Forest Change Research Articles

Coastal Forest Change and Shoreline Erosion across a Salinity Gradient in a Micro-Tidal Estuary System

Coastal Forest Change and Shoreline Erosion across a Salinity Gradient in a Micro-Tidal Estuary System

Potential of Earth Observation to Assess the Impact of Climate Change and Extreme Weather Events in Temperate Forests—A Review

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level.

Effects of the Co-Application of Glucose, Nitrogen, and Elevated Temperature on Buried Black Soil Carbon in a Cool Temperate Deciduous Broad-Leaved Forest

Accurately predicting the feedback mechanisms between forest ecosystem carbon cycling and climate change is crucial for effective climate mitigation. Understanding soil organic carbon (SOC) responses to the combined impacts of plant biomass, litter, and nitrogen deposition, especially regarding temperature sensitivity, is essential but remains poorly understood. We conducted incubation experiments using buried black soil from a cool temperate deciduous broad-leaved forest in Japan, which has high C content and a highly stable molecular structure. The stepwise addition of glucose and a temperature increase from 15 to 35 °C accelerated SOC mineralization by 74.0 mg C kg−1 with a positive priming effect (PE) during the 49-day incubation period, while the simultaneous addition of nitrogen had no significant effect on this phenomenon, with SOC mineralization measured at 75.5 mg C kg−1. Conversely, glucose mineralization was significantly accelerated by 10%, from 241.0 to 261.3 mg C kg−1, by stepwise nitrogen addition and temperature increase. Under the combined impacts, the Q10 value of the soil increased significantly from 1.6 to 2.0 compared to that in the unmodified conditions, primarily due to the stepwise addition of glucose. We also found a strong positive correlation between activation energy (Ea) and Q10. This result strongly supports the carbon quality–temperature (CQT) hypothesis. These results likely stem from interactions between SOC quality and carbon availability, suggesting that, in the future, climate change is likely to have a positive feedback effect, especially on buried black soils.

Integrating local ecological knowledge and remote sensing reveals patterns and drivers of forest cover change: North Korea as a case study

Satellite-based remote sensing approaches provide a cost-efficient means to collect information on the world’s forests and to repeatedly survey large, or inaccessible, forest areas. However, it may not always be possible to ground truth–associated findings using direct ecological field surveys conducted by trained forest scientists. Local ecological knowledge (LEK) is an alternative form of data which could be used to complement, interpret and verify information from satellite data. Using a case study on the Democratic People’s Republic of Korea (North Korea), we evaluate the potential for integrating remote sensing and LEK data, gathered with non-specialist former residents, to understand patterns and drivers of forest cover change. LEK reports often concurred with, or provided key information to enable interpretation of, satellite data. This revealed that between 1986 and 2021, North Korea experienced high, but uneven, rates of deforestation. There was a pronounced northwards deforestation shift in the mid-1990s, coinciding with a period of extreme hardship and famine (the “Arduous March”), and associated with clearance of trees in more forested northern provinces as an economic and fuel resource, and conversion of forest to agricultural cropland. Loss of forest cover in North Korea has continued and recently accelerated, to a rate of > 200 km2 per annum between 2019 and 2021. This increases the vulnerability of North Korean socio-ecological systems to future environmental change and is an obstacle to the recovery of threatened species across the Korean Peninsula. We recommend that LEK- and remote sensing–based approaches are considered within a suite of complementary techniques to analyse forest changes where ecological field surveys cannot be conducted.

