Changes In Forest Area Research Articles

Spatio-Temporal Changes in Forest Area and Its Ecosystem Service Value in Ganzi Prefecture, China, in the Period 1997–2017

It is possible to manage the forest ecosystem and promote sustainable development by keeping track of spatio-temporal fluctuation in the forest area and its ecosystem service value (ESV). The forest ecology of Ganzi Tibetan Autonomous Prefecture (Ganzi Prefecture), which is located in the northern Hengduan Mountains region, i.e., China’s most important ecological functional area, has seen significant alteration during the past 20 years. However, little is known about how the forest and its ESV evolve. We obtained data regarding Ganzi Prefecture’s forests using visual interpretation of remote sensing images derived from 1997, 2007, and 2017, and we evaluated the spatial–temporal changes in the forest ESV from 1997 to 2017 using global value coefficients and adjusted local value coefficients. The results revealed that (1) from 1997 to 2017, the forest area of Ganzi Prefecture increased by 6729.95 km2, and the forest growth rate was 336.50 km2/a, while (2) from 1997 to 2017, the forest ESV in Ganzi Prefecture experienced an overall increase of 257.59 × 108 yuan. The primary driver of the forest ESV increase was the implementation of forestry ecological engineering and protection policies. (3) Finally, the spatial distribution of the forest ESV revealed that the forest ESV density increased during this period, with the most significant increase occurring in Yajiang. The forest ESV was scattered with the highest density in Yajiang and the lowest density in Shiqu. This study emphasizes how crucial forest ecosystems are to Ganzi Prefecture’s mechanisms for maintaining life. It provided a scientific basis for the sustainable management of the forest ecosystem in the Hengduan Mountains.

Spatial and temporal variation of vegetation NPP and analysis of influencing factors in Heilongjiang Province, China

Vegetation net primary productivity (NPP) plays an important role in investigating carbon cycle and NPP provided by forest constitutes the main component of terrestrial vegetation NPP. Since late 1990s, a series of policies for protecting forest have been implemented in Heilongjiang Province, China, directly leading to the continuous change of forest area and growth environment. As a result, analysis of spatiotemporal characteristics of NPP after the implementation of protection policies was conducted in this study, and influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP was also compared quantitatively, aiming to reveal the changing characteristics and influencing factors of NPP in a large area over a longer time span. In this study, multi-source data including MOD17A3 NPP product, meteorological data, topographic data and land-use data was selected, and then the spatiotemporal characteristics of NPP from 2001 to 2020 were analyzed through commonly-used spatiotemporal analysis indices and methods. Finally, influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP in the study area were quantitatively compared and analyzed. Results show that the total amount of NPP in Heilongjiang Province has a fluctuating trend with an increasing rate of 23.47% over the past twenty years, and the increase/decrease scenarios of NPP usually can be found in forest regions with high forest coverage and urban regions experiencing dramatic urbanization processes, respectively. Meanwhile, this study also finds that there is no absolute dominant factor can be responsible for the difference in spatial distribution of NPP, but the conversion between forest and other land-use types are the main factors leading to the spatiotemporal heterogeneity of NPP in the study area. The conclusions in this study may provide guiding significance for formulating forestry protection policies in forest regions and designing land-use plans in the future, aiming to achieve the balance between ecological protection and urban development.

Zmiany powierzchni leśnych na terenie Parku Krajobrazowego Puszcza Zielonka w ostatnich 180 latach w aspekcie krajobrazowym

The article presents the results of research on changes in forest areas in the PuszczaZielonka Landscape Park. An analysis of forest area transformations was carried out with the use of landscape metrics. Archival cartographic material in the form of topographic maps in the scale of 1 : 25 000, coming from four different periods (1830, 1890, 1940 and 1979/80) and a generalized Database of Topographic Object (BDOT) dated 2010, were used for the research. The result of the research are quantitative and qualitative characteristics of changes in the area and the spatial layout of the forest areas and a model of landscape processes taking place in the Puszcza Zielonka Landscape Park. The first analyzed period (1830–1890) was characterized by a small regression of forest area (index of changes WD = ‒0.05), whereas fragmentation and dissection of forest patches prevailed. The second period (1890–1940), despite the slight increase in forest areas (WD = +0.03), was characterized by a decrease in the number of patches and a reduction of their borders’ length. In the third period (1940-1979), the landscape increment process (WD = +0.27), mainly related to the filling of non-forested areas within forest complexes, prevailed. After 1979, the direction of changes was similar but on a much smaller scale (WD = +0.04).

