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

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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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

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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Interdependent effects of climate variability and forest cover change on streamflow dynamics: a case study in the Upper Umvoti River Basin, South Africa

Regional Environmental Change ◽

10.1007/s10113-019-01521-8 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1963-1971

Author(s):

Karen Lebek ◽

Cornelius Senf ◽

David Frantz ◽

José A. F. Monteiro ◽

Tobias Krueger

Keyword(s):

South Africa ◽

Climate Variability ◽

River Basin ◽

Forest Cover ◽

Forest Cover Change

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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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