scholarly journals Assessing the Importance of Tree Cover Threshold for Forest Cover Mapping Derived from Global Forest Cover in Myanmar

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1062 ◽  
Author(s):  
Kay Khaing Lwin ◽  
Tetsuji Ota ◽  
Katsuto Shimizu ◽  
Nobuya Mizoue
Keyword(s):  
Large Scale ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Tree Cover ◽  
Tropical Rainforests ◽  
Forest Change ◽  
Tropical Regions ◽  
Ecological Zones ◽  
Optimal Tree ◽  
Forest Cover Mapping

Comprehensive forest cover mapping is essential for making policy and management decisions. However, creating a forest cover map from raw remote sensing data is a barrier for many users. Here, we investigated the effects of different tree cover thresholds on the accuracy of forest cover maps derived from the Global Forest Change Dataset (GFCD) across different ecological zones in a country-scale evaluation of Myanmar. To understand the effect of different thresholds on map accuracy, nine forest cover maps having thresholds ranging from 10% to 90% were created from the GFCD. The accuracy of the forest cover maps within each ecological zone and at the national scale was assessed. The overall accuracies of ecological zones other than tropical rainforest were highest when the threshold for tree cover was less than 50%. The appropriate threshold for tropical rainforests was 80%. Therefore, different optimal tree cover thresholds were required to achieve the highest overall accuracy depending on ecological zones. However, in the unique case of Myanmar, we were able to determine the threshold across the whole country. We concluded that the threshold for tree cover for creating a forest cover map should be determined according to the areal ratio of ecological zones determined from large-scale monitoring. Our results are applicable to tropical regions having similar ecological zones.

scholarly journals Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

2020 ◽  
Vol 12 (19) ◽  
pp. 3226
Author(s):  
Daniel Cunningham ◽  
Paul Cunningham ◽  
Matthew E. Fagan
Keyword(s):  
Costa Rica ◽  
Forest Cover ◽  
Agricultural Land ◽  
Forest Area ◽  
Tree Cover ◽  
Agricultural Crop ◽  
Forest Change ◽  
Alos Palsar ◽  
Tropical Regions ◽  
The Impact

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

Global analysis of mountain forest distribution and change during 2000 to 2018

2020 ◽  
Author(s):  
Xinyue He ◽  
Dominick Spracklen ◽  
Joseph Holden ◽  
Zhenzhong Zeng
Keyword(s):  
Forest Cover ◽  
River Flow ◽  
Global Analysis ◽  
Tree Cover ◽  
Mountain Forest ◽  
Mountain Forests ◽  
Forest Change ◽  
Elevation Data ◽  
Forest Distribution ◽  
Hydrological Cycles

<p>Mountain forests cover a small fraction of the Earth’s surface, but may exert important influence on the hydrological cycles of river basins (e.g., evapotranspiration, river flow). Many montane ecosystems are currently experiencing forest loss or gain, due to direct land-use change and due to changes in climate. Previous studies revealed most deforestation and afforestation occur in the lowlands, while how forest cover changes at different altitudes in the mountains has not been fully understood. Here we present a study that aims to better understand the distribution of mountain forest change. We use a high-resolution global map of forest change during 2000-2018 combined with elevation data to complete a global analysis of the relationship of elevation with tree cover and tree cover loss and gain. We also assess which climate variables (temperature, rainfall, wind speed) might explain observed variations in tree cover. Our analysis provides new information on how and why mountain forests are changing.</p>

scholarly journals STUDY OF THE STATE OF FORESTS OF THE ROSTOV REGION WITH THE USE OF MODERN GEOINFORMATION TECHNOLOGIES

2021 ◽  
Vol 2 (6) ◽  
pp. 100-109
Author(s):  
M.M. Streltsova ◽  
◽  
, O.E. Arkhipova ◽  
Keyword(s):  
Forest Fires ◽  
Forest Cover ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Steppe Zone ◽  
The State ◽  
Forest Change ◽  
Space Images ◽  
Rostov Region ◽  
Geoinformation Technologies

The work is devoted to the study of the forests of the Rostov region, the determination of the spatio-temporal dynamics of the area of the territory covered with forest, using remote sensing data and geoinformation systems. The relevance of the study is due to the active anthropogenic impact on forests in the steppe zone, in a region with a forest deficit cover. The purpose of the study is to study the state of forests based on the use of modern geoinformation technologies, to assess the dynamics of forest cover in the forest fund of the Rostov region. The object of research is one of the most wooded areas of the Rostov region – the Verkhnedonsky. To study the state of the forests of the Rostov region, satellite images obtained using the Sentinel-2 spacecraft and data from the Global Forest Change application were used. Earth Engine. The efficiency of application of various methods of classification of space images has been investigated. It was revealed that despite the forest fires that affect the forests of the region due to climatic and natural factors, the area of gum massifs since 2015, in accordance with the classification carried out, has increased by about 300 hectares.