Simulating the Dynamics of the Characteristics of Snow Cover Formation Regime in the Russian Federation Territory. 2. Forest Areas of ER in the Historical Period

A procedure for calculating various characteristics of snow cover formation, based on the use of the land surface model SWAP was tested on forest areas of the European Russia for a historical period (1967−2019). The comparison of simulation results with appropriate observation data showed the good quality of reproduction of snow formation processes at these objects. Changes of the climatic values of snow cover formation characteristics in the historical period were analyzed to reveal tendencies in these changes in forest areas in the region. Thus, it was found that, despite the decrease in the duration of snow cover, there is increase in snowpack, in particular, in the maximal snow water equivalent. The difference between the characteristics of snow cover formation in the field and forest areas of European Russia was assessed. The mean values of the snow accumulation coefficient over the forest area relative to the field was found to be greater than 1.0. At the same time, the climatic changes in the historical period lead to a decrease of this characteristic in time.

Sundarbans Mangrove Mapping and Above Ground Biomass Estimation Using Earth Observation Techniques

The Sundarbans, the world's largest mangrove forest, plays a crucial role in Bangladesh's economy and environment. However, overexploitation, anthropogenic activities like tree cutting for development and agriculture, as well as natural disasters, have caused severe damage and changes to the Sundarbans. This research aims to detect changes in the mangrove forest areas and create an above-ground biomass map of the Sundarbans Forest. Previous studies relied on time-consuming and inaccurate traditional methods, while this study seeks to make a significant contribution to mangrove mapping and forest resource management. Landsat 5 and 8 images from 2007 to 2017 were used to generate the mangrove index (including CMRI and MI) for different years. ALOS-PALSAR 2 and JERS images from 1996, 2007, and 2017, with HV+HH polarization, were processed to calculate the backscatter ratio, enabling identification and estimation of vegetation loss and gain over the years. To estimate the above-ground biomass (AGB), tree height was derived from SRTM DEM, and an allometric equation was used to calculate AGB. The results indicate a continuous shrinkage of the mangrove forest. The 2017 maps of the two different indexes reveal significant mangrove vegetation loss compared to 1996 and 2007. Additionally, the study estimates the total above-ground biomass of the Sundarbans mangrove forest to be 329 million tons. The findings can assist relevant authorities in taking necessary action, formulating policies, and implementing plans for sustainable management of the Sundarbans mangrove forest resources.

The Impact of Increasing Forest Loss Areas on the Global Temperature, and Tourism Industry

This paper aims to investigate the relationship between deforestation, climate change, and the tourism industry, and to explore the potential impacts of decreasing forest areas on these phenomena. The background of the study is that deforestation has become a major environmental problem in many parts of the world, with significant consequences for the global climate and natural ecosystems. Climate change, in turn, has a range of impacts on human societies, including changes in weather patterns, rising sea levels, and altered ecosystems.
 The study found that decreasing forest areas have a significant impact on increasing global temperatures, as forests play a critical role in absorbing and storing carbon dioxide, one of the main greenhouse gases responsible for climate change. As a result, deforestation contributes to the release of carbon dioxide into the atmosphere, which contributes to global warming. The study also highlighted the impact of climate change on the tourism industry, which can alter weather patterns and affect the availability of natural attractions.
 In terms of concrete values, the study provides specific data on the forest area and the annual change in forest area for six countries, including Brazil, Indonesia, the Democratic Republic of Congo, Peru, Thailand, and Costa Rica. The study also provides data on the average annual temperature increase from 1990 to 2019 for these countries, as well as international tourist arrivals in 2019.
 The study further emphasizes the role of sustainable forest management and eco-tourism in mitigating the negative impacts of deforestation on the tourism industry. This includes promoting responsible tourism practices, such as minimizing environmental impacts, supporting local communities, and conserving natural resources. Overall, the study provides valuable insights into the complex relationship between forest conservation, climate change, and the tourism industry, and highlights the importance of sustainable practices for the long-term viability of both the natural environment and the tourism sector.

