30-year changes of natural forests under human activities in the Indochina peninsula - case studies in Cambodia, Laos and Vietnam

Natural forests are a basic component of the earth's ecology. It is essential for biodiversity, hydrological cycle regulation, and environmental protection. Natural forests are gradually degraded and reduced due to timber logging, conversion to cropland, production forests, commodity trees, and infrastructure development. Decreasing natural forests results in loss of valuable habitats, land degradation, soil erosion, and imbalance of water cycle on the regional scale. Thus, operational monitoring of natural forest cover change has been in the interest of scientists for a long time. Current forest mapping methods using remotely sensed data provide limited capability to separate natural forests and planted forests. Natural forest statistics are often generated using official forestry national reports that have different bias levels due to different methodologies applied in different countries in forest inventory. Over the last couple of decades, natural forests have been over-exploited for various reasons. This led to forest cover degradation and water regulation capability, which results in extreme floods and drought of a watershed in general. This situation demands an urgent need to develop a fast, reliable, and automated method for mapping natural forests. In this study, by applying a new method for mapping natural forests by Landsat time series, the authors succeeded in mapping changes of natural forests of Cambodia, Laos, and Vietnam from 1989 to 2018. As a focused study area, three provinces: Ratanakiri of Cambodia, Attapeu of Laos, and Kon Tum of Vietnam were selected. The study reveals that after 30 years, 51.3% of natural forests in Ratanakiri, 27.8% of natural forests in Attapeu, and 50% of natural forests in Kon Tum were lost. Classification results were validated using high spatial resolution imagery of Google Earth. The overall accuracy of 99.3% for the year 2018 was achieved.

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AUTOMATED CLASSIFICATION OF NATURAL FORESTS WITH LANDSAT TIME SERIES USING SIMPLIFIED SPECTRAL PATTERNS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-983-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 983-988

Author(s):

N. D. Duong

Keyword(s):

Time Series ◽

Land Cover ◽

Forest Cover ◽

Hydrological Cycle ◽

Regional Scale ◽

Natural Forest ◽

New Method ◽

Natural Forests ◽

Automated Method ◽

Spectral Patterns

Abstract. Natural forests are a basic component of the earth ecology. It is essential for biodiversity, hydrological cycle regulation and environmental protection. Globally, natural forests are gradually degraded and reduced due to timber logging, conversion to cropland, production forest, commodity trees, and infrastructure development. Decreasing of natural forests results in loss of valuable habitats, land degradation, soil erosion and imbalance of water cycle in regional scale. Thus operational monitoring natural forest cover change, therefore, has been in interest of scientists for long time. Forest cover mapping methods are divided to two groups: field-based survey and remotely sensed image data based techniques. The field-based methods are conventional and they have been used widely in forestry management practice. Satellite-image-based methods were developed since beginning of earth observation. These methods, except visual image interpretation, can be grouped to supervised and unsupervised classification that rely on various algorithm as statistical, clustering or artificial intelligence. However, there is little report about method, which can extract natural forests from generic forest cover. Over the last couple of decades, natural forests have been over-exploited by various reasons. This practice led to urgent need of development of fast, reliable and automated method for mapping natural forests. In this study, a new method for mapping of natural forest by Landsat time series is presented. The new method is fully automated. It uses spectral patterns as principal classifier to recognize land cover classes. The proposed method was applied in study area consisted of Ratanakiri of Cambodia, Attapeu of Laos and Kon Tum of Vietnam. About 2000 Landsat images were used to generate land cover maps of the study area across years from 1989 to 2018.

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A New Multi-Temporal Forest Cover Classification for the Xingu River Basin, Brazil

Data ◽

10.3390/data4030114 ◽

2019 ◽

Vol 4 (3) ◽

pp. 114 ◽

Cited By ~ 2

Author(s):

Margaret Kalacska ◽

Oliver Lucanus ◽

Leandro Sousa ◽

J. Pablo Arroyo-Mora

Keyword(s):

River Basin ◽

Forest Cover ◽

Regional Scale ◽

Google Earth ◽

Classification And Regression Tree ◽

Forest Cover Change ◽

Anthropogenic Pressures ◽

Mapping Unit ◽

Xingu River ◽

Multi Temporal

We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil. This region is well known for its exceptionally high biodiversity, especially in terms of the ichthyofauna, with approximately 600 known species, 10% of which are endemic to the river basin. Global and regional scale datasets do not adequately capture the rapidly changing land cover in this region. Accurate forest cover and forest cover change data are important for understanding the anthropogenic pressures on the aquatic ecosystems. We developed the new classifications with a minimum mapping unit of 0.8 ha from cloud free mosaics of Landsat TM5 and OLI 8 imagery in Google Earth Engine using a classification and regression tree (CART) aided by field photographs for the selection of training and validation points.

