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Special Issue "Mapping the Dynamics of Forest Plantations in Tropical and Subtropical Regions from Multi-Source Remote Sensing"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2015)

Special Issue Editors

Guest Editor
Prof. Xiangming Xiao

Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, 101 David L. Boren Blvd., Norman, Oklahoma 73019-5300, USA
Website | E-Mail
Interests: applications of remote sensing and gis in ecosystems science and natural resources; land use and cover changes; ecosystem service assessment; biogeochemistry of terrestrial ecosystems; ecosystem modeling at large spatial scales; integrated impact assessment of climate change; ecology and epidemiology of infectious diseases.
Guest Editor
Dr. Jinwei Dong

Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, 101 David L. Boren Blvd., Norman, Oklahoma 73019-5300, USA
Website | E-Mail
Interests: land cover and land use change; ecological remote sensing; effects of global change on ecosystem services

Special Issue Information

Dear Colleagues,

With the development of the economy, areas of industrial forest plantations have expanded enormously in recent years across tropical and subtropical regions in the world. The main species include rubber, oil palm, teak, eucalyptus, acacia, pine and bamboo. The rapid expansion of these forest plantations is likely to have substantial impacts on biodiversity, terrestrial carbon cycle, hydrology and climate. However, our capacity to better understand and predict these impacts is still constrained by lack of accurate and updated data on spatial distribution, area, and dynamics of forest plantations.

Satellite remote sensing plays an important role in mapping spatial distribution and temporal dynamics of forests and plantations. A number of studies have used optical satellite images (Landsat and MODIS) to identify and map industrial forest plantations, e.g., rubber plantations, oil palm plantations, eucalyptus plantations, teak, acacia, and bamboo, and the main difficulty to map industrial forest plantations is the similar spectral characteristics between natural forests and forest plantations. Recently, a number of studies have evaluated the potential of synthetic aperture radar (SAR) and Light Detection and Ranging (LiDAR) images to map plantations as well. However, these studies are not systematic and so far no high quality maps of plantations are available on the regional, continental and global scales.

This special issue aims to review and synthesize the latest progress in plantation mapping algorithms, spatio-temporal changes of plantations, and their impacts on the environment and climate. Prospective authors are invited to contribute to this special Issue of Remote Sensing by submitting an original manuscript. Contributions may focus on, but are not limited to:

1)      New and improved algorithms for mapping plantations, including type, stand age, phenology, structure, and biomass;
2)      Application of multi-sensor and multi-scale remote sensing on plantation mapping;
3)      Process and pattern of plantation expansion;
4)      Likely effects of plantation expansion on biodiversity, carbon, water and climate;
5)      Citizen science and crowdsourcing in land cover mapping.

Prof. Xiangming Xiao
Dr. Jinwei Dong
Guest Editors


Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).

Published Papers (9 papers)