Forest management to Achieve Sustainable Forestry Policy in Indonesia

Due to the uncertainty of forest areas in Indonesia, it often creates conflicts with various parties with an interest in forest areas, such as communities, institutions that have forestry business permits, and the government. Changes in forest area occur from time to time due to forest conversion for non-forestry sector development, encroachment, and forest fire. The government has issued Government Regulation Number 23 of 2021 about forest administration in relation to this issue. However, the law has not been effective in safeguarding forests from damage caused by humans, fires, natural disasters, pests, and diseases, nor has it preserved and protected state, community, and individual rights to wooded property. The method utilized in this study is empirical legal research employing sociological, conceptual, and statutory approaches. The specific objective of this research is to identify an ideal concept for forest utilization that is pro-environment, pro-development, and people-centered so that sustainable development-supporting forestry policies can be considered. The findings of the study reveal that sustainable forest management is largely dependent on the diversity and quality of enabling policies, as well as the legal and institutional conditions that serve as the basis for good forest governance. Forest management relies heavily on the collaboration between the government, the commercial sector, and the community.

LANDUSE/LAND COVER CHANGE DETECTION OF KATERIGI ARTISANAL GOLD MINE IN NIGER STATE NIGERIA

Mining activities in Niger State has attracted people from all over the Nigeria in search of gold and other solid minerals. The issue of insecurity and economic hardship has triggered the exploitation living behind serious human and environmental impact. The land use and cover classification was conducted using recent technology of GIS software (Arcgis). this was done in other to trace the change detection that have occurred over the years in the mines the result were found out that the artisanal mining have brought rapid urbanization and high health impact of due the mining process. The magnitude of change of forest area in 30 years between 1990 to 2020 shows that forest decreased further by -30.82Sq. km representing a change (18.01%) of the total change for the period in the Mining zones.

Changes in Forest Vegetation Carbon Storage and Its Driving Forces in Subtropical Red Soil Hilly Region over the Past 34 Years: A Case Study of Taihe County, China

After decades of afforestation, China’s forest coverage has increased significantly, but the forest quality and its impact on ecosystem services are still controversial. Taking forest vegetation carbon storage as an example, most assessment results based on the national forest inventory data showed that the contribution of enlarged forest area to the increase in China’s forest vegetation carbon storage was higher than that of the change in forest quality (vegetation carbon density). This means that the increase in forest carbon storage in China was mostly contributed by the increase in forest area and was less due to the increased forest vegetation carbon density. However, the national forest inventory data are based on national permanent plots that may be protected or are less disturbed to some extent. Therefore, the contribution of the change in forest quality to the increase in forest vegetation carbon storage, as evaluated from the national forest inventory data, might be overestimated, especially in regions with frequent human activities. To test the hypothesis, we selected Taihe County, a typical representative of the red soil hilly region in subtropical China, where the population is dense, economic development is fast, and the forest has experienced destruction and re-establishment. To accurately assess the impact of changes in the forest area and forest quality on forest vegetation carbon storage variation in real situations, we collected and used a series of remote sensing images from 1986 to 2019, inventory data for forest management, and field data. The results showed that the forest area and forest vegetation carbon density increased from 10.85 × 104 ha and 17.89 Mg/ha in 1986 to 16.40 × 104 ha and 26.51 Mg/ha in 2019, with an increase of 51.11% and 48.23%, respectively. Meanwhile, the forest vegetation carbon storage increased by 123.99%, from 1.94 Tg in 1986 to 4.35 Tg in 2019, suggesting a significant carbon sequestration ability. Further analysis showed that the contributions of changes in forest area and forest quality to the forest vegetation carbon storage variation were 1.23 Tg (51.19%) and 1.17 Tg (48.81%), respectively. The result implies that the increase in forest area and forest quality almost contributed equally to the increase in forest vegetation carbon storage during the 34 years of vegetation restoration in Taihe County. However, forest vegetation carbon density controlled the variation of forest vegetation carbon storage in all three forest developing stages. The precision of our results was also tested with two inventory datasets for forest management in Taihe County in 2009 and 2019. The relative contribution of forest vegetation carbon density is lower than most of the previous study results using national forest inventory data in this region, indicating that the contribution of change in the forest vegetation carbon density to the forest vegetation carbon storage variation might be overestimated based on these national permanent plots, which were protected or less disturbed to some extent.

