Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their productivity, etc. In this paper, we propose a deep learning based framework for oil palm tree detection and counting using high-resolution remote sensing images for Malaysia. Unlike previous palm tree detection studies, the trees in our study area are more crowded and their crowns often overlap. We use a number of manually interpreted samples to train and optimize the convolutional neural network (CNN), and predict labels for all the samples in an image dataset collected through the sliding window technique. Then, we merge the predicted palm coordinates corresponding to the same palm tree into one palm coordinate and obtain the final palm tree detection results. Based on our proposed method, more than 96% of the oil palm trees in our study area can be detected correctly when compared with the manually interpreted ground truth, and this is higher than the accuracies of the other three tree detection methods used in this study.

Satellite precipitation products (SPPs) potentially constitute an alternative to sparse rain gauge networks for assessing the spatial distribution of precipitation. However, applications of these products are still limited due to the lack of robust quality assessment. This study compares daily, monthly, seasonal, and annual rainfall amount at 342 rain gauges over Malaysia to estimations using five SPPs (3B42RT, 3B42V7, GPCP-1DD, PERSIANN-CDR, and CMORPH) and a ground-based precipitation product (APHRODITE). The performance of the precipitation products was evaluated from 2003 to 2007 using continuous (RMSE, R², ME, MAE, and RB) and categorical (ACC, POD, FAR, CSI, and HSS) statistical approaches. Overall, 3B42V7 and APHRODITE performed the best, while the worst performance was shown by GPCP-1DD. 3B42RT, 3B42V7, and PERSIANN-CDR slightly overestimated observed precipitation by 2%, 4.7%, and 2.1%, respectively. By contrast, APHRODITE and CMORPH significantly underestimated precipitations by 19.7% and 13.2%, respectively, whereas GPCP-1DD only slightly underestimated by 2.8%. All six precipitation products performed better in the northeast monsoon than in the southwest monsoon. The better performances occurred in eastern and southern Peninsular Malaysia and in the north of East Malaysia, which receives higher rainfall during the northeast monsoon, whereas poor performances occurred in the western and dryer Peninsular Malaysia. All precipitation products underestimated the no/tiny (<1 mm/day) and extreme (≥20 mm/day) rainfall events, while they overestimated low (1–20 mm/day) rainfall events. 3B42RT and 3B42V7 showed the best ability to detect precipitation amounts with the highest HSS value (0.36). Precipitations during flood events such as those which occurred in late 2006 and early 2007 were estimated the best by 3B42RT and 3B42V7, as shown by an R² value ranging from 0.49 to 0.88 and 0.52 to 0.86, respectively. These results on SPPs’ uncertainties and their potential controls might allow sensor and algorithm developers to deliver better products for improved rainfall estimation and thus improved water management.

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 gives a global overview of some aspects of small satellite developments since the launch of Sputnik‐1 50 years ago. These developments are offering new opportunities for remote sensing. The earliest satellites were small but, as time went on, the satellites that were flown were developed to serve several different projects and they became larger and more expensive and took a long time to design, build and be launched. One of the extreme examples was Envisat. For these large satellites compromises often had to be made between different objectives and different instruments. A failure of the whole system meant the death of many different projects. The future is likely to see more small satellites, each of which is dedicated to a particular mission objective and carries a single instrument. Through this approach more and more countries around the world are becoming involved in Earth observation from space, not just in using the data from the major established systems but also in constructing their own systems. There were some small, low‐cost satellites in the early days, but they were overlooked or considered toys by the space community. The first microsatellites were built by enthusiasts of the amateur radio community and launched in the early 1960s. The invention/introduction of the microprocessor in the 1970s represented a quantum jump for the onboard capabilities of a spacecraft. This technology introduction represented a prime catalyst in the development of microsatellites since it enabled small physical structures in support of sophisticated data handling applications. The engineering of microsatellites, which emerged in the early 1980s, took a radical change of approach from the custom design of traditional spacecraft, namely a design‐to‐capability scheme to achieve cost reductions by focusing on available, and existing technologies using a general purpose bus and ‘off‐the‐shelf’ components and instruments. The new approach of small satellite design was pioneered by Surrey Satellite Technology Ltd (SSTL) of Surrey University, UK. SSTL's lead has now been followed by various companies and space agencies throughout the world. A key feature of this work is the development of microsatellite technology transfer programmes, providing partnerships and on‐the‐job training of engineers and scientists of foreign national organizations in cooperative programmes – in particular to those who were not in a position to start or afford their own space projects – to participate in the development of their own microsatellites. In addition to discussing these developments, this article also covers small satellite classification, small satellite initiatives in the USA, small satellite development in the rest of the world, some aspects of the technology and applications of small satellites, and small satellites developed by universities, particularly the CubeSats programme. Today, small satellites are changing the economics of space. These spacecraft embrace cutting edge Commercial Off‐The‐Shelf (COTS) technology, permitting novel and less‐expensive ways to perform meaningful observation missions, although there are various technical challenges. There are several synthetic aperture radar (SAR) and hyperspectral imaging missions on small satellites in operation and in planning.

Photograph taken around themidpoint of the 40-year periodto which this Editorial refers.This editorial has its origins in a keynote presentation entitled ‘The Evolution of the Development of Remote...

Gridded climate products (GCPs) provide a potential source for representing weather in remote, poor quality or short-term observation regions. The accuracy of three long-term GCPs (Asian Precipitation—Highly-Resolved Observational Data Integration towards Evaluation of Water Resources: APHRODITE, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record: PERSIANN-CDR and National Centers for Environmental Prediction Climate Forecast System Reanalysis: NCEP-CFSR) was analyzed for the Kelantan River Basin (KRB) and Johor River Basin (JRB) in Malaysia from 1983 to 2007. Then, these GCPs were used as inputs into calibrated Soil and Water Assessment Tool (SWAT) models, to assess their capability in simulating streamflow. The results show that the APHRODITE data performed the best in precipitation estimation, followed by the PERSIANN-CDR and NCEP-CFSR datasets. The NCEP-CFSR daily maximum temperature data exhibited a better correlation than the minimum temperature data. For streamflow simulations, the APHRODITE data resulted in strong results for both basins, while the NCEP-CFSR data showed unsatisfactory performance. In contrast, the PERSIANN-CDR data showed acceptable representation of observed streamflow in the KRB, but failed to track the JRB observed streamflow. The combination of the APHRODITE precipitation and NCEP-CFSR temperature data resulted in accurate streamflow simulations. The APHRODITE and PERSIANN-CDR data often underestimated the extreme precipitation and streamflow, while the NCEP-CFSR data produced dramatic overestimations. Therefore, a direct application of NCEP-CFSR data should be avoided in this region. We recommend the use of APHRODITE precipitation and NCEP-CFSR temperature data in modeling of Malaysian water resources.