Integrating multi-source remote sensing data for mapping boreal forest canopy height and species in interior Alaska in support of radar modeling

Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) and forest species (FS) distributions in the Delta Junction region of interior Alaska, by integrating multi-source remote sensing observations within a machine learning framework based on the extreme gradient boosting technique. Model inputs included multi-frequency (C-/L-/P-band) SAR observations from Sentinel-1, UAVSAR (Uninhabited Aerial Vehicle SAR) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface), and Sentinel-2 optical reflectance data. LVIS (Land Vegetation and Ice Sensor) LiDAR measurements (RH98) and Tanana Valley State Forest timber inventory data were used as respective canopy height and species ground truth data. The combination of multi-source datasets produced the best model performance (RMSE 1.62 m for FCH, and 84.27% overall FS classification accuracy) over other models developed from single source observations. The resulting FCH and FS maps using multi-source datasets were derived at 30 m spatial resolution and showed favorable agreement with plot level field measurements from the Forest Inventory and Analysis record. The model results also captured characteristic differences in stand structure between dominant species and from post-fire vegetation succession. Our results show the potential of multi-source remote sensing observations, including low frequency microwave sensors, for monitoring boreal forest complexity and changes due to global warming.

MUNICIPAL WASTE MANAGEMENT IN TIER-2 CITIES IN NORTHERN INDIA

Public health and environmental concerns, along with the sheer amount of waste being generated, have made managing municipal solid waste (MSWM) a significant task today. Due to institutional and financial constraints, research shows that many urban local bodies (ULBs) in India find it difficult to handle the country's high solid waste loads. ULBs frequently lack the infrastructure, finances, resources, and efficient plans required for improved solid waste management. India faces a number of difficulties in managing its municipal solid waste, including issues with garbage segregation, door-to-door collection, waste treatment technology, land resources, and appropriate disposal techniques. The Ministry of Environment, Forest and Climate Change and the Ministry of Urban Development (MoUD) have put in place a number of policies and initiatives to address these issues and enhance the nation's municipal solid waste management (MSWM) system as it is now. The Municipal Solid Waste Management Rules were first released by the Environment Ministry in 2000. They have since been updated and are now known as the Solid Waste Management Rules 2015. To help cities and towns plan and execute efficient MSWM systems in compliance with the 2015 SWM Rules amendment, the MoUD has also created a draft MSWM manual. Due to insufficient enforcement by regulators and a lack of awareness among stakeholders, many policies and programmes frequently fall short of their objectives. This essay provides a comprehensive review of solid waste management and highlights important facets of the policies and initiatives the Indian government has put in place to address the issues in this field.

LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert

The Land Use Change Alert (LUCA) dataset was developed for effective and timely monitoring of global forest changes that are mostly associated with human activities. Near- real-time changes of forest land use are mapped at 0.05 ha minimum mapping unit for all forest types across the Earth’s ecoregions, every two weeks. LUCA is based on Sentinel-1 cloud penetrating synthetic aperture radar (SAR) observations to circumvent limitations of optical imagery from pervasive cloud cover over forested areas globally, and especially in the tropics. The methodology is based on a combination of time-series change detection and machine learning analytics to achieve high accuracy of alerts across all ecoregions and landscapes globally with an average area-adjusted users and producers accuracy of 83% and 63%, respectively. The bi-weekly global alert maps capture forest clearing associated with deforestation and industrial timber harvesting, along with forest degradation associated with selective logging, fragmentation, fire, and roads. The product was developed and released publicly through Google Earth Engine to allow for the rapid assessment of land use change activities, quantifying patterns and processes driving forest change and dynamics across forest ecoregions. LUCA is designed to help monitor a variety of emission reduction programs at the local to regional scales and play a key role in implementing regulations on deforestation-free products.

Spatiotemporal assessment of deforestation and forest degradation indicates spillover effects from mining activities and related biodiversity offsets in Madagascar