Long-term dynamic monitoring of forest area changes with the aid of Google Earth Engine

Remote sensing has become the most effective tool for monitoring and evaluating forest resources. Owing to the development of big data, current research on forest resource information mainly focuses on a regional, national, or global scale. However, there are still certain limitations on small regional scales. We used supervised classification based on the Google Earth Engine cloud platform to extract county land use information and a Whittaker smoother to extract vegetation indices to monitor forest area and vegetation indices in the study areas from the 1980s to the 2020s. The results show that the classification accuracy is improved for the study area where the image quality is affected by clouds after adding complementary datasets of greenness and water bodies. The overall accuracy (OA) increases from 0.82 to 0.89, and the kappa accuracy increases from 0.77 to 0.85. The OA of the classification results was 0.88, and the forest area increased and decreased with an overall increase of 225.35 km2. The building area continued to increase with an increase of 34.88 km2. The cropland area first decreased and then increased with a complete decrease of 243.6 km2. The bare soil area decreased by 16.2 km2. The area of the water bodies was relatively stable. The vegetation index indicated good forest growth. Jiangle County’s forest area and forest growth stability cannot be achieved without the protection of national and local policies.

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

The global deforestation rate continues to worsen each year, and will eventually lead to various negative consequences for humans and the environment. It is essential to develop an effective forest monitoring system to detect any changes in forest areas, in particular, by monitoring the progress of forest conservation efforts. In general, changes in forest status are difficult to annotate manually, whereby the boundaries can be small in size or hard to discern, especially in areas that are bordering residential areas. The previously implemented forest monitoring systems were ineffective due to their use of low-resolution satellite images and the inefficiency of drone-based data that offer a limited field of view. Most government agencies also still rely on manual annotation, which makes the monitoring process time-consuming, tedious, and expensive. Therefore, the goal of this study is to overcome these issues by developing a forest monitoring system that relies on a robust deep semantic segmentation network that is capable of discerning forest boundaries automatically, so that any changes over the years can be tracked. The backbone of this system is based on satellite imaging supplied to a modified U-Net deep architecture to incorporate multi-scale modules to deliver the semantic segmentation output. A dataset of 6048 Landsat-8 satellite sub-images that were taken from eight land parcels of forest areas was collected and annotated, and then further divided into training and testing datasets. The novelty of this system is the optimal integration of the spatial pyramid pooling (SPP) mechanism into the base model, which allows the model to effectively segment forest areas regardless of their varying sizes, patterns, and colors. To investigate the impact of SPP on the forest segmentation system, a set of experiments was conducted by integrating several variants of SPP ranging from two to four parallel paths with different combinations of pooling kernel size, placed at the bottleneck layer of the U-Net model. The results demonstrated the effectiveness of the SPP module in improving the performance of the forest segmentation system by 2.57%, 6.74%, and 7.75% in accuracy (acc), intersection over union (IoU), and F1-score (F1score), respectively. The best SPP variant consists of four parallel paths with a combination of pooling kernel sizes of 2×2, 4×4, 6×6, and 8×8 pixels that produced the highest acc, IoU, and F1score of 86.71%, 75.59%, and 82.88%, respectively. As a result, the multi-scale module improved the proposed forest segmentation system, making it a highly useful system for government and private agencies in tracking any changes in forest areas.

The Climate Response to Global Forest Area Changes under Different Warming Scenarios in China

The Climate Response to Global Forest Area Changes under Different Warming Scenarios in China

Deep Learning to Assess the Effects of Land use/Land Cover and Climate Change on Landslide Susceptibility in the Tra Khuc River Bassin of Vietnam

