Towards a global oil palm sample database: design and implications

Cheng, Yuqi; Yu, Le; Zhao, Yuanyuan; Xu, Yidi; Hackman, Kwame Oppong; Cracknell, A. P.; Gong, Peng

doi:10.1080/01431161.2017.1312622

Cited by 17 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the ALOS PALSAR and ALOS-2 PALSAR-2 have different satellite microwave sensor properties (e.g., frequency, off-nadir angle), the backscatter signals are relatively stable for the given period (2007-2010 and 2015-2016) as seen by comparing the distribution of backscattering values between different years (Cheng et al, 2017;Qin et al, 2017). Therefore, the consistency between ALOS PALSAR and ALOS-2 PALSAR-2 allows tracking the oil palm changes in the study period.…”

Section: Palsar/palsar-2 Product and Data Preparationmentioning

confidence: 99%

“…In this study, a multi-year training sample set (2007-2010, 2015 and 2016) was used to map the oil palm extent in Indonesia and Malaysia from 2007 to 2016. We used the training sample set for Malaysia from our previous study (Cheng et al, 2017) and interpreted the training datasets for Indonesia using the same interpretation method. The sample collection was mainly based on the high-resolution (<1m) images from Google Earth with the assistance of PALSAR/PALSAR-2 images.…”

Section: Training Sample Collection and Image Classificationmentioning

confidence: 99%

“…Remote sensing has been used in oil palm monitoring since 1990s. Progress has been made in oil palm mapping and change detection, including 1) data sources from optical satellite earth observations (Lee et al, 2016;Srestasathiern and Rakwatin, 55 2014) to microwave datasets such as Phased Array Type L-band Synthetic Aperture Radar (PALSAR) (Cheng et al, 2018;Dong et al, 2015), 2) spatiotemporal resolutions from regional to national scale (Miettinen et al, 2017) and from single to multi-decadal mapping (Gaveau et al, 2016;Miettinen et al, 2016), 3) interpretation methods from manual to semiautomatic identification (Cheng et al, 2017;Ordway et al, 2019), 4) products going from oil palm land cover maps to more detailed datasets on plantation structure, e.g. tree counting age and yield estimation (Balasundram et al, 60 2013;Tan et al, 2013) and etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Xu¹,

Yu²,

Li³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Increasing global demand of vegetable oils and biofuels results in significant oil palm expansion in Southeast Asia, predominately in Malaysia and Indonesia. The land conversion to oil palm plantations leads to deforestation, loss of biodiversity, and greenhouse gas emission over the past decades. Quantifying the consequences of oil palm expansion requires fine scale and frequently updated datasets of land cover dynamics. Previous studies focused on total changes for a multi-year interval without identifying the exact time of conversion, causing uncertainty in the timing of carbon emission estimates from 15 land cover change. Using Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we produced an Annual Oil Palm Area Dataset (AOPD) at 100-meter resolution in Malaysia and Indonesia from 2001 to 2016. We first mapped the oil palm extent using PALSAR/PALSAR-2 data for 2007-2010 and 2015-2016 and then applied a disturbance and recovery algorithm (BFAST) to detect land cover change time-points using MODIS data during the years without PALSAR data (2011-20 2014 and 2001-2006). The new oil palm land cover maps are assessed to have an accuracy of 86.61% in the mapping step (2007-2010 and 2015-2016). During the intervening years when MODIS data are used, 75.74% of the change detected time matched the timing of actual conversion using Google Earth and Landsat images. The AOPD dataset revealed spatiotemporal oil palm dynamics every year and shows that plantations expanded from 2.59 to 6.39 M ha and from 3.00 to 12.66 M ha in Malaysia and Indonesia, respectively (i.e., a net increase of 146.60% and 322.46%) between 2001 and 2016. The increasing 25 trends from our dataset are consistent with those from the national inventories, but slightly greater because of inclusion of smallholder oil palm plantations in our dataset. We highlight the capability of combining multiple resolution radar and optical satellite datasets in annual plantation mapping at large extent using image classification and statistical boundary-based change detection to achieve long time-series. The consistent characterization of oil palm dynamics can be further used in downstream applications. The annual oil palm plantation maps from 2001 to 2016 at 100 m resolution is published in the Tagged Image 30File Format with georeferencing information (GeoTIFF) at https://doi.org/10.5281/zenodo.3467071 .

show abstract

Section: Palsar/palsar-2 Product and Data Preparationmentioning

confidence: 99%

Section: Training Sample Collection and Image Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Xu¹,

Yu²,

Li³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, palm oil has become the world's most consumed vegetable oil, making up 35% of the total consumption of vegetable oil [3]. As a result of the increasing demand of palm oil, a considerable amount of land (e.g., existing arable land and forests) has been replaced by oil palm plantation areas [4,5]. The expansion of oil palm plantation areas has caused serious environmental problems, e.g., deforestation, the reduction of biodiversity, and the loss of ecosystem functioning, etc.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks

Dong

et al. 2018

Remote Sensing

103

View full text Add to dashboard Cite

Being an important economic crop that contributes 35% of the total consumption of vegetable oil, remote sensing-based quantitative detection of oil palm trees has long been a key research direction for both agriculture and environmental purposes. While existing methods already demonstrate satisfactory effectiveness for small regions, performing the detection for a large region with satisfactory accuracy is still challenging. In this study, we proposed a two-stage convolutional neural network (TS-CNN)-based oil palm detection method using high-resolution satellite images (i.e. Quickbird) in a large-scale study area of Malaysia. The TS-CNN consists of one CNN for land cover classification and one CNN for object classification. The two CNNs were trained and optimized independently based on 20,000 samples collected through human interpretation. For the large-scale oil palm detection for an area of 55 km2, we proposed an effective workflow that consists of an overlapping partitioning method for large-scale image division, a multi-scale sliding window method for oil palm coordinate prediction, and a minimum distance filter method for post-processing. Our proposed approach achieves a much higher average F1-score of 94.99% in our study area compared with existing oil palm detection methods (87.95%, 81.80%, 80.61%, and 78.35% for single-stage CNN, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), respectively), and much fewer confusions with other vegetation and buildings in the whole image detection results.

show abstract

“…Recently, oil palm has attracted lots of attention from governments and researchers, because it plays an essential role in maintaining carbon balance and possessing high economic value. In addition, the expansion of oil palm plantation area is condemned by the environmental protectors due to threatening the survival of native species, destroying the tropical rain forest and reducing the biodiversity (Busch et al, 2015;Cheng et al, 2017;Cheng et al, 2018;Carlson et al, 2018;Quezada et al, 2019). As a result, counting and detecting oil palm trees from high-resolution remotely sensed images is a significant work for better management, efficient fertilization and irrigation of oil palm plantation.…”

Section: Introductionmentioning

confidence: 99%

Towards a global oil palm sample database: design and implications

Cited by 17 publications

References 23 publications

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks

Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network

Contact Info

Product

Resources

About