Mining has severe environmental and social impacts. To compensate for the environmental damage caused at mining sites, mining companies are required to engage in biodiversity offsetting activities elsewhere. In forest landscapes, most offsetting policies focus on compensating for biodiversity loss from deforestation, while forest degradation is largely ignored – even though it contributes substantially to biodiversity loss. One reason for this is that forest degradation is challenging to assess and monitor. This study focuses on a large nickel and cobalt mine in Madagascar. By analysing remote sensing time series, we assess detailed annual forest change dynamics and distinguish different types of forest disturbance within and around the mining lease area and the two main associated biodiversity offset areas between 2006 and 2020. Our results show that deforestation rates within the two biodiversity offset areas are low (18 ha, or 0.4%; 164 ha, or 2.4%), suggesting that conservation measures are effective. However, this is not the case when looking at forest degradation. We found that substantial shares of forest within the two biodiversity offset areas are affected by degradation (545 ha, or 11.4%; 662 ha, or 9.7%). In the surrounding unprotected landscape, the rates of deforestation (451 ha, or 6.7%; 553 ha, or 4.9%) and forest degradation (2360 ha, or 34.8%; 5794 ha, or 51.1%) are much higher. The spatiotemporal pattern indicates spillover effects for both deforestation and forest degradation. Taken together, our findings show that restrictions on local communities’ access to forest resources within biodiversity offset areas affect the surrounding landscape and can cause substantial additional adverse environmental impacts there. We also demonstrate that forest degradation monitoring is feasible, and that forest degradation is widespread even though it is still largely ignored. These findings should be considered in future biodiversity offsetting policies and best practices.

Spatiotemporal forest and land cover change in Türkiye: The role of economic factors in driving environmental transformations

Spatiotemporal forest and land cover change in Türkiye: The role of economic factors in driving environmental transformations

Investigating Forest Cover Change Using Historical GIS Technologies: A Case Study with an Example of Jurbarkas District of the Republic of Lithuania

Effective forest management systems based on comprehensive tools need to be developed for the analysis and assessment of forest cover change. This requires a wide range of data, starting from old maps, which allow the reconstruction of spatially referenced information on past forest areas, to recently collected data. The research methodology proposed in this paper involves a combined analysis of data from different sources, using statistical, comparative, and visual analysis methods. The case study is Jurbarkas District of the Republic of Lithuania characterized by a high land productivity score. The research found that the highest forest cover areas were in the 19th century and the lowest in the 20th century. It can be seen that during the period under research, forest areas ‘migrated’ from an area favorable for agricultural activities to a less favorable area with higher slopes. Permanent forest cover areas and the age of the dominant trees have been identified. The age of the trees in the forest areas has allowed for the identification of the periods when the most intensive logging took place. The rate of forest cover change made it possible to predict the total disappearance of the forest areas provided that the factors influencing forest cover change at that time remained unchanged. If the trends in forest change in Jurbarkas District had been maintained in the period between the 19th and 20th centuries, the forest would have disappeared in 2006.

Using a Logistic Regression Model to Examine the Variables Influencing Changes in Northern Thailand’s Forest Cover and Comparing Machine Learning Algorithms

Protecting biodiversity and keeping the Earth’s temperature stable are both very important jobs performed by tropical forests. In the last few decades, remote sensing has given us new tools and ways to track changes in land cover. To understand what causes changes in forest cover, it is important to look at the things that affect those changes. However, there is not enough research that uses a logistic regression model (LRM) and compares the results with machine learning (ML) techniques to investigate the specific factors that cause forest cover change in remote mountainous areas like Thailand’s Mae Hong Son and Chiang Mai Provinces. Following a comparison of an LRM, a random forest, and an SVM, this study of the causes of changes in forest cover in Mae Hong Son found six important factors: soil series, rock types, slope, the NDVI, the NDWI, and the distances to city areas. Compared to the LRM, both the RF and SVM machine learning algorithms had higher values for the kappa coefficient, sensitivity, specificity, accuracy, positive and negative predictions, and sensitivity, especially the RF. Following what was found in Mae Hong Son, when the important factors were examined in Chiang Mai, the RF came out on top. It is believed that these results can be used in more situations to help make plans for restoring ecosystems and to promote long-lasting methods of managing land use.

Ứng dụng GIS và tư liệu viễn thám đánh giá biến động lớp phủ rừng tại vùng đệm Vườn Quốc gia Cát Tiên thuộc địa phận huyện Tân Phú, tỉnh Đồng Nai.