Understanding the negative effects of climate change and changes to land use/land cover on natural hazards is an important feature of sustainable development worldwide, as these phenomena are inextricably linked with natural hazards such as landslides. The contribution of this study is an attempt to develop a state-of-the-art method to assess the effects of climate change and changes in land use/land cover on landslide susceptibility in the Tra Khuc river basin in Vietnam. The method is based on machine learning and remote sensing algorithms, namely radial basis function neural networks–search and rescue optimization (RBFNN–SARO), radial basis function neural network–queuing search algorithm (RBFNN–QSA), radial basis function neural network–life choice-based optimizer (RBFNN–LCBO), radial basis function neural network–dragonfly optimization (RBFNN–DO). All proposed models performed well, with AUC value of >0.9. The RBFNN–QSA model performed best, with an AUC value of 0.98, followed by RBFNN–SARO (AUC = 0.97), RBFNN–LCBO (AUC = 0.95), RBFNN–DO (AUC = 0.93), and support vector machine (SVM; AUC = 0.92). The results show that both climate and land use/land cover change greatly in the future: Precipitation increases 18% by 2030 and 25.1% by 2050; the total production forest, protected forest and built-up area change considerably between 2010 and 2050. These changes influence landslide susceptibility: The area of high and very high landslide susceptibility decrease by approximately 100 and 300 km2 respectively in the study area from 2010 to 2050.The findings of this study can support decision-makers in formulating appropriate strategies to reduce damage from landslides, such as limiting construction in areas where future landslides are predicted. Although this study applies to a particular region of Vietnam, the findings can be applied in other mountainous regions around the world.

Future Carbon Sequestration and Timber Yields from Chinese Commercial Forests under Shared Socioeconomic Pathways

Socio-economic status, technologies, and policies are key factors affecting forest management planning and forest ecosystem functions. This study applied shared socioeconomic pathways (SSPs) to a forest-management model framework. The potential timber yields and carbon sinks of spatially allocate alternatives were examined by quantifying their consequent changes at the regional tree species level in Chinese commercial forests (CFs) under the harvest and afforestation restrictions. The results indicate that the annual carbon sequestration rate of China’s CFs over the next 50 years is estimated to be 152.0–162.5 Tg/a, which can offset approximately 5% of the anthropogenic CO2 emissions identified in 2019. Newly planted and regenerated forests can contribute more than 80% of this offset. The annual timber supply capacity is estimated to be 119.2–142.4 million m3/a with current policy interventions, which is not enough to meet the demand for China’s timber market. Although most existing forests are managed as the primary source for forest goods and carbon service, the total commercial forest area changes are not as large as expected, resulting in only 2.0–10.6% differences. Our results also demonstrate that socioeconomic factors (e.g., social preference, carbon price, and forest logging and silvicultural practices) have a strong impact on carbon sinks but a minor impact on timber yields timber, except for improving harvesting and processing technologies. Establishing local long-term effective forest management systems and making afforestation and regeneration as a priority at the national level are suggested to comprehensively enhance the carbon sequestration and timber-supplying abilities of regional CFs.

Global forest products markets and forest sector carbon impacts of projected sea level rise

Sea level rise (SLR) is among the climate-change-related problems of greatest concern, threatening the lives and property of coastal residents and generating far-reaching economic and ecological impacts. We project that SLR will lead to an increase in the rate of new housing construction to replace destroyed structures, impact global wood products supply and demand conditions, and cause changes in global forest sector carbon mitigation potential. Findings indicate that 71 million new units will be built by 2050 to accommodate the SLR-affected global population. More than two-thirds of these new units are projected to be in Asia. The estimated extra wood products needed to build these new residential units is 1,659 million m3, assuming that all these structures would be built mainly with wood, representing a 4 % increase in total wood consumption, compared to projected reference level global wood products consumption. Increased timber removals to meet this higher construction wood demand (alternative scenario) is shown to deplete global forest carbon by 2 % by 2050 compared to the reference scenario. However, all such projected declines in forest biomass carbon could be more than offset by increased carbon sequestration in harvested wood products, avoided emissions due to substitution of wood for non-wood materials in construction, and biomass regrowth on forestland by 2050, with an estimated net emissions reduction benefit of 0.47 tCO2e/tCO2e of extra wood used in SLR-related new houses over 30 years. The global net emissions reduction benefit increased to 2.13 tCO2e/tCO2e of extra wood when price-induced changes in forest land area were included.

