content of rice during its various growth stages. However, monitoring still requires a quick, simple, accurate and inexpensive technique that needs to be developed. In this study, Tetracam Agriculture Digital Camera was used to acquire high spatial and temporal resolution images to determine the status of N and predict the grain yield of rice (Oryza sativa L.). Twelve pots of rice were subjected to four different N treatments (0, 125, 175 and 250 kg ha-1). Three replicates were arranged in a randomized complete block design to determine the status of N and predict rice yield. The images were captured at different growth stages (i.e., tillering, panicle initiation, booting and heading stage) of rice in each pot. N and grain yield were significantly correlated with NDVI (R2 = 0.78) and GNDVI (R2 = 0.88), especially at the panicle initiation and booting stages, respectively. The study demonstrated the suitability of using the Tetracam images as a sensor for estimating chlorophyll content and N. Moreover, the findings showed that the images revealed their potential use in forecasting grain yield at different growth stages of rice

Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale

Nitrogen is an important variable for paddy farming management. The objectives of this study were to develop and test a new method to determine the status of nitrogen and chlorophyll content in rice leaf by analysing and considering all visible bands derived from images captured using a conventional digital camera. The images from the 6-pannel leaf colour chart were acquired using Basler Scout scA640-70fc under light-emitting diode lighting, in which principal component analysis was used to retain the lower order principal component to develop a new index. Digital photographs of the upper most collared leaf of rice (Oriza sativa L.), grown over a range of soils with different nitrogen treatments, were processed into 11 indices and IPCA through six growth stages. Also a conventional digital camera mounted to an unmanned aerial vehicle was used to acquire images over the rice canopy for the purpose of verification. The result indicated that the conventional digital camera at the both leaf (r = −0.81) and the canopy (r = 0.78) scale could be used as a sensor to determine the status of chlorophyll content in rice plants through different growth stages. This indicates that conventional low-cost digital cameras can be used for determining chlorophyll content and consequently for monitoring nitrogen content of the growing rice plant, thus offering a potentially inexpensive, fast, accurate and suitable tool for rice growers. Additionally, results confirmed that a low cost LARS system would be well suited for high spatial and temporal resolution images and data analysis for proper assessment of key nutrients in rice farming in a fast, inexpensive and non-destructive way

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km(2): dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of similar to 15 g.kg(-1) and a range of 30 g.kg(-1) in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information

How can unmanned aerial vehicles be used for detecting weeds in agricultural fields?

Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts

Determination of chlorophyll and nitrogen content in rice leaves at various growth stages

Nitrogen (N) is an important input variable for rice farming. Concerns over the use of nitrogen have increased due to the increase in fertilizer costs and environmental pollution from excess nitrogen applications in paddy fields. Several methods are available to assess nitrogen in crops. However, they are either expensive, time consuming, inaccurate or needs specialists to operate the tools. Recently researchers suggest remote sensing of chlorophyll content in crop canopies as a low-cost alternative to determine plant N status. Among different remote sensing platforms, low altitude remote sensing (LARS), which is a relatively new concept of acquiring earth imagery at a low altitude using unmanned aerial vehicle (UAV), is an attractive method and being promoted by researchers to determine status of chlorophyll content and nitrogen in rice field. The high spatial and temporal resolution, fast way to collect data and relatively low data acquisition cost would suggest this platform as ideal tool for monitoring crops through out the plant‟s growth. Furthermore, researchers have found that visible wavelengths would be useful for determining nutrient status and biomass in crops. Thus, the main objective of this study was to develop and test new methods for determining the status of chlorophyll content and N in rice leaf by analyzing and considering information derived from the images captured using conventional digital cameras (Panasonic Lumix DMC-TZ10 and Basler Scout scA640-70fc) and a new generation of multispectral camera (Tetracam ADC). Digital photographs of the upper most collared leaf of rice (Oriza sativa L.) in both leaf and canopy scale, grown over a range of N treatments, were processed into all published indices and also IPCA, which is a new index for rice developed in this study based on utilizing all three visible bands and principal component analysis. Also conventional digital camera mounted to an UAV was used to acquire image over the rice canopy for verification purpose. The results indicate that conventional digital camera (r = -0.81) and Tetracam ADC (r = 0.89) could be used as sensors to determine the status of chlorophyll content in rice plants through different growth stages. Also IPCA shows significant negative correlation not only in leaf scale (r = -0.81) but also in canopy scale when UAV was utilized to capture images (r = -0.78). This indicates that conventional low-cost digital cameras and Tetracam ADC cameras, both can be used for determining chlorophyll content and consequently monitor Chlorophyll and N content of the growing rice plants, and also offers a potentially inexpensive, fast, accurate and suitable tool for rice growers. Additionally, results exhibited that a low cost LARS system would be well suited for high spatial and temporal resolution images and data analysis for proper assessment of key nutrients in rice farming in a fast, inexpensive and non-destructive way. Hence, the results of this study could be a significant contribution to develop the site-specific management decision for in-season, variable rate fertilizer application towards sustainable agriculture

Soil toxic elements determination using integration of Sentinel-2 and Landsat-8 images: Effects of fusion techniques on model performance

Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8- OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value, Brovey, principal component analysis, Gram-Schmidt, wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least square regression (PLSR) model developed on genetic algorithm (GA)- selected laboratory visible-near infrared-shortwave infrared (VNIR–SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements’ prediction models

In vivo fish diet discrimination using selected hyperspectral image classification methods

International audienceThe main aim of this study was to evaluate the performance of different supervised classification methods to discriminate live fish based on their diet received during cultivation using hyperspectral imagery system. 160 rainbow trout were fed either a commercial based diet or completely plant-based diet. Hyperspectral images of the live fish acquired in the spectral region of 394-1090 nm. Spectra were extracted from the region of interest and pre-processed using Savitzky-Golay smoothing algorithm to remove noise. Afterward, three classifiers including support vector machine, random forest and k-nearest neighbors were used. According to the criteria of correct classification rate and kappa coefficient, the support vector machine with linear kernel was achieved the best performance for classifying live fish due to their diet