OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national research facility to monitor and assess trends, and improve predictions, of Australia's terrestrial biosphere and climate. This paper describes the evolution, design, and current status of OzFlux as well as provides an overview of data processing. We analyse measurements from all sites within the Australian portion of the OzFlux network and two sites from New Zealand. The response of the Australian biomes to climate was largely consistent with global studies except that Australian systems had a lower ecosystem water-use efficiency. Australian semi-arid/arid ecosystems are important because of their huge extent (70 %) and they have evolved with common moisture limitations. We also found that Australian ecosystems had a similar radiation-use efficiency per unit leaf area compared to global values that indicates a convergence toward a similar biochemical efficiency. The two New Zealand sites represented extremes in productivity for a moist temperate climate zone, with the grazed dairy farm site having the highest GPP of any OzFlux site (2620 gC m⁻² yr⁻¹) and the natural raised peat bog site having a very low GPP (820 gC m⁻² yr⁻¹). The paper discusses the utility of the flux data and the synergies between flux, remote sensing, and modelling. Lastly, the paper looks ahead at the future direction of the network and concludes that there has been a substantial contribution by OzFlux, and considerable opportunities remain to further advance our understanding of ecosystem response to disturbances, including drought, fire, land-use and land-cover change, land management, and climate change, which are relevant both nationally and internationally. It is suggested that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing the delicately balanced ecosystems in Australasia.

Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean, sunflower, and rapeseed. The growth of the oil palm industry causes degradation to the environment, especially when the expansion of plantations goes uncontrolled. Remote sensing is a useful tool to monitor the development of oil palm plantations. In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability, this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring. In addition, the existing knowledge gaps are identified and recommendations for further research are given.

Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM.

This article demonstrates some techniques for studying the age of oil palm trees (Elaeis guineensis Jacq.) using the Disaster Monitoring Constellation 2 from the UK (UK-DMC 2) and Advanced Land Observing Satellite phased array L-band synthetic aperture radar (ALOS PALSAR) remote-sensing data at a private oil palm estate in southern peninsular Malaysia. Several techniques were explored with UK-DMC 2 data, namely (1) radiance, vegetation indices, and fraction of shadow; (2) texture measurement; (3) classifications, namely Iterative Self-Organizing Data Analysis Technique (ISODATA) classification, maximum-likelihood classification (MLC), and random forest (RF) classification; (4) in terms of ALOS PALSAR data, the correlation of polarizations (i.e. horizontal transmitting and horizontal receiving (termed HH polarization) and horizontal transmitting and vertical receiving (termed HV polarization)) and the ratio of these polarizations to the age of oil palm trees. From the results, band 1 (near-infrared) of UK-DMC 2, fraction of shadow, and mean filter from the grey-level co-occurrence matrix (GLCM) demonstrated strong correlation of determination (R ² = 0.76–0.80) with the age of oil palm trees, while the ALOS PALSAR HH polarization could correlate moderately strongly (R ² = 0.49) with the age of oil palm trees. Adding fraction of shadow and UK-DMC 2 data using the RF method further improved the overall accuracy of age classification from 45.3% (MLC method) to 52.9%. This study concluded that texture measurement (GLCM mean) and fraction of shadow are useful for studying the age of oil palm trees, although discriminating variation in age between mature oil palm trees is difficult because the leaf area index development of mature oil palm trees stabilizes at about 10 years of age. Future studies should involve height information, because this has the potential to be used as one of the most important variables for studying the age of oil palm trees.