scholarly journals CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 855-862 ◽  
Author(s):  
Y. T. Guo ◽  
X. M. Zhang ◽  
T. F. Long ◽  
W. L. Jiao ◽  
G. J. He ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Forest Cover ◽  
Survey Methods ◽  
Coniferous Forest ◽  
Google Earth ◽  
Forest Area ◽  
Landsat 8 ◽  
Forest Change ◽  
Google Earth Engine

Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

Progressive Midlatitude Afforestation: Impacts on Clouds, Global Energy Transport, and Precipitation

2016 ◽  
Vol 29 (15) ◽  
pp. 5561-5573 ◽  
Author(s):  
Marysa M. Laguë ◽  
Abigail L. S. Swann
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Large Scale ◽  
Forest Cover ◽  
Energy Transport ◽  
Climate Impacts ◽  
Heat Fluxes ◽  
Tree Cover ◽  
Climate Response ◽  
Cloud Feedbacks

Abstract Vegetation influences the atmosphere in complex and nonlinear ways, such that large-scale changes in vegetation cover can drive changes in climate on both local and global scales. Large-scale land surface changes have been shown to introduce excess energy to one hemisphere, causing a shift in atmospheric circulation on a global scale. However, past work has not quantified how the climate response scales with the area of vegetation. Here, the response of climate to linearly increasing the area of forest cover in the northern midlatitudes is systematically evaluated. This study shows that the magnitude of afforestation of the northern midlatitudes determines the local climate response in a nonlinear fashion, and the authors identify a threshold in vegetation-induced cloud feedbacks—a concept not previously addressed by large-scale vegetation manipulation experiments. Small increases in tree cover drive compensating cloud feedbacks, while latent heat fluxes reach a threshold after sufficiently large increases in tree cover, causing the troposphere to warm and dry, subsequently reducing cloud cover. Increased absorption of solar radiation at the surface is driven by both surface albedo changes and cloud feedbacks. This study shows how atmospheric cross-equatorial energy transport changes as the area of afforestation is incrementally increased. The results highlight the importance of considering both local and remote climate effects of large-scale vegetation change and explore the scaling relationship between changes in vegetation cover and resulting climate impacts.

scholarly journals EVALUATION OF ANNUAL MODIS PTC DATA FOR DEFORESTATION AND FOREST DEGRADATION ANALYSIS

2016 ◽  
Vol XLI-B2 ◽  
pp. 9-13
Author(s):  
Y. Gao ◽  
A. Ghilardi ◽  
J. F. Mas ◽  
J. Paneque-Galvez ◽  
M. Skutsch
Keyword(s):  
Case Studies ◽  
Forest Cover ◽  
Forest Degradation ◽  
Tree Cover ◽  
Visual Interpretation ◽  
Forest Change ◽  
Validation Data ◽  
Degradation Analysis ◽  
Spot 5 ◽  
Quality Layer

Anthropogenic land-cover change, e.g. deforestation and forest degradation cause carbon emissions. To estimate deforestation and forest degradation, it is important to have reliable data on forest cover. In this analysis, we evaluated annual MODIS Percent Tree Cover (PTC) data for the detection of forest change including deforestation, forest degradation, reforestation and revegetation. The annual MODIS PTC data (2000 – 2010) were pre-processed by applying quality layer. Based on the PTC values of the annual MODIS data, forest change maps were produced and assessed by comparing with the data from visual interpretation of SPOT-5 images. The assessment was applied to two case-studies: Ayuquila Basin and Monarch Reserve. Results show that the detected deforestation patches by visual interpretation are roughly 4 times in quantity more than those by MODIS PTC data, which can be partially due to the much higher spatial resolution of SPOT-5, being able to pick up small deforestation patches. This analysis found poor spatial overlapping for both case-studies. Possible reasons for the discrepancy in quantity and spatial coincidence were provided. It is necessary to refine the methodology for forest change detection by PTC images; also to refine the validation data in terms of data periods and forest change categories to ensure a better assessment.

scholarly journals Forest transitions: a new conceptual scheme

2017 ◽  
Vol 72 (4) ◽  
pp. 465-474 ◽  
Author(s):  
Christian A. Kull
Keyword(s):  
Forest Cover ◽  
Forest Recovery ◽  
Tree Cover ◽  
Native Forest ◽  
Heuristic Model ◽  
Forest Change ◽  
Ecological Processes ◽  
Political Processes ◽  
Political Effect ◽  
Forest Transitions