The Cat Tien National Park in the South of Vietnam represents a protected ecological area, it is located in tropical rainforest zone. For assessment of forest cover change in three buffer zones of Cat Tien National Park, namely Nam Cat Tien, Nui Tuong and Phu An at Tan Phu district, satellite images (Landsat 5 TM and Landsat 8 OLI) take in 1990, 2000, 2010 and 2020 were used. Land use/land cover were classified by 5 categories using maximum likelihood classifier (MLC) method, result of constructing a key for image classification showed that images without reference data for classification have over 83.50% of map accuracies. After classification, differentiation of forest land use/landcover was analysied in each year. Deforestation and agricultural area reduction took place in 1990 - 2020 period. Besides, study has quantified changes in forest cover during periods: 1990 - 2000, 2000 - 2010 and 2010 - 2020. As a result, findings show that the extents of forest cover have decreased from 5522.22 ha to 2620.98 ha during the period of 1990 - 2020. Maps of forest status as well as changes in forests can be used for forest resource management activities under the Cat Tien National Park management.

Deep Learning Techniques for Enhanced Mangrove Land use and Land change from Remote Sensing Imagery: A Blue Carbon Perspective

Deep learning techniques are revolutionizing the analysis of remote sensing imagery for mangrove land use and land change studies. These methods improve the accuracy and efficiency of identifying changes in mangrove forests, providing valuable insights for environmental monitoring and conservation efforts. This study presents an in-depth analysis of land cover changes on Hainan Island, China, over 30 years from 1993 to 2023, using Landsat 5 imagery. Through the classification of land cover into six distinct categories—barren/agriculture, urban/infrastructure, high vegetation, low vegetation, water, and clouds—we examined the temporal shifts and trends in the island's landscape. An accuracy assessment using high-resolution Landsat Satellite imagery provided validation for the classification method, achieving an accuracy of over 70%. The results revealed a notable urban expansion and recuperation of high vegetation areas, indicating a strong interaction between human activities and natural ecosystems. The study underscores the dynamic nature of land cover influenced by both anthropogenic and natural factors and highlights the importance of remote sensing in environmental monitoring. Future work will involve the adoption of newer satellite technologies and machine learning techniques to enhance the precision of land cover classification, integrate additional ecological data, and employ predictive modeling for sustainable land management strategies. The continuation of these efforts is crucial for informing policy decisions to ensure the preservation of Hainan's rich biodiversity and natural resources.

The area and distribution changes of mangrove forests on the coast of Gresik Regency, East Java, 2012-2023

Abstract Environmental dynamics in coastal areas have resulted in changes in the area and distribution of mangrove forests in Gresik Regency. Mangroves have ecological and economic functions for living things. This study aims to analyse changes in the area and distribution of mangrove forests in Gresik Regency in 2012 and 2023. The data source is IKONOS imagery from Google Earth in 2012 and 2023, the coastal location of Gresik Regency, East Java Province. Interpretation and digitisation were done to determine the area and distribution of mangrove forests, and maps of the mangrove forests were produced in 2012 and 2023. Then, the two maps were overlayed to determine their area and distribution changes. Based on image interpretation and analysis, in 2012, the area with the largest mangrove forest was Ujung Pangkah District (802.75 hectares) or 63.21% of the total mangrove forest area of Gresik Regency. The largest mangrove forest in 2023 is still in Ujung Pangkah District, with an area of 1261.50 hectares or 64.26%. For ten years (from 2012 to 2023), Gresik Regency’s mangrove forests increased by 590.41 hectares, the most significant increase in Ujung Pangkah District (increased by 458; 76 hectares).

