Forest Change Research Articles

Detection of Change in Forest and Trees Outside Forest Using Normalized Difference Vegetation Index Techniques in Adamawa Central, Adamawa State Nigeria

Detection of Change in Forest and Trees Outside Forest Using Normalized Difference Vegetation Index Techniques in Adamawa Central, Adamawa State Nigeria

Individual tree detection and crown delineation in the Harz National Park from 2009 to 2022 using mask R–CNN and aerial imagery

Forest diebacks pose a major threat to global ecosystems. Identifying and mapping both living and dead trees is crucial for understanding the causes and implementing effective management strategies. This study explores the efficacy of Mask R–CNN for automated forest dieback monitoring. The method detects individual trees, delineates their crowns, and classifies them as alive or dead. We evaluated the approach using aerial imagery and canopy height models in the Harz Mountains, Germany, a region severely affected by forest dieback. To assess the model's ability to track changes over time, we applied it to images from three separate flight campaigns (2009, 2016, and 2022). This evaluation considered variations in acquisition dates, cameras, post-processing techniques, and image tilting. Forest changes were analyzed based on the detected trees' number, spatial distribution, and height. A comprehensive accuracy assessment demonstrated the Mask R–CNN's robust performance, with precision scores ranging from 0.80 to 0.88 and F1-scores from 0.88 to 0.91. These results confirm the model's ability to generalize across diverse image acquisition conditions. While minor changes were observed between 2009 and 2016, the period between 2016 and 2022 witnessed substantial dieback, with a 64.57% loss of living trees. Notably, taller trees appeared to be particularly affected. This study highlights Mask R–CNN's potential as a valuable tool for automated forest dieback monitoring. It enables efficient detection, delineation, and classification of both living and dead trees, providing crucial data for informed forest management practices.

Assessment of Species Composition, Diversity and Carbon Stock in a Community Managed Forest of Udaypur District of Nepal

Species composition, diversity, and carbon sock of forests are all critical factors that affect the ability of forests to provide various important ecosystem services. However, there is a notable dearth of research regarding these factors in the community forests of Udaypur district. Therefore, this research was undertaken to assess species composition, and biodiversity and quantify the carbon stock potential of the Sringar community forest (CF) of the Udaypur district of Eastern Nepal. A total of 57 circular plots of 500m2 were inventoried using a systematic random sampling method with 0.5 % sampling intensity. In the CF, 17 tree species from 15 genera and 11 families were identified. The dominant tree species, Shorea robusta observed with a maximum importance value index (IVI) (176.15). According to our study, the total biomass and carbon stock in Sringar CF were 276.98 ton ha-1 and 138.18 ton ha-1, respectively. Accurate estimation of soil carbon stocks is crucial for long-term forest management and climate change mitigation, and the integration of advanced monitoring techniques and predictive models to enhance accuracy and account for future climate projections is needed.

Late Pleistocene to Holocene vegetation changes in southeastern Patagonia (49° S): landscape changes related to disturbances

ABSTRACTSoutheastern Patagonia's (49° S) post‐glacial history inferred from high Andean lake sediments provides new insights regarding Late Pleistocene and Holocene vegetation dynamics at the eastern margin of the Southern Patagonian Icefield. The fossil records of pollen, charcoal and geochemical data from tephra layers from Laguna Chiquita and Laguna Gemelas Este were analysed to reveal past landscape dynamics related to vegetation changes, fire and volcanic events from ca. 15 200 cal a bp to the present. Nothofagus forest expanded over shrubland communities sometime between 15 200 and 4600 cal a bp, along with at least three disturbance sources related to the volcanic eruptions of Lautaro, Aguilera and Hudson, important local fire episodes, and neoglacial advances. Major charcoal deposition reveals moderate fire activity during the Late Pleistocene related to an open landscape characterised by a grass/shrub steppe. Local glacier advances may have affected the Laguna Gemelas Este sedimentation. Tephra deposition events do not correlate to vegetation changes inferred from the Laguna Gemelas Este and Laguna Chiquita pollen records. Late Holocene eastern Andean forest changes and fire activity at 49° S match other southern palaeoenvironmental records (50–52° S) suggesting that changes in the Southern Westerly Wind latitudinal position and intensity drove major palaeovegetation and fire dynamics before the European settlement. In the last centuries, fire and vegetation changes have been closely related to an increase in local ignition sources and the introduction of alien species.

Solar Radiation Triggers the Bimodal Leaf Phenology of Central African Evergreen Broadleaved Forests

AbstractCentral African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE‐AFP), which utilizes short‐wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE‐AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE‐AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE‐AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests.

