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

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.

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

2021 ◽  
Vol 43 (3) ◽  
Author(s):  
Duong Nguyen Dinh ◽  
Cam Lai Vinh
Keyword(s):  
Forest Cover ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Google Earth ◽  
Natural Forest ◽  
Infrastructure Development ◽  
Natural Forests ◽  
Forest Cover Change ◽  
Remotely Sensed Data ◽  
Automated Method

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.

scholarly journals The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments

2013 ◽  
Vol 17 (7) ◽  
pp. 2613-2635 ◽  
Author(s):  
H. E. Beck ◽  
L. A. Bruijnzeel ◽  
A. I. J. M. van Dijk ◽  
T. R. McVicar ◽  
F. N. Scatena ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Regeneration ◽  
Forest Cover ◽  
Impact Analysis ◽  
Forest Area ◽  
Macro Scale ◽  
Annual Time Series ◽  
The Impact ◽  
Annual Time

Abstract. Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastures and agricultural lands, followed by forest regeneration. This paper examines the possible impacts of these secondary forests on several Q characteristics for 12 mesoscale catchments (23–346 km2; mean precipitation 1720–3422 mm yr−1) with long (33–51 yr) and simultaneous records for Q, precipitation (P), potential evaporation (PET), and land cover. A simple spatially-lumped, conceptual rainfall–runoff model that uses daily P and PET time series as inputs (HBV-light) was used to simulate Q for each catchment. Annual time series of observed and simulated values of four Q characteristics were calculated. A least-squares trend was fitted through annual time series of the residual difference between observed and simulated time series of each Q characteristic. From this the total cumulative change (Â) was calculated, representing the change in each Q characteristic after controlling for climate variability and water storage carry-over effects between years. Negative values of  were found for most catchments and Q characteristics, suggesting enhanced actual evaporation overall following forest regeneration. However, correlations between changes in urban or forest area and values of  were insignificant (p ≥ 0.389) for all Q characteristics. This suggests there is no convincing evidence that changes in the chosen Q characteristics in these Puerto Rican catchments can be ascribed to changes in urban or forest area. The present results are in line with previous studies of meso- and macro-scale (sub-)tropical catchments, which generally found no significant change in Q that can be attributed to changes in forest cover. Possible explanations for the lack of a clear signal may include errors in the land cover, climate, Q, and/or catchment boundary data; changes in forest area occurring mainly in the less rainy lowlands; and heterogeneity in catchment response. Different results were obtained for different catchments, and using a smaller subset of catchments could have led to very different conclusions. This highlights the importance of including multiple catchments in land-cover impact analysis at the mesoscale.

scholarly journals Uncertainty Analysis of Multisource Land Cover Products in China

2021 ◽  
Vol 13 (16) ◽  
pp. 8857
Author(s):  
Longhao Wang ◽  
Jiaxin Jin
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Regional Scale ◽  
Least Squares Regression ◽  
Average Deviation ◽  
Deviation Analysis ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Forest Coverage ◽  
High Consistency

Satellite-based land cover products play a crucial role in sustainability. There are several types of land cover products, such as qualitative products with discrete classes, semiquantitative products with several classes at a predetermined ratio, and quantitative products with land cover fractions. The proportions of land cover types in the grids with coarse resolution should be considered when used at the regional scale (e.g., modeling and remote sensing inversion). However, uncertainty, which varies with spatial distribution and resolution, needs to be studied further. This study used MCD12, ESA CCI, and MEaSURES VCF land cover data as indicators of qualitative, semiquantitative, and quantitative products, respectively, to explore the uncertainty of multisource land cover data. The methods of maximum area aggregation, deviation analysis, and least squares regression were used to investigate spatiotemporal changes in forests and nontree vegetation at diverse pixel resolutions across China. The results showed that the average difference in forest coverage for the three products was 8%, and the average deviation was 11.2%. For forest cover, the VCF and ESA CCI exhibited high consistency. For nontree vegetation, the ESA CCI and MODIS exhibited the lowest differences. The overall uncertainty in the temporal and spatial changes of the three products was relatively small, but there were significant differences in local areas (e.g., southeastern hills). Notably, as the spatial resolution decreased, the three products’ uncertainty decreased, and the resolution of 0.1° was the inflection point of consistency.