Analyzing Urbanization using Satellite Image Processing

Abstract: Urbanization is directly proportional to the increase in population, which contributes to the increase in country’s GDP but affects the environment and ecosystem. This paper introduces an analysis of the satellite imagery of Mumbai for past 5 years (2019-2023). Mumbai has witnessed significant changes in the geographic region. In order to control and prevent the effects of urbanization, it is necessary to analyze the geographical changes and effects of the same. In this proposed analysis, we have analyzed urbanization with respect to changes in water, forest, urban land and barren land. Use of satellite imagery plays an important role in providing the opportunity for this analysis. Google Earth Engine (GEE) is used for the change detection for the selected area using the satellite imagery dataset provider COPERNICUS named Harmonized Sentinel-2 MSI as an input to this analysis. For this analysis we have used 3 classification methods i.e. supervised, unsurprised and NDVI

A shift in transitional forests of the North American boreal will persist through 2100

High northern latitude changes with Arctic amplification across a latitudinal forest gradient suggest a shift towards an increased presence of trees and shrubs. The persistence of change may depend on the future scenarios of climate and on the current state, and site history, of forest structure. Here, we explore the persistence of a gradient-based shift in the boreal by connecting current forest patterns to recent tree cover trends and future modeled estimates of canopy height through 2100. Results show variation in the predicted potential height changes across the structural gradient from the boreal forest through the taiga-tundra ecotone. Positive potential changes in height are concentrated in transitional forests, where recent positive changes in cover prevail, while potential change in boreal forest is highly variable. Results are consistent across climate scenarios, revealing a persistent biome shift through 2100 in North America concentrated in transitional landscapes regardless of climate scenario.

Fine Root Density Dynamics and Carbon Stock of Eucalyptus spp.: Interplay of Age, Genotype, and Edaphoclimatic Conditions.

Roots play a fundamental role in forest ecosystems, but obtaining samples from deep layers remains a challenging process due to the methodological and financial efforts required. In our quest to understand the dynamics of Eucalyptus roots, we raise three fundamental questions. First, we inquire about the average extent of the roots of two contrasting Eucalyptus genotypes. Next, we explore the factors that directly influence the growth and depth of these roots, addressing elements such as soil type, climate, and water availability. Lastly, we investigate how the variation in Eucalyptus species may impact root growth patterns, biomass, and carbon stock. In this study, we observed that the maximum root depth increased by an average of 20% when genotypes were grown on sites with higher water availability (wet site). E. urophylla stands had a higher biomass and carbon stock (5.7 Mg C ha-1) of fine roots when cultivated on dry sites (annual rainfall~727 mm) than the wet sites (annual rainfall~1590 mm). In E. grandis × E. camaldulensis stands, no significant differences were observed in the stock of fine root biomass (3.2 Mg C ha-1) between the studied environments. Our results demonstrated that genotypes with greater drought tolerance (E. grandis × E. camaldulensis) tend to maintain higher stocks of fine root biomass (3.2-6.3 Mg ha-1) compared to those classified as plastic (E. urophylla), regardless of the edaphoclimatic conditions of the cultivation site. Finally, our research helps understand how Eucalyptus trees adapt to their environment, aiding sustainable forest management and climate change mitigation. We also provide a practical tool to estimate underground biomass, assisting forest managers and policymakers in ensuring long-term forest sustainability.

Spatial Temporal Analysis of Air Quality and their Relation to Meteorological Parameters, in India

Air pollution has become a significant global issue, with India ranking 8th in the world for hazardous air quality based on the latest 2022 report. (World Air Quality Report 2023). The growing urban sprawl and human-induced environmental activities have led to a decline in air quality. The Air Quality Index assesses the level of pollution in the air at a particular region. It is a fundamental right for individuals to be informed about the quality of the air they breathe for their well-being. This study aims to examine air pollution levels and trends across various geographical locations in India using data provided by the Central Pollution Control Board under the Ministry of Environment, Forest, and Climate Change in India. The study focuses on analyzing the annual growth of pollutants such as SO2, NO2, PM10, and PM2.5 from 2016 to 2021, collected from monitoring stations in 28 states across 256 locations. The research evaluates the impact of different particulate matter concentrations and showcases the spatial variation of air pollutants between 2016 and 2021. Results indicate a significant decrease in air pollution levels during lockdown periods compared to pre-lockdown periods. On an average (over all cities), more than 24% decrease has been observed for all the AQI of the pollutants. Additionally, a comparative analysis reveals correlations between meteorological factors and air quality, showing that temperature, relative humidity, and wind speed are negatively correlated with AQI, while surface pressure is positively correlated. Temporal analysis was also conducted to examine the relationship between air pollutant concentrations and meteorological parameters.

