The Road to Operationalization of Effective Tropical Forest Monitoring Systems

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring.

Download Full-text

Developing community-based forest monitoring systems through action research

APN Science Bulletin ◽

10.30852/sb.2015.16 ◽

2015 ◽

Vol 5 (1) ◽

pp. 16-19

Author(s):

Henry Scheyvens ◽

Makino Yamanoshita ◽

Taiji Fujisaki ◽

Agus Setyarso ◽

Saykham Boutthavong ◽

...

Keyword(s):

Action Research ◽

Forest Monitoring ◽

Monitoring Systems ◽

Community Based

Download Full-text

Patterns and Drivers of Deforestation and Forest Degradation in Myanmar

Sustainability ◽

10.3390/su13147539 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7539

Author(s):

Zaw Naing Tun ◽

Paul Dargusch ◽

DJ McMoran ◽

Clive McAlpine ◽

Genia Hill

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Local Governments ◽

Forest Canopy ◽

Forest Cover ◽

Forest Degradation ◽

Forest Monitoring ◽

Map Algebra ◽

Change Detection Analysis ◽

Detection Analysis

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

Download Full-text

Disentangling the functional trait correlates of spatial aggregation in tropical forest trees

Ecology ◽

10.1002/ecy.2591 ◽

2019 ◽

Vol 100 (3) ◽

Cited By ~ 5

Author(s):

Ian R. McFadden ◽

Megan K. Bartlett ◽

Thorsten Wiegand ◽

Benjamin L. Turner ◽

Lawren Sack ◽

...

Keyword(s):

Tropical Forest ◽

Functional Trait ◽

Spatial Aggregation ◽

Forest Trees

Download Full-text

Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

Annals of Bangladesh Agriculture ◽

10.3329/aba.v24i2.55781 ◽

2021 ◽

Vol 24 (2) ◽

pp. 15-32

Author(s):

KMM Uzzaman ◽

MG Miah ◽

HM Abdullah ◽

MR Islam ◽

MSI Afrad ◽

...

Keyword(s):

Mangrove Forest ◽

Forest Cover ◽

Water Bodies ◽

Forest Monitoring ◽

Continuous Change ◽

Forest Cover Change ◽

Subsequent Increase ◽

Stable Pattern ◽

Sundarban Mangrove ◽

Conservation And Management

Accurate and realistic forest cover change assessment is essential for the conservation and management of the Sundarban mangrove forest of Bangladesh. With these views, an integrated way of the vegetation cover assessment was conducted using time-series Landsat satellite imageries of 1991, 2001, 2011, and 2021. During the last 30-year (1991-2021), variations in four land cover classes viz. healthy vegetation, unhealthy vegetation, water body, and sandbar were recorded. It showed a decreasing trend of forest vegetation and a subsequent increase of water bodies during the study period. The healthy vegetation and unhealthy vegetation decreased at 1.33 and 1.66%, respectively, whereas water bodies increased 2.55% at the same time. The healthy vegetation consistently decreased over the decades, but unhealthy vegetation decreased during the 2001-2011 period. Conversion from healthy vegetation to unhealthy vegetation and unhealthy vegetation to healthy vegetation during 1991-2001 was similar. Such transform was much higher from unhealthy to healthy vegetation during 2001-2011. Transformation of healthy vegetation to unhealthy vegetation was remarkably higher during the 2011-2021 period. Further continuous change detection and classification algorithm (CCDC) showed a stable pattern over the study period without significant breakpoints. This study reveals the need for regular mangrove forest monitoring. The findings of this study can be used as a reference in the formulation and implementation of sustainable mangrove forest conservation and management. Ann. Bangladesh Agric. (2020) 24(2): 15-32

Download Full-text

Integrating forest and landscape restoration into national forest monitoring systems

10.4060/cb6021en ◽

2021 ◽

Keyword(s):

Forest Monitoring ◽

Monitoring Systems ◽

National Forest ◽

Landscape Restoration

Download Full-text

Monitoring of Tropical Forest Cover with Remote Sensing

Tropical Forestry Handbook ◽

10.1007/978-3-642-41554-8_145-1 ◽

2015 ◽

pp. 1-19

Author(s):

Abner Josue Jimenez Galo

Keyword(s):

Remote Sensing ◽

Tropical Forest ◽

Forest Cover

Download Full-text

Study on Global Burned Forest Areas Based on Landsat Data

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.86.8.503 ◽

2020 ◽

Vol 86 (8) ◽

pp. 503-508

Author(s):

Zhaoming Zhang ◽

Tengfei Long ◽

Guojin He ◽

Mingyue Wei ◽

Chao Tang ◽

...

