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Research on the Forest Health Monitoring and Evaluation in Rwanda using Deep Learning
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Accurate crop mapping is of great significance for crop yield forecasting, agricultural productivity development and agricultural management. Thanks to its all-time and all-weather capability, integrating multi-temporal synthetic aperture radar (SAR) for crop mapping has become essential and challenging task in remote sensing. In recent years, deep learning (DL) has demonstrated excellent crop mapping accuracy to interpret crop dynamics. However, existing DL-based methods tend to be incapable of capturing spatial and temporal features at different scales simultaneously, and this often leads to severe mis-classification due to the complex and heterogeneous distribution of crops and diverse phenological patterns. In this paper, we propose a novel spatio-temporal multi-level attention method, named as STMA, for crop mapping using time-series SAR imagery in an end-to-end fashion to increase the capability of crop phenology retrieval. Specifically, the multi-level attention mechanism is designed to aggregate multi-scale spatio-temporal representations on crops via cascaded spatio-temporal self-attention (STSA) and multi-scale cross-attention (MCA) modalities. To ensure a fine extraction of multi-granularity features, a learnable spatial attention position encoding is proposed to adaptively generate the position priors to facilitate multi-level attention learning. Experimental results on Brandenburg Sentinel-1 dataset, public PASTIS-R dataset and South Africa dataset demonstrated that STMA can achieve state-of-the-art performance in crop mapping tasks, with the accuracy of 96.54% in the Brandenburg Sentinel-1 dataset, 86.77% in the PASTIS-R dataset and 83.37% in the South Africa dataset, validating its effectiveness and superiority. Further comparison of spatio-temporal generalisation capability reflected its excellent performance in spatio-temporal modelling on different crops and scenarios. This research provides a viable and intelligent spatio-temporal framework for large-area crop mapping using time-series SAR imagery in complex agricultural systems. The Brandenburg Sentinel-1 dataset and the STMA code will be publicly available at https://github.com/hanzhu97702/ISPRS_STMA.
Sustainable Development Goal (SDG) 13 refers to "Climate Action". It is one of the 17 goals established by the United Nations in their 2030 Agenda for Sustainable Development. The primary objective of SDG13 is to take urgent action to combat climate change and its impacts. It recognises that climate change is a global challenge that requires immediate attention and concerted efforts from governments, businesses, communities, and individuals worldwide. SDG13 permeates a number of SDGs and also influences them in a significant way. Based on the need to contextualise SDG13 and considering its role as one of the central SDGs, this article outlines the links between SDG13 and the other SDGs. It also reports on a survey involving experts from 61 countries. The findings suggest that even though climate change impacts, particularly extreme weather events, are known to disproportionally affect poorer and minoritized communities, the synergies among related goals and climate justice seem to receive less attention. The article concludes by describing some of the means via which synergies between SDG13 and other SDGs may be achieved.
Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previous review papers in this domain have been confined to 2020, an up-to-date review to show the progression of research extending into the present phase is lacking. In this review, we explore the recent articles, providing a thorough classification of approaches into three main categories: Convolutional Neural Network (CNN)-based, Vision Transformer (ViT)-based, and Generative Adversarial Network (GAN)-based architectures. Notably, within the CNN-based category, we further refine the classification based on specific methodologies and techniques employed. In addition, a novel and rigorous meta-analysis is performed to synthesize and analyze the findings from 50 peer-reviewed journal articles to provide valuable insights in this domain, surpassing the scope of existing review articles. Our meta-analysis shows that the most adopted remote sensing scene datasets are AID (41 articles) and NWPU-RESISC45 (40). A notable paradigm shift is seen towards the use of transformer-based models (6) starting from 2021. Furthermore, we critically discuss the findings from the review and meta-analysis, identifying challenges and future opportunities for improvement in this domain. Our up-to-date study serves as an invaluable resource for researchers seeking to contribute to this growing area of research.
Classification and analysis of high-resolution satellite images using conventional techniques have been limited. This is due to the complex characteristics of the imagery. These images are characterized by features such as spectral signatures, complex texture and shape, spatial relationships and temporal changes. In this research, we present the performance evaluation and analysis of deep learning approaches based on Convolutional Neural Networks and vision transformer towards achieving efficient classification of remote sensing satellite images. The CNN-based models explored include ResNet, DenseNet, EfficientNet, VGG and InceptionV3. The models were evaluated on three publicly available EuroSAT, UCMerced-LandUse and NWPU-RESISC45 datasets containing categories of images. The models achieve promising results in accuracy, recall, precision and F1-score. This performance demonstrates the feasibility of Deep Learning approaches in learning the complex and in-homogeneous features of the high-resolution remote sensing images.