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Research

Open AccessArticle Detection of Drought-Induced Hickory Disturbances in Western Lin An County, China, Using Multitemporal Landsat Imagery
Remote Sens. 2016, 8(4), 345; doi:10.3390/rs8040345
Received: 15 December 2015 / Revised: 29 February 2016 / Accepted: 4 April 2016 / Published: 20 April 2016
PDF Full-text (6118 KB) | HTML Full-text | XML Full-text
Abstract
Hickory plantations play an important role in improving local farmers’ economic conditions, but extreme drought in July–August 2013 seriously influenced hickory nut production. It is necessary to understand the extent and magnitude of this drought-induced hickory disturbance through mapping its spatial distribution using
[...] Read more.
Hickory plantations play an important role in improving local farmers’ economic conditions, but extreme drought in July–August 2013 seriously influenced hickory nut production. It is necessary to understand the extent and magnitude of this drought-induced hickory disturbance through mapping its spatial distribution using remote sensing data. This paper proposes a new approach to examine hickory disturbance based on multitemporal Landsat imagery. Ratios of green vegetation to soil fractions were calculated, in which the green vegetation and soil fractions were extracted from Landsat multispectral imagery using the linear spectral mixture analysis approach. We used the differences between before-drought and after-drought ratios to detect hickory disturbances. Four disturbance levels—non-disturbance, light, medium, and severe—were grouped according to the field survey data. The spatial distribution of these four levels was developed using the ratio-based approach. The result indicates that this approach is effective to detect drought-induced hickory disturbance and may be transferred to detect other kinds of disturbances, such as forest disease and selective logging. Cautions should be taken to properly select image acquisition dates and the change detection period, in addition to the approach itself. Full article
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Open AccessArticle Regional Mapping of Plantation Extent Using Multisensor Imagery
Remote Sens. 2016, 8(3), 236; doi:10.3390/rs8030236
Received: 31 December 2015 / Revised: 15 February 2016 / Accepted: 4 March 2016 / Published: 14 March 2016
Cited by 1 | PDF Full-text (7083 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Industrial forest plantations are expanding rapidly across Monsoon Asia and monitoring extent is critical for understanding environmental and socioeconomic impacts. In this study, new, multisensor imagery were evaluated and integrated to extract the strengths of each sensor for mapping plantation extent at regional
[...] Read more.
Industrial forest plantations are expanding rapidly across Monsoon Asia and monitoring extent is critical for understanding environmental and socioeconomic impacts. In this study, new, multisensor imagery were evaluated and integrated to extract the strengths of each sensor for mapping plantation extent at regional scales. Two distinctly different landscapes with multiple plantation types were chosen to consider scalability and transferability. These were Tanintharyi, Myanmar and West Kalimantan, Indonesia. Landsat-8 Operational Land Imager (OLI), Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2), and Sentinel-1A images were fused within a Classification and Regression Tree (CART) framework using random forest and high-resolution surveys. Multi-criteria evaluations showed both L-and C-band gamma nought γ° backscatter decibel (dB), Landsat reflectance ρλ, and texture indices were useful for distinguishing oil palm and rubber plantations from other land types. The classification approach identified 750,822 ha or 23% of the Taninathryi, Myanmar, and 216,086 ha or 25% of western West Kalimantan as plantation with very high cross validation accuracy. The mapping approach was scalable and transferred well across the different geographies and plantation types. As archives for Sentinel-1, Landsat-8, and PALSAR-2 continue to grow, mapping plantation extent and dynamics at moderate resolution over large regions should be feasible. Full article
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Open AccessArticle Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets
Remote Sens. 2016, 8(2), 117; doi:10.3390/rs8020117
Received: 9 October 2015 / Revised: 20 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
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Abstract
Eucalyptus, a short-rotation plantation, has been expanding rapidly in southeast China in recent years owing to its short growth cycle and high yield of wood. Effective identification of eucalyptus, therefore, is important for monitoring land use changes and investigating environmental quality. For this
[...] Read more.