Using multiple linear regression to analyze changes in forest area: the case study of Akdeniz Region

Forest area losses are one of the most significant changes in land cover. These losses negatively affect ecosystems and cause severe economic and social life problems. It is necessary to monitor the process carefully and analyze the effects well to minimize all these negative effects in forest land losses and improve the development in urban areas positively. It is of great importance that these analyses are carried out quickly and accurately in terms of developing the natural environment. In this study, the effects that cause forest losses in the Mediterranean Region over the years are examined with the data obtained with the Google Earth Engine (GEE). Within the scope of the study, the changes in forest areas in the Mediterranean Region between 2004 and 2019 have been examined by considering many factors. In the study, Normalized Difference Vegetation Index (NDVI), precipitation, temperature, land surface temperature, aerosol optical depth, ozone, fire, urban areas, and population data were obtained with GEE. The data obtained were analyzed statistically, and the factors affecting the losses in forest areas the most were determined.

What can the Glasgow Declaration on Forests bring to global emission reduction?

What can the Glasgow Declaration on Forests bring to global emission reduction?

The Influence of Urbanization to the Outer Boundary Ecological Environment Using Remote Sensing and GIS Techniques—A Case of the Greater Bay Area

Urbanization brings great enrichment to human production and life, but also has certain environmental impact on the area where the city is located. Many studies have revealed the negative effects of urbanization on the ecological environment of urban or urban agglomerations, especially in the early stage of urbanization, but there are few studies on the impact on the peripheral ecological space environment. Will the peripheral environment be better off with less human interference as people move to cities during urbanization? In order to answer this question, we took the Guangdong-Hong Kong-Macao Greater Bay Area, the most economically dynamic area in China, as an example to explore the relationship between impervious changes of urban agglomerations monitored by remote sensing in the Bay Area and ecological indicators of forest and grassland in Guangdong Province outside the Bay area. The results showed that:(1) in the past 30 years, the area of grassland outside the bay area did not change regularly, while the area of forest decreased year by year. The landscape indices of forest and grassland were gradually fragmented and discrete. Moreover, the distribution of Fraction Vegetation Coverage (FVC) of forest and grassland has changed since before urbanization. (2) Through correlation analysis, it is found that the changes in forest area and the landscape index of forest and grassland are strongly correlated with the development of urbanization in the Greater Bay Area. This shows that the process of urbanization in the Greater Bay Area will have a non-negligible impact on the peripheral environment. In the process of urban development, we should not only focus on the inner city but also consider the outer environment of the city.

Mapping The Rate Of Deforestation In South Kalimantan Using Landsat Imagery

South Kalimantan Province is an area that has a very large forest area, but this forest continues to decrease due to deforestation. Deforestation continues to be one of the factors causing flooding. Flooding in this area is a recurring disaster that must be overcome. For this reason, it is necessary to calculate changes in forest area so that forest processing can be carried out properly and that banners can be overcome. In this study, the rate of deforestation was calculated using a map of land use change in South Kalimantan Province through the google earth engine platform using Landsat imagery. The method in this research is supervised classification, which is taking polygon samples in land use classification. The image classification algorithm with javascript on the google earth engine platform will classify the same pixel values into the same classification based on the samples that have been made. Through this platform, images in geo.tif format will be obtained. then the image is processed using ArcGIS software to process the image into a map and the area of each classification. This map was then overlaid between 2015, 2018, and 2020 to see the changes. Based on the results of the study, it was found that there was a change in land use, especially in the forest class and community plantations. Forests decreased to 311,255 hectares with a decreasing rate of 62,251 hectares every year, while community plantations increased by 328,858 hectares with an increase of 65,771 hectares every year.

Forest management plan based on carbon sequestration model

The main purpose of this question is to establish a carbon sequestration model that is the most effective in sequestering carbon dioxide to determine the carbon sequestration capacity of forests and their products. A comprehensive evaluation model was established by the entropy weight method on the basis of the three indicators that affect the carbon sequestration amount, social factors such as "residents near the forest" and social and economic benefits are introduced to determine the scope of the forest management plan. We incorporate factors such as tree age change rate and forest area change rate into the forest management plan. We also establish a multiple regression prediction model to predict the carbon sequestration amount of forests in each year, and then added up to obtain the carbon sequestration carbon amount in 100 years.