Remote sensing has developed into an omnipresent technology in the scientific field of forestry and is also increasingly used in an operational fashion. However, the pace and level of uptake of remote sensing technologies into operational forest inventory and monitoring programs varies notably by geographic region. Herein, we highlight some key challenges that remote sensing research can address in the near future to further increase the acceptance, suitability and integration of remotely sensed data into operational forest inventory and monitoring programs. We particularly emphasize three recurrent themes: (1) user uptake, (2) technical challenges of remote sensing related to forest inventories and (3) challenges related to map validation. Our key recommendations concerning these three thematic areas include (1) a need to communicate and learn from success stories in those geographic regions where user uptake was successful due to multi-disciplinary collaborations supported by administrative incentives, (2) a shift from regional case studies towards studies addressing ‘real world’ problems focusing on forest attributes that match the spatial scales and thematic information needs of end users and (3) an increased effort to develop, communicate, and apply best-practices for map and model validation including an effort to inform current and future remote sensing scientists regarding the need for and the functionalities of these best practices. Finally, we present information regarding the use of remote sensing for forest inventory and monitoring, combined with recommendations where possible, and highlighting areas of opportunity for additional investigation.
Forest and vegetation play an important role in balancing ecosystem patterns, providing food security, and blessing the environment for living beings, so the status of global forests and biodiversity, their impact and change overtime with climatic effects and challenges is important. This study’s methods include a review of global forest cover and status; distribution, and assessment; biodiversity, forest carbon assessment; causes of forest loss; and the impacts and implications of CO2 emissions. Forests encompass 31% of the world’s forests, are home to 2 million to 1 trillion species, and provide habitat for 80% of amphibian species, 75% of bird species, 68% of mammalian species, and so on. Deforestation is the major cause of forest loss, with a decrease of 4.7 million ha. From 2010 to 2020, only in the Asia Pacific region and from 2000 to 2010, 13 million ha of world forests were lost. All flora, fauna, and microbes are slowly degrading and disappearing due to human activities such as deforestation, intensive use, inappropriate forest management, agriculture, encroachment of forest land, slash burn practices, forest fires, urbanization, overharvesting, environmental deterioration, etc. Because the globe has emitted over 1.5 trillion tonnes of CO2 since 1751, the persistence of biodiversity in human-modified habitats is crucial for conservation and the provision of ecosystem services.
This research sought to investigate the moderating influence of monitoring and evaluation practices on the relationship between community empowerment and the sustainability of forest conservation projects. This study was conducted in Taita-Taveta County, located in the southern part of Kenya. Community empowerment is viewed as a process and an end where community members gain control of their lives. The empowerment practices considered were knowledge and skills empowerment, decision-making empowerment, conflict resolution empowerment and income-generating empowerment activities. The study relied on a cross-sectional survey study design. A sample of 365 respondents for quantitative data was determined using Yamane formulae. Mixed methods were used to collect and analyse data. Cluster sampling and systematic sampling were used progressively to select respondents for quantitative questions, while those for qualitative questions were selected purposively. Additional data collection methods were observation and document analysis. The study noted that projects implemented in the study area were barely sustainable, with at least 44.1% of respondents agreeing while 52.1% neither agreed nor disagreed. Regression analysis results indicated the R-square change (model 2) of 0.015, which implied that model 2 with monitoring and evaluation practices positively enhanced the influence of community empowerment activities on the sustainability of forest conservation practices by at least 1.5%. Based on the results, the study concluded that monitoring and evaluation practices enhance the sustainability of forest conservation projects with a recommendation that project designers should always include monitoring and evaluation practices in their projects.
This paper examines forest conservation dynamics and the ensuing challenges encountered in Cameroon. It assesses forest resources and identifies forest conservation patterns and the various challenges faced by these initiatives. To attain this objective, a broad review of published literature was performed using Semantic Scholar, Google Scholar, Research Gate, and Web of Science. The study observed that, despite various management practices, enormous challenges still exist which give rise to forest loss at an alarming rate. This is primarily a result of unsustainable agricultural activities, logging, and urbanization (settlement expansion). Similarly, this study also found the underlying causes of forest loss to include inadequate land for farming, limited access to forest resources, lack of alternative sources of livelihood as well as the increasing value of Non-Timber Forest Products. This study concluded that forest management can effectively respond to the threats emerging from forest loss through adequate and proper finance, cooperation, and support from law enforcement and the involvement of local communities in forest management plans.