Eucalyptus, a short-rotation plantation, has been expanding rapidly in southeast China in recent years owing to its short growth cycle and high yield of wood. Effective identification of eucalyptus, therefore, is important for monitoring land use changes and investigating environmental quality. For this article, we used remote sensing images over 15 years (one per year) with a 30-m spatial resolution, including Landsat 5 thematic mapper images, Landsat 7-enhanced thematic mapper images, and HJ 1A/1B images. These data were used to construct a 15-year Normalized Difference Vegetation Index (NDVI) time series for several cities in Guangdong Province, China. Eucalyptus reference NDVI time series sub-sequences were acquired, including one-year-long and two-year-long growing periods, using invested eucalyptus samples in the study region. In order to compensate for the discontinuity of the NDVI time series that is a consequence of the relatively coarse temporal resolution, we developed an inverted triangle area methodology. Using this methodology, the images were classified on the basis of the matching degree of the NDVI time series and two reference NDVI time series sub-sequences during the growing period of the eucalyptus rotations. Three additional methodologies (Bounding Envelope, City Block, and Standardized Euclidian Distance) were also tested and used as a comparison group. Threshold coefficients for the algorithms were adjusted using commission–omission error criteria. The results show that the triangle area methodology out-performed the other methodologies in classifying eucalyptus plantations. Threshold coefficients and an optimal discriminant function were determined using a mosaic photograph that had been taken by an unmanned aerial vehicle platform. Good stability was found as we performed further validation using multiple-year data from the high-resolution Gaofen Satellite 1 (GF-1) observations of larger regions. Eucalyptus planting dates were also estimated using invested eucalyptus samples and the Root Mean Square Error (RMSE) of the estimation was 84 days. This novel and reliable method for classifying short-rotation plantations at small scales is the focus of this study. Full article
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Open AccessArticle Extent and Area of Swidden in Montane Mainland Southeast Asia: Estimation by Multi-Step Thresholds with Landsat-8 OLI Data
Remote Sens. 2016, 8(1), 44; doi:10.3390/rs8010044
Received: 5 November 2015 / Revised: 16 December 2015 / Accepted: 16 December 2015 / Published: 7 January 2016
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Abstract
Information on the distribution, area and extent of swidden agriculture landscape is necessary for implementing the program of Reducing Emissions from Deforestation and Forest Degradation (REDD), biodiversity conservation and local livelihood improvement. To our knowledge, explicit spatial maps and accurate area data on
[...] Read more.
Information on the distribution, area and extent of swidden agriculture landscape is necessary for implementing the program of Reducing Emissions from Deforestation and Forest Degradation (REDD), biodiversity conservation and local livelihood improvement. To our knowledge, explicit spatial maps and accurate area data on swidden agriculture remain surprisingly lacking. However, this traditional farming practice has been transforming into other profit-driven land use, like tree plantations and permanent cash agriculture. Swidden agriculture is characterized by a rotational and dynamic nature of agroforestry, with land cover changing from natural forests, newly-cleared swiddens to different-aged fallows. The Operational Land Imager (OLI) onboard the Landsat-8 satellite has visible, near-infrared and shortwave infrared bands, which are sensitive to the changes in vegetation cover, land surface moisture content and soil exposure, and therefore, four vegetation indices (VIs) were calculated, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), the Normalized Burn Ratio (NBR) and the Soil Adjusted Vegetation Index (SAVI). In this study, we developed a multi-step threshold approach that uses a combination of thresholds of four VIs and local elevation range (LER) and applied it to detect and map newly-opened swiddens and different-aged fallows using OLI imagery acquired between 2013 and 2015. The resultant Landsat-derived swidden agriculture maps have high accuracy with an overall accuracy of 86.9% and a Kappa coefficient of 0.864. The results of this study indicated that the Landsat-based multi-step threshold algorithms could potentially be applied to monitor the long-term change pattern of swidden agriculture in montane mainland Southeast Asia since the late 1980s and also in other tropical regions, like insular Southeast Asia, South Asia, Latin America and Central Africa, where swidden agriculture is still common. Full article
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Open AccessArticle The Use of Multi-Temporal Landsat Imageries in Detecting Seasonal Crop Abandonment
Remote Sens. 2015, 7(9), 11974-11991; doi:10.3390/rs70911974
Received: 17 April 2015 / Revised: 20 August 2015 / Accepted: 25 August 2015 / Published: 18 September 2015
Cited by 1 | PDF Full-text (2125 KB) | HTML Full-text | XML Full-text
Abstract
Abandonment of agricultural land is a global issue and a waste of resources and brings a negative impact on the local economy. It is also one of the key contributing factors in certain environmental problems, such as soil erosion and carbon sequestration. In
[...] Read more.