Deforestation drivers in northern Morocco: an exploratory spatial data analysis

Formulating effective policies to address or mitigate deforestation requires a comprehensive understanding of the contributing factors. This study examines the drivers of deforestation from 2001 to 2020 in the Tangier-Tetouan-Al Hoceima (TTA) region, a northern Moroccan area distinguished by the country’s highest deforestation rate. Through an extensive review of existing literature and employing Geist and Lambin’s deforestation framework, we identified five key causes: infrastructure extension, agricultural expansion, logging, wildfires as direct causes, and demographic factors as an indirect cause. Data on deforestation and its contributing factors were sourced from diverse databases, including Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), World Population, Forest Proximate People (FPP), and National Forest Inventory (NFI) datasets. Pixel-level analysis of GFC data indicated that wildfires are the primary driver of deforestation in the region, accounting for 35.2%, followed by agricultural expansion (30.6%), logging (13.2%), and infrastructure extension (10.1%). The remaining 10.9% of losses were attributed to other disturbances, such as illegal extraction, pests, and dieback. Spatial patterns were further analyzed through Exploratory Spatial Data Analysis (ESDA) methods at a 1 km2 gridded scale, revealing strong clustering for all studied factors. Spatial relationships were explored using the bivariate local Moran’s index, which highlighted the highest spatial dependence between deforestation and fires (I = 0.21). Correlations between deforestation and other factors, including agricultural expansion, logging, infrastructure extension, and demographic pressure, were assessed at 0.18, 0.17, 0.08, and 0.05, respectively. Landscape pressures (LSP), encompassing deforestation, agricultural expansion, fires, infrastructure extension, and demographic pressure, were analyzed using the local Geary index, revealing a positive correlation in approximately 59% of spatial units. Last, a composite map of LSP clusters and an explanatory diagram illustrating dominant patterns in the TTA region were generated based on the results from local Geary’s multivariate and local Moran’s univariate tests.

Evaluation of large-scale deforestation susceptibility mapping in the mountainous region of the Himalayas: A case study of the Khangchendzonga Biosphere Reserve, India

The Khangchendzonga Biosphere Reserve (KBR) is located in the Eastern Himalayas and serves as a critical habitat for endemic species of flora and fauna, as well as playing a key role in carbon sequestration. The primary aim of this study was to map large-scale deforestation susceptibility zones in the mountainous region of KBR. The study area was divided into three zones: (a) Transition Zone, (b) Core Zone, and (c) Buffer Zone. This study utilized multiple remote sensing datasets acquired through the Google Earth Engine (GEE) platform, including precipitation, temperature, elevation, forest density, distance from rivers, NDVI, NDSI, distance from settlements, settlement density, distance from roads, and land use and land cover data. Additionally, the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods were employed to map deforestation susceptibility. To validate the proposed deforestation susceptibility, Hansen Global Forest Change (HGFC) data from 2001 to 2022 were used. Moreover, deforestation susceptibility was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metrics. Notably, our findings revealed significant declines in tree cover of 1.60%, 1.27%, and 0.89% in the Transition, Core, and Buffer Zones, respectively, during critical years (2009, 2011, 2019, 2020). These periods witnessed substantial deforestation, indicating a deteriorating condition of the reserve's forest cover. Although there were minor discrepancies in the results of the two methods, both highlighted the particular vulnerability of the transition zones in the eastern and southern regions of KBR. The comprehensive methodology employed in this research establishes an advanced spatial data infrastructure that is indispensable for immediate conservation planning and adaptive management strategies. The insights gleaned from this investigation hold substantial promise for guiding future restoration and conservation efforts aimed at enriching biodiversity and fortifying ecosystem services in this critical area.

The forecast and low‐carbon performance of land use in rapid urbanization area under the low‐carbon oriented spatial planning: Evidence from Hangzhou, China

AbstractThe introduction of the carbon peak and carbon‐neutral targets by many countries' central governments has put low‐carbon‐oriented spatial planning at the forefront of discussions. However, few studies have focused on the balance of carbon emission reduction and economic goals in spatial planning, and the governance influence on land use change simulation. This study addresses this gap by conducting an empirical analysis in the rapidly urbanizing area of Hangzhou, China, taking into consideration low‐carbon constraints and economic development demands. Using the stochastic impacts by regression on population, affluence, and technology (STRIPAT) model and linear programming–Markov, we simulate the governance decision‐making process to calculate the optimal land‐use structures under both low‐carbon and baseline scenario, then simulated land use patterns by using artificial‐neural‐network‐based cellular automata (ANN‐CA). The results showed 12.35% and 2.5% growth in urban and forest land, and 9.69% and 6.4% decline in farm and rural land under the low‐carbon scenario. 92.31% of urban land change occur in the downtown districts and suburbs; while 59.77% of farm land change and 95.53% of forest land change occur in the exurban districts. The low‐carbon performance of land use was reflected in carbon storage release, carbon emission capability change, and low‐carbon capability. The most common conversion of land use categories under the low‐carbon scenario was between farm and forest land, and between rural and urban land, which resulted in less carbon storage release and carbon emissions compared with the baseline scenario. Furthermore, under the low‐carbon scenario, the compactness of construction land increased by 2 × 10−5, while its fragmentation decreased by 0.0027. This study sheds light on the impact of low‐carbon‐oriented land use planning on urban land expansion, providing empirical evidence for city governments in rapid urbanization areas to improve land use efficiency.

