• Energy Research

Abstract. Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

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.

Abstract. 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.

Les forêts du monde connaissent des changements rapides en raison de l'utilisation des terres et du changement climatique. Cependant, une carte détaillée du gain forestier mondial à de fines résolutions spatiales et temporelles est toujours manquante. Pour combler cette lacune, nous avons développé un cadre automatique pour cartographier le gain forestier annuel à l'échelle mondiale à l'aide des séries chronologiques Landsat, de l'algorithme LandTrendr et de la plate-forme Google Earth Engine (GEE). Tout d'abord, des échantillons de forêts stables collectés sur la base du premier ensemble d'échantillons toutes saisons (FAST) et d'une méthode de migration automatisée des échantillons ont été utilisés pour déterminer les seuils annuels du ratio de brûlure normalisée (NBR) pour la détection du gain forestier. Deuxièmement, avec les séries chronologiques NBR de 1982 à 2021 et l'algorithme LandTrendr, nous avons produit un ensemble de données sur l'année de gain forestier mondial de 1983 à 2021 sur la base d'un ensemble de règles de décision. Nos résultats révèlent que plus de 60% des gains se sont produits en Russie, au Canada, aux États-Unis, en Indonésie et en Chine, et environ la moitié du gain forestier mondial s'est produit entre 2001 et 2010. La carte des gains forestiers développée dans cette étude a montré une bonne cohérence avec les inventaires statistiques et les produits régionaux et mondiaux indépendants. Notre ensemble de données peut être utile pour la recherche pertinente pour les politiques sur le cycle mondial du carbone, et notre méthode fournit une approche efficace et transférable pour surveiller d'autres types de dynamique de la couverture terrestre. Los bosques del mundo están experimentando rápidos cambios debido al uso de la tierra y al cambio climático. Sin embargo, todavía falta un mapa detallado de la ganancia forestal global con resoluciones espaciales y temporales finas. Para llenar este vacío, desarrollamos un marco automático para mapear la ganancia forestal anual a nivel mundial utilizando series de tiempo Landsat, el algoritmo LandTrendr y la plataforma Google Earth Engine (GEE). En primer lugar, se utilizaron muestras forestales estables recolectadas en función del primer conjunto de muestras para todas las estaciones (FAST) y un método automatizado de migración de muestras para determinar los umbrales anuales de la relación de combustión normalizada (NBR) para la detección de la ganancia forestal. En segundo lugar, con la serie temporal NBR de 1982 a 2021 y el algoritmo LandTrendr, produjimos un conjunto de datos del año de ganancia forestal mundial de 1983 a 2021 basado en un conjunto de reglas de decisión. Nuestros resultados revelan que se produjeron ganancias de más del 60% en Rusia, Canadá, Estados Unidos, Indonesia y China, y aproximadamente la mitad de la ganancia forestal mundial se produjo entre 2001 y 2010. El mapa de ganancia forestal desarrollado en este estudio mostró una buena consistencia con los inventarios estadísticos y los productos regionales y globales independientes. Nuestro conjunto de datos puede ser útil para la investigación relevante para las políticas sobre el ciclo global del carbono, y nuestro método proporciona un enfoque eficiente y transferible para monitorear otros tipos de dinámica de la cubierta terrestre. The world's forests are experiencing rapid changes due to land-use and climate change. However, a detailed map of global forest gain at fine spatial and temporal resolutions is still missing. To fill this gap, we developed an automatic framework for mapping annual forest gain globally using Landsat time series, the LandTrendr algorithm, and the Google Earth Engine (GEE) platform. First, stable forest samples collected based on the first all-season sample set (FAST) and an automated sample migrate method were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2021 and LandTrendr algorithm, we produced dataset of global forest gain year from 1983 to 2021 based on a set of decision rules. Our results reveal that over 60% gains occurred in Russia, Canada, the United States, Indonesia and China, and approximately half of global forest gain occurred between 2001 and 2010. The forest gain map developed in this study exhibited good consistency with statistical inventories and independent regional and global products. Our dataset can be useful for policy-relevant research on the global carbon cycle, and our method provides an efficient and transferable approach for monitoring other types of land cover dynamics. تشهد غابات العالم تغيرات سريعة بسبب استخدام الأراضي وتغير المناخ. ومع ذلك، لا تزال هناك خريطة مفصلة لمكاسب الغابات العالمية بدقة مكانية وزمنية دقيقة مفقودة. لسد هذه الفجوة، قمنا بتطوير إطار عمل تلقائي لرسم خرائط المكاسب السنوية للغابات على مستوى العالم باستخدام سلسلة Landsat الزمنية، وخوارزمية LandTrendr، ومنصة Google Earth Engine (GEE). أولاً، تم استخدام عينات الغابات المستقرة التي تم جمعها بناءً على أول مجموعة عينات طوال الموسم (FAST) وطريقة ترحيل العينات الآلية لتحديد العتبات السنوية لنسبة الحروق الطبيعية (NBR) للكشف عن مكاسب الغابات. ثانيًا، مع السلاسل الزمنية لـ NBR من 1982 إلى 2021 وخوارزمية LandTrendr، أنتجنا مجموعة بيانات من السنة العالمية لكسب الغابات من 1983 إلى 2021 بناءً على مجموعة من قواعد القرار. تكشف نتائجنا أن أكثر من 60 ٪ من المكاسب حدثت في روسيا وكندا والولايات المتحدة وإندونيسيا والصين، وأن ما يقرب من نصف المكاسب العالمية للغابات حدثت بين عامي 2001 و 2010. أظهرت خريطة مكاسب الغابات التي تم تطويرها في هذه الدراسة اتساقًا جيدًا مع قوائم الجرد الإحصائية والمنتجات الإقليمية والعالمية المستقلة. يمكن أن تكون مجموعة البيانات الخاصة بنا مفيدة للبحوث ذات الصلة بالسياسات حول دورة الكربون العالمية، وتوفر طريقتنا نهجًا فعالًا وقابلًا للتحويل لرصد الأنواع الأخرى من ديناميكيات الغطاء الأرضي.