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Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽

10.3390/land10020173 ◽

2021 ◽

Vol 10 (2) ◽

pp. 173

Author(s):

Changjun Gu ◽

Yili Zhang ◽

Linshan Liu ◽

Lanhui Li ◽

Shicheng Li ◽

...

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

Ecosystem Service ◽

Forest Cover ◽

Google Earth ◽

Forest Cover Change ◽

Sacred Landscape ◽

Lulc Changes ◽

Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

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Mapping Regional Landscape by Using OpenstreetMap (OSM)

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch033 ◽

2019 ◽

pp. 771-790 ◽

Cited By ~ 1

Author(s):

Di Yang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Cover ◽

Regional Scale ◽

Google Earth ◽

Computing Power ◽

New Approach ◽

Computation Cost ◽

Regional Landscape ◽

Google Earth Engine

A forest patterns map over a large extent at high spatial resolution is a heavily computation task but is critical to most regions. There are two major difficulties in generating the classification maps at regional scale: large training points sets and expensive computation cost in classifier modelling. As one of the most well-known Volunteered Geographic Information (VGI) initiatives, OpenstreetMap contributes not only on road network distributions, but the potential of justify land cover and land use. Google Earth Engine is a platform designed for cloud-based mapping with a strong computing power. In this study, we proposed a new approach to generating forest cover map and quantifying road-caused forest fragmentations by using OpenstreetMap in conjunction with remote sensing dataset stored in Google Earth Engine. Additionally, the landscape metrics produced after incorporating OpenStreetMap (OSM) with the forest spatial pattern layers from our output indicated significant levels of forest fragmentation in Yucatan peninsula.

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Mapping Regional Landscape by Using OpenstreetMap (OSM)

Advances in Geospatial Technologies - Volunteered Geographic Information and the Future of Geospatial Data ◽

10.4018/978-1-5225-2446-5.ch008 ◽

2017 ◽

pp. 138-157

Author(s):

Di Yang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Cover ◽

Regional Scale ◽

Google Earth ◽

Computing Power ◽

New Approach ◽

Computation Cost ◽

Regional Landscape ◽

Google Earth Engine

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Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data

Sustainability ◽

10.3390/su11246916 ◽

2019 ◽

Vol 11 (24) ◽

pp. 6916 ◽

Cited By ~ 7

Author(s):

Lubanzi Z. D. Dlamini ◽

Sifiso Xulu

Keyword(s):

Negative Impact ◽

Google Earth ◽

Remotely Sensed Data ◽

Mining Areas ◽

Surface Mines ◽

Automated Method ◽

Observation Strategy ◽

Petroleum Resources ◽

The Status ◽

Disturbance And Recovery

Considering the negative impact of mining on ecosystems in mining areas, the South African government legislated the Mineral and Petroleum Resources Development Act (No. 28 of 2002), to compel mining companies to restore the land affected by mining. Several studies have used remotely sensed data to observe the status and dynamics of surface mines. Advances in remote sensing along the cloud-based Google Earth Engine (GEE) now promise an enhanced observation strategy for improved monitoring of mine environments. Despite these advances, land rehabilitation at Richards Bay Minerals (RBM) is mainly restricted to field-based approaches which are unable to reveal seamless patterns of disturbance and restoration. Here, we illustrate the value of the trajectory-based LandTrendr algorithm in conjunction with GEE for mine rehabilitation studies. Our automated method produced disturbance and recovery patterns (1984–2018) over the RBM site. The study revealed that RBM has progressively been mining different portions of the mineral-rich coastal area after which restoration was undertaken. The duration of mining over each site ranged from 2 to 6 years. The LandTrendr outputs correspond with independent reference datasets that were classified with an overall accuracy of 99%; it captures mine-induced disturbance efficiently and offers a practical tool for mine restoration management.

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Value Chain of Charcoal Production and Implications for Forest Degradation: Case Study of Bié Province, Angola

Environments ◽

10.3390/environments5110113 ◽

2018 ◽

Vol 5 (11) ◽

pp. 113 ◽

Cited By ~ 6

Author(s):

Vasco Chiteculo ◽

Bohdan Lojka ◽

Peter Surový ◽

Vladimir Verner ◽

Dimitrios Panagiotidis ◽

...