Keyword(s):

Spatial Resolution ◽

Forest Cover ◽

Time Series Data ◽

Global Climate ◽

Distribution Patterns ◽

Forest Degradation ◽

Forest Monitoring ◽

Series Data ◽

Burned Area ◽

Burned Forest

Forests are an extremely valuable natural resource for human development. Satellite remote sensing technology has been widely used in global and regional forest monitoring and management. Accurate data on forest degradation and disturbances due to forest fire is important to understand forest ecosystem health and forest cover conditions. For a long time, satellite-based global burned area products were only available at coarse native spatial resolution, which was difficult for detecting small and highly fragmented fires. In order to analyze global burned forest areas at finer spatial resolution, in this study a novel, multi-year 30 meter resolution global burned forest area product was generated and released based on Landsat time series data. Statistics indicate that in 2000, 2005, 2010, 2015, and 2018 the total area of burned forest land in the world was 94.14 million hm2, 96.65 million hm2, 59.52 million hm2, 76.42 million hm2, and 83.70 million hm2, respectively, with an average value of 82.09 million hm2. Spatial distribution patterns of global burned forest areas were investigated across different continents and climatic domains. It was found that burned forest areas were mainly distributed in Africa and Oceania, which accounted for approximately 73.85% and 6.81% of the globe, respectively. By climatic domain, the largest burned forest areas occurred in the tropics, with proportions between 88.44% and 95.05% of the world's total during the study period. Multi-year dynamic analysis shows the global burned forest areas varied considerably due to global climate anomalies, e.g., the La Niña phenomenon.

Download Full-text

Tropical forest cover change in the 1990s and options for future monitoring

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2004.1590 ◽

2005 ◽

Vol 360 (1454) ◽

pp. 373-384 ◽

Cited By ~ 234

Author(s):

Philippe Mayaux ◽

Peter Holmgren ◽

Frédéric Achard ◽

Hugh Eva ◽

Hans-Jürgen Stibig ◽

...

Keyword(s):

Latin America ◽

Tropical Forest ◽

Forest Cover ◽

National Level ◽

Forest Resources ◽

Environmental Agreements ◽

Detailed Knowledge ◽

Multilateral Environmental Agreements ◽

Humid Forest ◽

Resources Assessment

Despite the importance of the world's humid tropical forests, our knowledge concerning their rates of change remains limited. Two recent programmes (FAO 2000 Forest Resources Assessment and TREES II), exploiting the global imaging capabilities of Earth observing satellites, have recently been completed to provide information on the dynamics of tropical forest cover. The results from these independent studies show a high degree of conformity and provide a good understanding of trends at the pan-tropical level. In 1990 there were some 1150 million ha of tropical rain forest with the area of the humid tropics deforested annually estimated at 5.8 million ha (approximately twice the size of Belgium). A further 2.3 million ha of humid forest is apparently degraded annually through fragmentation, logging and/or fires. In the sub-humid and dry tropics, annual deforestation of tropical moist deciduous and tropical dry forests comes to 2.2 and 0.7 million ha, respectively. Southeast Asia is the region where forests are under the highest pressure with an annual change rate of −0.8 to −0.9%. The annual area deforested in Latin America is large, but the relative rate (−0.4 to −0.5%) is lower, owing to the vast area covered by the remaining Amazonian forests. The humid forests of Africa are being converted at a similar rate to those of Latin America (−0.4 to −0.5% per year). During this period, secondary forests have also been established, through re-growth on abandoned land and forest plantations, but with different ecological, biophysical and economic characteristics compared with primary forests. These trends are significant in all regions, but the extent of new forest cover has proven difficult to establish. These results, as well as the lack of more detailed knowledge, clearly demonstrate the need to improve sound scientific evidence to support policy. The two projects provide useful guidance for future monitoring efforts in the context of multilateral environmental agreements and of international aid, trade and development partnerships. Methodologically, the use of high-resolution remote sensing in representative samples has been shown to be cost-effective. Close collaboration between tropical institutions and inter-governmental organizations proved to be a fruitful arrangement in the different projects. To properly assist decision-making, monitoring and assessments should primarily be addressed at the national level, which also corresponds to the ratification level of the multilateral environmental agreements. The Forest Resources Assessment 2000 deforestation statistics from countries are consistent with the satellite-based estimates in Asia and America, but are significantly different in Africa, highlighting the particular need for long-term capacity-building activities in this continent.

Download Full-text

Forest Cover Types Classification Based on Online Machine Learning on Distributed Cloud Computing Platforms of Storm and SAMOA

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.3803 ◽

2014 ◽

Vol 955-959 ◽

pp. 3803-3812

Author(s):

Guang Di Li ◽

Guo Yin Wang ◽

Xue Rui Zhang ◽

Wei Hui Deng ◽

Fan Zhang

Keyword(s):

Machine Learning ◽

Forest Cover ◽

Stream Processing ◽

Processing Technique ◽

Machine Learning Algorithms ◽

Learning Tasks ◽

Hoeffding Tree ◽

Computing Platforms ◽

Processing Platform ◽

Forest Cover Types

Storm is the most popular realtime stream processing platform, which can be used to deal with online machine learning. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation. SAMOA includes distributed algorithms for the most common machine learning tasks like Mahout for Hadoop. SAMOA is both a platform and a library. In this paper, Forest cover types, a large benchmaking dataset available at the UCI KDD Archive is used as the data stream source. Vertical Hoeffding Tree, a parallelizing streaming decision tree induction for distributed enviroment, which is incorporated in SAMOA API is applied on Storm platform. This study compared stream prcessing technique for predicting forest cover types from cartographic variables with traditional classic machine learning algorithms applied on this dataset. The test then train method used in this system is totally different from the traditional train then test. The results of the stream processing technique indicated that it’s output is aymptotically nearly identical to that of a conventional learner, but the model derived from this system is totally scalable, real-time, capable of dealing with evolving streams and insensitive to stream ordering.

Download Full-text