Abstract. Forest transitions have recently received much attention, particularly in the hope that the historical transitions from net deforestation to forest recovery documented in several temperate countries might be reproduced in tropical countries. The analysis of forest transitions, however, has struggled with questions of forest definition and has at times focussed purely on tree cover, irrespective of tree types (e.g. native forest or exotic plantations). Furthermore, it has paid little attention to how categories and definitions of forest are used to political effect or shape how forest change is viewed. In this paper, I propose a new heuristic model to address these lacunae, building on a conception of forests as distinct socio-ecological relationships between people, trees, and other actors that maintain and threaten the forest. The model draws on selected work in the forest transition, land change science, and critical social science literatures. It explicitly forces analysts to see forests as much more than a land cover statistic, particularly as it internalizes consideration of forest characteristics and the differential ways in which forests are produced and thought about. The new heuristic model distinguishes between four component forest transitions: transitions in quantitative forest cover (FT1); in characteristics like species composition or density (FT2); in the ecological, socio-economic, and political processes and relationships that constitute particular forests (FT3); and in forest ideologies, discourses, and stories (FT4). The four are interlinked; the third category emerges as the linchpin. An analysis of forest transformations requires attention to diverse social and ecological processes, to power-laden official categories and classifications, and to the discourses and tropes by which people interpret these changes. Diverse examples are used to illustrate the model components and highlight the utility of considering the four categories of forest transitions.

scholarly journals Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia

2015 ◽  
Vol XL-7/W3 ◽  
pp. 557-562 ◽  
Author(s):  
A. Wijaya ◽  
R. A. Sugardiman Budiharto ◽  
A. Tosiani ◽  
D. Murdiyarso ◽  
L.V. Verchot
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Spatial Information ◽  
Brazilian Amazon ◽  
Congo Basin ◽  
Reliable Estimate ◽  
Forest Conversion ◽  
Forest Cover Change ◽  
Forest Change

Indonesia possesses the third largest tropical forests coverage following Brazilian Amazon and Congo Basin regions. This country, however, suffered from the highest deforestation rate surpassing deforestation in the Brazilian Amazon in 2012. National capacity for forest change assessment and monitoring has been well-established in Indonesia and the availability of national forest inventory data could largely assist the country to report their forest carbon stocks and change over more than two decades. This work focuses for refining forest cover change mapping and deforestation estimate at national scale applying over 10,000 scenes of Landsat scenes, acquired in 1990, 1996, 2000, 2003, 2006, 2009, 2011 and 2012. Pre-processing of the data includes, geometric corrections and image mosaicking. The classification of mosaic Landsat data used multi-stage visual observation approaches, verified using ground observations and comparison with other published materials. There are 23 land cover classes identified from land cover data, presenting spatial information of forests, agriculture, plantations, non-vegetated lands and other land use categories. We estimated the magnitude of forest cover change and assessed drivers of forest cover change over time. Forest change trajectories analysis was also conducted to observe dynamics of forest cover across time. This study found that careful interpretations of satellite data can provide reliable information on forest cover and change. Deforestation trend in Indonesia was lower in 2000-2012 compared to 1990-2000 periods. We also found that over 50% of forests loss in 1990 remains unproductive in 2012. Major drivers of forest conversion in Indonesia range from shrubs/open land, subsistence agriculture, oil palm expansion, plantation forest and mining. The results were compared with other available datasets and we obtained that the MOF data yields reliable estimate of deforestation.

scholarly journals FOREST COVER CHANGE ANALYSIS IN INNER MONGOLIA USING REMOTE SENSING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1951-1954
Author(s):  
S. Xie ◽  
J. Gong ◽  
X. Huang
Keyword(s):  
Protected Area ◽  
Inner Mongolia ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Shape Index ◽  
Forest Area ◽  
Forest Cover Change ◽  
Forest Change ◽  
Change Analysis ◽  
Forest Patches

Forest is the lung of the earth, and it has important effect on maintaining the ecological balance of the whole earth. This study was conducted in Inner Mongolia during the year 1990–2015. Land use and land cover data were used to obtain forest cover change of Inner Mongolia. In addition, protected area data, road data, ASTER GDEM data were combined with forest cover change data to analyze the relationship between them. Moreover, patch density and landscape shape index were calculated to analyze forest change in perspective of landscape aspect. The results indicated that forest area increased overall during the study period. However, a few cities still had a phenomenon of reduced forest area. Results also demonstrated that the construction of protected area had positive effect on protecting forest while roads may disturbed forest due to human activities. In addition, forest patches in most of cities of Inner Mongolia tended to be larger and less fragmented. This paper reflected forest change in Inner Mongolia objectively, which is helpful for policy making by government.

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