Abandonment of agricultural land is a global issue and a waste of resources and brings a negative impact on the local economy. It is also one of the key contributing factors in certain environmental problems, such as soil erosion and carbon sequestration. In order to address such problems related to land abandonment, their spatial distribution must first be precisely identified. Hence, this study proposes the use of multi-temporal Landsat imageries, together with crop phenology information and an object-oriented classification technique, to identify abandoned paddy and rubber areas. Results indicate that Landsat time-series images were highly beneficial and, in fact, essential in identifying abandoned paddy and rubber areas, particularly due to the unique phenology of these seasonal crops. To differentiate between abandoned and non-abandoned paddy areas, a minimum of three time-series images, mainly acquired during the planting seasons is required. For rubber, multi-temporal images should be examined in order to confirm the wintering season. The study demonstrates the advantages of using multi-temporal Landsat imageries in identifying abandoned paddy and rubber areas wherein an accuracy of 93.33% ± 14% and 83.33% ± 1%, respectively, were achieved. Full article
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Open AccessArticle Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery
Remote Sens. 2015, 7(5), 5660-5696; doi:10.3390/rs70505660
Received: 14 February 2015 / Accepted: 14 April 2015 / Published: 5 May 2015
Cited by 7 | PDF Full-text (3495 KB) | HTML Full-text | XML Full-text
Abstract
An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal,
[...] Read more.
An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p < 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer’s accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes. Full article
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Open AccessArticle National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China
Remote Sens. 2015, 7(5), 5534-5564; doi:10.3390/rs70505534
Received: 29 January 2015 / Revised: 16 April 2015 / Accepted: 22 April 2015 / Published: 4 May 2015
Cited by 5 | PDF Full-text (6792 KB) | HTML Full-text | XML Full-text
Abstract
Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land
[...] Read more.
Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%. Full article
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Open AccessArticle Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images
Remote Sens. 2015, 7(2), 1206-1224; doi:10.3390/rs70201206
Received: 13 September 2014 / Accepted: 14 January 2015 / Published: 23 January 2015
Cited by 7 | PDF Full-text (10574 KB) | HTML Full-text | XML Full-text
Abstract
Oil palm plantations have expanded rapidly. Estimating either positive effects on the economy, or negative effects on the environment, requires accurate maps. In this paper, three classification algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) were explored to map oil palm plantations
[...] Read more.
Oil palm plantations have expanded rapidly. Estimating either positive effects on the economy, or negative effects on the environment, requires accurate maps. In this paper, three classification algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) were explored to map oil palm plantations in Cameroon, using PALSAR 50 m Orthorectified Mosaic images and differently sized training samples. SVM had the ideal performance with overall accuracy ranging from 86% to 92% and a Kappa coefficient from 0.76 to 0.85, depending upon the training sample size (ranging from 20 to 500 pixels per class). The advantage of SVM was more obvious when the training sample size was smaller. K-Means required the user’s intervention, and thus, the accuracy depended on the level of his/her expertise and experience. For large-scale mapping of oil palm plantations, the Decision Tree algorithm outperformed both SVM and K-Means in terms of speed and performance. In addition, the decision threshold values of Decision Tree for a large training sample size agrees with the results from previous studies, which implies the possible universality of the decision threshold. If it can be verified, the Decision Tree algorithm will be an easy and robust methodology for mapping oil palm plantations. Full article
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Open AccessArticle Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images
Remote Sens. 2015, 7(1), 1048-1073; doi:10.3390/rs70101048
Received: 10 September 2014 / Revised: 5 January 2015 / Accepted: 12 January 2015 / Published: 19 January 2015
Cited by 5 | PDF Full-text (47145 KB) | HTML Full-text | XML Full-text
Abstract
Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by
[...] Read more.
Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by integration of PALSAR 50-m mosaic images and multi-temporal Landsat TM/ETM+ images. The L-band PALSAR 50-m mosaic images were used to map forests (including both natural forests and rubber trees) and non-forests. For those PALSAR-based forest pixels, we analyzed the multi-temporal Landsat TM/ETM+ images from 2000 to 2009. We first studied phenological signatures of deciduous rubber plantations (defoliation and foliation) and natural forests through analysis of surface reflectance, Normal Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) and generated a map of rubber plantations in 2009. We then analyzed phenological signatures of rubber plantations with different stand ages and generated a map, in 2009, of rubber plantation stand ages (≤5, 6–10, >10 years-old) based on multi-temporal Landsat images. The resultant maps clearly illustrated how rubber plantations have expanded into the mountains in the study area over the years. The results in this study demonstrate the potential of integrating microwave (e.g., PALSAR) and optical remote sensing in the characterization of rubber plantations and their expansion over time. Full article
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