scholarly journals Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin

2021 ◽  
Vol 13 (8) ◽  
pp. 1596
Author(s):  
Bo Zhong ◽  
Aixia Yang ◽  
Kunsheng Jue ◽  
Junjun Wu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Regional Scale ◽  
Heihe River ◽  
Land Cover Mapping ◽  
High Quality ◽  
Long Time Series ◽  
Training Samples ◽  
Long Time ◽  
Land Cover Map

Long time series of land cover changes (LCCs) are critical in the analysis of long-term climate, environmental, and ecological changes. Although several moderate to fine resolution global land cover datasets have been publicly released and they show strong consistency at the global scale, they have large deviations at the regional scale; furthermore, high-quality land cover datasets from before 2000 are not available and the classification consistency among different datasets is not very good. Thus, long time series of land cover datasets with high quality and consistency are in great demand but they are still unavailable, even at the regional scale. The Landsat series of satellite imagery composed of eight successive satellites can be traced back to 1972 and it is, therefore, possible to produce a long time series land cover dataset. In addition, the newly available satellite data have the capability to construct time series satellite images and a time series analysis method such as LCMM can be employed for making high-quality land cover datasets. Therefore, by taking the advantages of the two categories of satellite data, we proposed a new time series land cover mapping method based on machine learning and it, thereafter, is applied to Heihe River Basin (HRB) for verification purposes. Firstly, the high-quality land cover datasets at HRB from 2011–2015, which were retrieved using the LCMM method, are used for quickly and accurately making training samples. Secondly, a strategy for transferring the training samples after 2011 to earlier years is established. Thirdly, the random forest model is employed to train the selected yearly samples and a land cover map for every year is subsequently made. Finally, comprehensive analysis and validation are carried out for evaluation. In this study, a long time series land cover dataset including 1986, 1990, 1995, 2000, 2005, 2010, 2011, 2012, 2013, 2014, and 2015 is finally made and an average precision of about 90% is achieved. It is the longest time series land cover map with 30 m resolution at HRB and the dataset has good time continuity and stability.

scholarly journals Changes in the spatial and temporal pattern of natural forest cover on Hainan Island from the 1950s to the 2010s: implications for natural forest conservation and management

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3320 ◽  
Author(s):  
Siliang Lin ◽  
Yaozhu Jiang ◽  
Jiekun He ◽  
Guangzhi Ma ◽  
Yang Xu ◽  
...  
Keyword(s):  
Forest Cover ◽  
Hainan Island ◽  
Natural Forest ◽  
Natural Forests ◽  
Ecological Value ◽  
Spatial And Temporal Patterns ◽  
Conservation Policies ◽  
The Road ◽  
Conservation Actions ◽  
The 1950S

The study of the past, present, and future state and dynamics of the tropical natural forest cover (NFC) might help to better understand the pattern of deforestation and fragmentation as well as the influence of social and natural processes. The obtained information will support the development of effective conservation policies and strategies. In the present study, we used historical data of the road network, topography, and climatic productivity to reconstruct NFC maps of Hainan Island, China, from the 1950s to the 2010s, using the random forest algorithm. We investigated the spatial and temporal patterns of NFC change from the 1950s to the 2010s and found that it was highly dynamic in both space and time. Our data showed that grid cells with low NFC were more vulnerable to NFC decrease, suggesting that conservation actions regarding natural forests need to focus on regions with low NFC and high ecological value. We also identified the hot spots of NFC change, which provides insights into the dynamic changes of natural forests over time.

scholarly journals Changes in the spatial and temporal pattern of natural forest cover on Hainan Island from the 1950s to the 2010s：implications for natural forest conservation and management

2017 ◽  
Author(s):  
Siliang Lin ◽  
Yaozhu Jiang ◽  
Jiekun He ◽  
Guangzhi Ma ◽  
Yang Xu ◽  
...  
Keyword(s):  
Forest Cover ◽  
Hainan Island ◽  
Natural Forest ◽  
Natural Forests ◽  
Ecological Value ◽  
Spatial And Temporal Patterns ◽  
Conservation Policies ◽  
The Road ◽  
Conservation Actions ◽  
The 1950S

The study of the past, present, and future state and dynamics of the tropical natural forest cover (NFC) might help to better understand the pattern of deforestation and fragmentation as well as the influence of social and natural processes. The obtained information will support the development of effective conservation policies and strategies. In the present study, we used historical data of the road network, topography, and climatic productivity to reconstruct NFC maps of Hainan Island, China, from the 1950s to the 2010s, using the random forest algorithm. We investigated the spatial and temporal patterns of NFC change from the 1950s to the 2010s and found that it was highly dynamic in both space and time. Our data showed that grid cells with low NFC were more vulnerable to NFC decrease, suggesting that conservation actions regarding natural forests need to focus on regions with low NFC and high ecological value. We also identified the hot-spots of NFC change, which provides insights into the dynamic changes of natural forests over time.