Rapid urbanization in developing countries often results in uncontrolled urban growth. In order to support sustainable urban development, reliable and up-to-date information on urban land cover changes and their environmental impact is needed. In this study, we aim at evaluating the potential of Sentinel-2 (S-2) Multispectral Instrument (MSI) data for urban land cover change monitoring and for analyzing resulting impacts on Ecosystem Services (ES) provision in Kigali, Rwanda. Land cover classification into eight distinct urban classes (84% overall accuracies, 0.8 Kappa) was performed on data from 2016 and 2021 using a hybrid approach combining Random Forest with a U-Net-based impervious surface segmentation that improved the delineation of urban areas. The bi-temporal land cover maps were then analyzed regarding landscape structure using Landscape Metrics (LM). Ecosystem service bundles were derived for both years and their changes were summarized. Service providing areas were further evaluated in terms of changes in spatial attributes and structure of patches. ES were aggregated into eight bundles and grouped into provisioning, regulating and supporting services. The bundles were further analyzed using a matrix spatially linking landscape units with service supply and demand budgets. The results illustrated that three urban development scenarios can be distinguished including infill through housing and infrastructures development in core urban areas, urban sprawl in fringe zones and the development of urban patches at distant locations intercepted by cropland. The results revealed that the changes in LM negatively affected ES supply mainly through a decrease in cropland and forest. The expansion of built-up areas resulted in a high demand for provisioning and regulating services, especially food and water provision, surface runoff mitigation and erosion control. This is the first study demonstrating that detailed monitoring of urbanization and resulting environmental impacts can be performed with open access S-2 MSI data in Sub-Saharan Africa. Moreover, the framework developed in this study has the potential to be transferred to other Sub-Saharan cities.
Abstract As the need to monitor and evaluate progress on climate change adaptation is increasingly recognized, practitioners may benefit from applying lessons about effective monitoring from the conservation field. This study focuses on monitoring conservation interventions that aim to foster climate change adaptation by assessing: what ways practitioners are adopting best practices from monitoring and evaluation (M&E) in conservation; what practitioners are monitoring in relation to reported outcomes; how monitoring comprehensiveness varies in practice and what factors enable more comprehensive monitoring; and practitioner views on what could improve M&E of adaptation actions. We conducted this study using a portfolio of 76 adaptation projects implemented across the United States and employed a mixed‐methods design that included document analysis, an online survey, and semi‐structured interviews. The majority (84%) of projects reported social outcomes at project completion in addition to ecological outcomes (100%), but monitoring plans focused primarily on ecological and biophysical changes. Only 21% of projects connected monitoring metrics to a theory of change linking actions to expected outcomes. Involvement of an external research partner was identified as a key factor in supporting more comprehensive monitoring efforts. Results provide applied insights for enhancing delivery of social and ecological outcomes from adaptation projects, and suggest research pathways to improve monitoring and effectiveness of climate‐informed conservation.
Forests are known to have beneficial effects on the environment, influence the climate, water sources, and temperature. Forests help in conserving biodiversity, reducing harmful effects caused by chemicals from the soil, acting as a carbon sink, and protecting watersheds, all of these have great benefits to both humans and the environment. The ecosystem of the forests and the environmental sustainability is at risk as a result of deforestation. Deforestation also hampers the socioeconomic development in many parts of the world. Through secondary research and a descriptive research design, this paper analyzes the impacts of deforestation on the Environment and Agriculture in Kenya. According to research, agricultural activities are the major cause of deforestation globally, it accounts for about 80% of total deforestation in developing countries. Other causes of deforestation are human population growth, poor governance and corruption, illegal logging, and wildfires. Deforestation affects the environment through climate change, water loss, decreased biodiversity, habitat loss, and conflicts. Several conservation measures have been tried in various places to slow down the rate of deforestation. These include; imposing bans, legislations, devolution, public participation, barriers, and modernizing forest management. It is therefore recommended that; people need be educated about the benefits of forests and their adverse impacts that may result from deforestation, population control to avoid invasion in forested areas, adoption of sustainable and environmental-friendly agricultural practices, strengthening institutions such as legislature and judiciary to curb corruption, reduction of the continued widespread dependence of household biomass sources of energy ad adoption of Geographic Information System (GIS) techniques to monitor activities in and around forests.