Keyword(s):

Value Chain ◽

Forest Cover ◽

Forest Degradation ◽

Primary Data ◽

Natural Forests ◽

Forest Cover Change ◽

Charcoal Production ◽

Order Of Magnitude ◽

Alternative Sources ◽

Urban Consumption

Forest degradation and forest loss threaten the survival of many species and reduce the ability of forests to provide vital services. Clearing for agriculture in Angola is an important driver of forest degradation and deforestation. Charcoal production for urban consumption as a driver of forest degradation has had alarming impacts on natural forests, as well as on the social and economic livelihood of the rural population. The charcoal impact on forest cover change is in the same order of magnitude as deforestation caused by agricultural expansion. However, there is a need to monitor the linkage between charcoal production and forest degradation. The aim of this paper is to investigate the sequence of the charcoal value chain as a systematic key to identify policies to reduce forest degradation in the province of Bié. It is a detailed study of the charcoal value chain that does not stop on the production and the consumption side. The primary data of this study came from 330 respondents obtained through different methods (semi-structured questionnaire survey and market observation conducted in June to September 2013–2014). A logistic regression (logit) model in IBM SPSS Statistics 24 (IBM Corp, Armonk, NY, USA) was used to analyze the factors influencing the decision of the households to use charcoal for domestic purposes. The finding indicates that 21 to 27 thousand hectares were degraded due to charcoal production. By describing the chain of charcoal, it was possible to access the driving factors for charcoal production and to obtain the first-time overview flow of charcoal from producers to consumers in Bié province. The demand for charcoal in this province is more likely to remain strong if government policies do not aim to employ alternative sources of domestic energy.

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Forest Cover Change within the Russian European North after the Breakdown of Soviet Union (1990–2005)

International Journal of Forestry Research ◽

10.1155/2012/729614 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 9

Author(s):

Peter Potapov ◽

Svetlana Turubanova ◽

Ilona Zhuravleva ◽

Matthew Hansen ◽

Alexey Yaroshenko ◽

...

Keyword(s):

Soviet Union ◽

Forest Cover ◽

Spatial Information ◽

Regional Scale ◽

Canopy Cover ◽

Tree Canopy ◽

Forest Cover Change ◽

European North ◽

Tree Canopy Cover ◽

Forest Cover Loss

Forest cover dynamics (defined as tree canopy cover change without regard to forest land use) within the Russian European North have been analyzed from 1990 to 2005 using a combination of results from two Landsat-based forest cover monitoring projects: 1990–2000 and 2000–2005. Results of the forest cover dynamics analysis highlighted several trends in forest cover change since the breakdown of the Soviet planned economy. While total logging area decreased from the 1990–2000 to the 2000–2005 interval, logging and other forms of anthropogenically-induced clearing increased within the Central and Western parts of the region. The most populated regions of European Russia featured the highest rates of net forest cover loss. Our results also revealed intensive gross forest cover loss due to forest felling close to the Russian-Finland border. The annual burned forest area almost doubled between the two time intervals. The 2000–2005 gross forest cover gain results suggest that tree encroachment on abandoned agriculture land is a wide-spread process over the region. The analysis demonstrates the value of regional-scale Landsat-based forest cover and change quantification. Our results supplemented official data by providing independently derived spatial information that could be used for assessing on-going trends and serve as a baseline for future forest cover monitoring.

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Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar

10.1101/147827 ◽

2017 ◽

Author(s):

Ghislain Vieilledent ◽

Clovis Grinand ◽

Fety A. Rakotomalala ◽

Rija Ranaivosoa ◽

Jean-Roger Rakotoarijaona ◽

...

Keyword(s):

Forest Fragmentation ◽

Forest Cover ◽

National Level ◽

Tree Cover ◽

National Forest ◽

Area Network ◽

Natural Forests ◽

Forest Cover Change ◽

Current Conservation ◽

Forest Maps

AbstractThe island of Madagascar has a unique biodiversity, mainly located in the tropical forests of the island. This biodiversity is highly threatened by anthropogenic deforestation. Existing historical forest maps at national level are scattered and have substantial gaps which prevent an exhaustive assessment of long-term deforestation trends in Madagascar. In this study, we combined historical national forest cover maps (covering the period 1953-2000) with a recent global annual tree cover loss dataset (2001-2014) to look at six decades of deforestation and forest fragmentation in Madagascar (from 1953 to 2014). We produced new forest cover maps at 30 m resolution for the year 1990 and annually from 2000 to 2014 over the full territory of Madagascar. We estimated that Madagascar has lost 44% of its natural forest cover over the period 1953-2014 (including 37% over the period 1973-2014). Natural forests cover 8.9 Mha in 2014 (15% of the national territory) and include 4.4 Mha (50%) of moist forests, 2.6 Mha (29%) of dry forests, 1.7 Mha of spiny forests (19%) and 177,000 ha (2%) of mangroves. Since 2005, the annual deforestation rate has progressively increased in Madagascar to reach 99,000 ha/yr during 2010-2014 (corresponding to a rate of 1.1%/yr). Around half of the forest (46%) is now located at less than 100 m from the forest edge. Our approach could be replicated to other developing countries with tropical forest. Accurate forest cover change maps can be used to assess the effectiveness of past and current conservation programs and implement new strategies for the future. In particular, forest maps and estimates can be used in the REDD+ framework which aims at “Reducing Emissions from Deforestation and forest Degradation” and for optimizing the current protected area network.

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