Effectiveness assessment of protected areas based on the states, trends, and relative changes in forest ecosystem: a case study in the Three Parallel Rivers Region, China

Establishing protected areas (PAs) is a major measure of biodiversity conservation, and various methods have been explored to assess PAs’ effectiveness. However, those methods mainly compared the relative changes in land cover between treated samples inside the PAs and their matched samples outside the PAs, which would produce misjudgments, especially in some climax communities with a relatively steady state. Thus, in this study, we constructed an integrated framework through a series of assessments according to the state, trend, and relative change of each PA to explore the conservation effectiveness of PAs in the Three Parallel Rivers Region in China from 2000 to 2020. Here, “state” refers to the difference among samples from within and outside the PA, assessed through yearly sample mean comparison. “Trend” means linear regression of mean forest area of each PA throughout the assessment period. “Relative change” means the difference in the mean value of the slope of forest changes between the treated samples inside each PA and their matched control samples outside of PAs. The entire forest area within all PAs in the Three Parallel Rivers Region showed a significant increasing trend from 2000 to 2020 (R2 = 0.919, P<0.05). Among all the PAs, twelve (86%) had a positive effect on protecting the forest ecosystem, and two had a nonsignificant effect. Among the factors affecting the state and relative change in PAs’ forests, the annual total precipitation was the most important, followed by distance to the nearest road. Moreover, the management-level variable was an essential factor in the state of PAs’ forest ecosystems, which indicated that national PAs (nature reserves and natural parks) were in a better state than local (provincial- and county-level) nature reserves. Overall, the conservation effectiveness of forests in PAs was assessed at a regional scale in the Three Parallel Rivers Region, implying that our framework would be additional useful in regions with high biodiversity and steady ecosystems. This framework better avoids underestimating conservation effectiveness assessment tasks than traditional methods do. Thus, we posit that this framework is suitable for future global or country-level assessments.

The falling of a tree in the forest is the beginning of significant changes in the soil

Abstract Aims The aim of the project was to determine the vertical variability of soil under the influence of deadwood (DWD) in a temperate forest ecosystem. Methods The laboratory analyses included soil layers of 0–5 cm, 5–10 cm, 10–20 cm and 20–40 cm, which were taken directly under the deadwood, as well as the forest litter layer and deadwood fragments. The control samples were taken 30 m away. Results The decomposition processes of deadwood are associated with a 55% increase in soil organic carbon (SOC) deposition to a depth of -40 cm and a 36% increase in total nitrogen (N) content compared to soils without deadwood. DWD significantly increases exchangeable cations, especially at a depth of -5 cm to -20 cm. Deadwood contains slightly more hydrogen (H+) and aluminum ions (Al3+) than forest litter, but soil acidification is related to pedogenic processes rather than decomposition of deadwood in hyperacid forest soils. The soil surface under deadwood with a high degree of decomposition is characterised by a lower bulk density (BD) value than the soil where only forest litter was present. Conclusions Our studies suggest that the physicochemical properties of forest soils change under the influence of deadwood such that the deeper layers beneath the logs take on propertie8s that make them similar to the shallower layers without deadwood. To summarise, leaving deadwood in the forest has a positive effect on soil fertility by enriching the soil with nutrients (Ca2+, K+, Na+, less Mg2+) and improving its physical properties.