Extent and causes of forest cover changes in Vietnam’s provinces 1993–2013: a review and analysis of official data

2017 ◽  
Vol 25 (2) ◽  
pp. 199-217 ◽  
Author(s):  
Roland Cochard ◽  
Dung Tri Ngo ◽  
Patrick O. Waeber ◽  
Christian A. Kull
Keyword(s):  
Forest Regeneration ◽  
Forest Cover ◽  
Natural Forest ◽  
Tree Plantations ◽  
Environmental Services ◽  
Forest Transition ◽  
Natural Forests ◽  
Forest Regrowth ◽  
Forest Cover Changes ◽  
Natural Forest Regeneration

Within a region plagued by deforestation, Vietnam has experienced an exceptional turnaround from net forest loss to forest regrowth. This so-called forest transition, starting in the 1990s, resulted from major changes to environmental and economic policy. Investments in agricultural intensification, reforestation programs, and forestland privatization directly or indirectly promoted natural forest regeneration and the setting-up of plantation forests mainly stocked with exotic species. Forest cover changes, however, varied widely among regions due to specific socio-economic and environmental factors. We studied forest cover changes (including natural and planted forests) and associated drivers in Vietnam’s provinces from 1993–2013. An exhaustive literature review was combined with multivariate statistical analyses of official provincial data. Natural forest regrowth was highest in northern mountain provinces, especially in the period 1993–2003, whereas deforestation continued in the Central Highlands and Southeast Region. Forest plantations increased most in mid-elevation provinces. Statistical results largely confirmed case study-based literature, highlighting the importance of forestland allocation policies and agroforestry extension for promoting small-scale tree plantations and allowing natural forest regeneration in previously degraded areas. Results provide evidence for the abandonment of upland swidden agriculture during 1993–2003, and reveal that spatial competition between expanding natural forests, fixed crop fields, and tree plantations increased during 2003–2013. While we identified a literature gap regarding effects of forest management by para-statal forestry organizations, statistical results showed that natural forests increased in areas managed for protection/regeneration. Cover of other natural forests under the organizations’ management, however, tended to decrease or stagnate, especially more recently when the organizations increasingly turned to multi-purpose plantation forestry. Deforestation processes in the Central Highlands and Southeast Region were mainly driven by cash crop expansion (coffee, rubber) and associated immigration and population growth. Recent data trends indicated limits to further forest expansion, and logging within high-quality natural forests reportedly remained a widespread problem. New schemes for payments for forest environmental services should be strengthened to consolidate the gains from the forest transition, whilst improving forest quality (in terms of biodiversity and environmental services) and allowing local people to actively participate in forest management.

scholarly journals Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data

2020 ◽  
Vol 12 (2) ◽  
pp. 659
Author(s):  
Jinquan Ai ◽  
Chao Zhang ◽  
Lijuan Chen ◽  
Dajun Li
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Regional Scale ◽  
Series Data ◽  
Yangtze River Estuary ◽  
Classification Framework ◽  
Object Based

A system understanding of the patterns, causes, and trends of long-term land use and land cover (LULC) change at the regional scale is essential for policy makers to address the growing challenges of local sustainability and global climate change. However, it still remains a challenge for estuarine and coastal regions due to the lack of appropriate approaches to consistently generate accurate and long-term LULC maps. In this work, an object-based classification framework was designed to mapping annual LULC changes in the Yangtze River estuary region from 1985–2016 using Landsat time series data. Characteristics of the inter-annual changes of LULC was then analyzed. The results showed that the object-based classification framework could accurately produce annual time series of LULC maps with overall accuracies over 86% for all single-year classifications. Results also indicated that the annual LULC maps enabled the clear depiction of the long-term variability of LULC and could be used to monitor the gradual changes that would not be observed using bi-temporal or sparse time series maps. Specifically, the impervious area rapidly increased from 6.42% to 22.55% of the total land area from 1985 to 2016, whereas the cropland area dramatically decreased from 80.61% to 55.44%. In contrast to the area of forest and grassland, which almost tripled, the area of inland water remained consistent from 1985 to 2008 and slightly increased from 2008 to 2016. However, the area of coastal marshes and barren tidal flats varied with large fluctuations.

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