Quantifying forest resource change on the Korean Peninsula using satellite imagery and forest growth models

Quantifying forest resource change on the Korean Peninsula using satellite imagery and forest growth models

Deforestation Trends and Drivers in Central Halmahera Regency

Land use patterns are strongly influenced by human requirements; among the most important problems is deforestation. Particularly with deforestation, central Halmahera Regency has seen significant changes in land usage. We use Qgis software and remote sensing data, to seeks and identify the rate of deforestation in Central Halmahera Regency. from Global Forest Change, that satellite pictures data. According to this studies, Weda and Maba were the most impacted areas in Central Halmahera experiencing the worst deforestation between 2014 and 2017. The conversion of forest land into nickel mining sites caused notable destruction in several places. With an average annual loss of 1,435.43 hectares between 2001 and 2013, data showing varying rates of deforestation between 2001 and 2022 shows Rising during 2014 and 2015, the pace of deforestation peaked at 8,852.506 hectares but started to fall in 2016 and 2017.

Generation of High Temporal Resolution Fractional Forest Cover Data and Its Application in Accurate Time Detection of Forest Loss

Fractional Forest cover holds significance in characterizing the ecological condition of forests and serves as a crucial input parameter for climate and hydrological models. This research introduces a novel approach for generating a 250 m fractional forest cover product with an 8-day temporal resolution based on the updated GLASS FVC product and the annualized MODIS VCF product, thereby facilitating the development of a high-quality, long-time-series forest cover product on a global scale. Validation of the proposed product, employing high spatial resolution GFCC data, demonstrates its high accuracy across various continents and forest cover scenarios globally. It yields an average fit coefficient of determination (R2) of 0.9085 and an average root-mean-square error of 7.22%. Furthermore, to assess the availability and credibility of forest cover data with high temporal resolution, this study integrates the CCDC algorithm to map forest disturbances and quantify the yearly and even monthly disturbed trace area within two sub-study areas of the Amazon region. The achieved sample validation accuracy is over 86%, which substantiates the reliability of the data. This investigation offers a fresh perspective on monitoring forest changes and observing forest disturbances by amalgamating data from diverse sources, enabling the mapping of dynamic forest cover over an extensive time series with high temporal resolution, thereby mitigating data gaps and enhancing the precision of existing products.

Understanding Dynamics of Land Cover Changes Using GIS Technique in a River Catchment with High Altitude Differences – an Analysis from Kosovo

This article analyses Land Cover Changes in the Lumbardhi of Prizreni (LP) River catchment in southern Kosovo. As a landlocked country, Kosovo has highly diverse landform features. LP River catchment is located in Sharri's transboundary high mountain range and the southern part of Dukagjini Plain. In the last two decades, the landscape has experienced a transformation in land cover changes, especially in the increasing of artificial surfaces in the plains. Using GIS techniques, data were calculated for the selected years, and their dynamics were visualised for the last two decades. The analysis shows a decrease in agricultural land and complex cultivation patterns (-806 ha), an increase in artificial surface, especially discontinuous urban fabric (+354 ha), and small changes in forest and seminatural areas, which are primarily included in "Sharri" National Park.

Assessment of forest cover change, key drivers of change and perception of locals in Birendranagar Municipality, Surkhet District, Nepal

Land use and land cover change has an immense impact on the global environment and ecosystem and is mostly connected with human activities such as conversion of forest into agricultural land and settlements. Forests are changing globally in quantity and quality, and in both increasing and decreasing trends. For the Birendra Nagar Municipality, this study evaluated LULC dynamics, the conversion of forest cover from 2000 to 2020, as well as community perceptions on forest cover change, its drivers. Landsat images of 2000 and 2020 were used for LULC dynamics and for forest cover change detection Using software GIS 10.8. Questionnaire survey, key informant survey, field observation and secondary data were acquired from journal articles, reports, District Forest office, books, and various other sources to find out drivers of forest cover change and perception of locals. It was found that 1146 ha of forest area, 280.53 ha of water bodies, 752.62 ha of barren land were lost between 2000 and 2020. However, 1147.74 ha of settlement area and 1032.40 ha of agricultural land was gained.Net forest cover decreased overall by 4.67%, however, some areas within the municipality experienced an increase in forest cover namely Ward 11 and ward 16 of Municipality shows Highest positive and Negative Forest cover change in terms of Area. Expansion of agriculture land and settlements with the encroachment of forest area, haphazard infrastructure development, illegal timber harvesting and low regeneration capacity of land responsible for the reduction of forest, along with Community forestry approach, plantations programs, promotion of agroforestry practices, fuel transition were responsible drivers for improvements of forest cover. Water cycle disruption, Increases Natural hazard, Temperature rises, shortage of wood for fuel, extinction of indigenous species were the most perceived negative impacts of the forest cover reduction whereas income generation Activities, support biodiversity, easy to collect fuel wool and fodder and others including purifying air quality, increasing aesthetic beauty were the most perceived positive impacts of the forest cover improvement in the study area. Most of the locals have a holistic understanding of forest cover change. Strengthening forest protection, improving soil and water conservation structures, awareness creation, enrichment planting, Promote Alternate Energy Resources and Equitable benefit-sharing mechanism are possible solutions perceived by locals.

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.