This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800–1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results.
Land use change and deforestation can influence local temperature and climate. Here we use a coupled ocean-atmosphere model to assess the impact of savannization of the Amazon Basin on the wet-bulb globe temperature heat stress index under two climate change scenarios (RCP4.5 and RCP8.5). We find that heat stress exposure due to deforestation was comparable to the effect of climate change under RCP8.5. Our findings suggest that heat stress index could exceed the human adaptation limit by 2100 under the combined effects of Amazon savannization and climate change. Moreover, we find that risk of heat stress exposure was highest in Northern Brazil and among the most socially vulnerable. We suggest that by 2100, savannization of the Amazon will lead to more than 11 million people will be exposed heat stress that poses an extreme risk to human health under a high emission scenario. Complete savannization of the Amazon Basin would enhance the effects of climate change on local heat exposure and pose a risk to human health, according to climate model projections.
Many ecosystems and the biodiversity they contain are currently faced with increasing disturbance frequency and intensity, concurrent with converging stress agents such as climate change, invasive species, and pollutant loads. Research has shown that the earlier decline can be detected, the more successful efforts will be in sustaining critical natural resources. While historically remote sensing (RS) has been successfully used to assess and monitor vegetation condition on a relative, coarse scale, advances in RS technologies and new modeling approaches now enable the identification and tracking of early and more subtle changes in vegetation condition, function, and structure. Here we review the current techniques used to assess and monitor forest ecosystem condition and disturbance and outline a general approach for earlier, more detailed, and accurate decline assessment. We also discuss the importance of engaging land managers, practitioners, and decision-makers in these efforts to ensure that the final products developed can be utilized by stakeholders to maximize the impact of these technologies moving forward.
The ever increasing demand of farmland, fuel wood and charcoal production coupled with population growth has accelerated the rate of forest reduction in Ethiopia. This has resulted in soil erosion and land degradation. This study was conducted at Arba Minch Zuria Woreda Gamo Gofa Zone, Southern Nations and Nationalities Peoples' Region (SNNPR), with the aim of assessing impacts of deforestation on the livelihood of small holder farmers. Both qualitative and quantitative data were collected from primary and secondary data sources. Data on the socio-demographic variables as well as farmers' perception towards impacts of deforestation, were collected using structured questionnaire from 180 randomly selected households from each study sites. The overall average age of respondents was 46.29, where the highest age was recorded in Mille site as compared to other sites (P<0.001). The average household (HH) size of each site was 6.62, 5.52 and 4.82, respectively (P<0.001). About 89.4% of the respondents from all study sites indicated the existence of unwise utilization of forest resources which led to environmental degradation of the area. The result indicated that 27.2% of the forest resources are common woodland, while 72.8% was open access. All the respondents (100%, N= 180) across the study sites reported that the increased trends of degradation of forest resources. Generally, the results of the study indicated that the effects of deforestation on land productivity; agriculture and livestock production had a negative effect on livelihoods of the community. Based on the present study, it is appropriate to undertake the large-scale natural resource management by incorporating community-based watershed management, thereby reducing the adverse impact of deforestation.
Forests are one the main natural factors that regulate and determine climate, weather patterns and amount of CO2 of an area. With rapid industrialization and rapid urbanization there is a significant increase in deforestation and as a consequence rise in global mean surface temperatures. Rapid and unchecked cut down of forest cover has resulted in some of the worst disasters during the last decades. This paper focuses on studying the role of deforestation, its influence on climate change phenomena and its consequences in Pakistan.
The Mediterranean area is particularly affected by adverse consequences of climate variability and change coupled with existing socio-economic processes associated with growing bio-geographical vulnerability and
exposure in the coastal areas of the region. As a result, Mediterranean coastal communities and assets are increasingly at risk. Relevant authorities are encouraged to undertake adaptation measures that are in line with the ICZM (Integrated Coastal Zones Management) Protocol of the Convention for the Protection of the Mediterranean Sea Against Pollution (Barcelona Convention) and national ICZM strategies. In this view, UNEP/Mediterranean Action Plan is developing a Regional Climate Change Adaptation Framework aimed at providing enhanced regional coordination. In synergy with a revised Mediterranean Strategy on Sustainable
Development, the Regional Framework will assist stakeholders and policymakers at all levels across the Mediterranean to take action in order to increase the resilience of the coastal natural and socio-economic systems to the impacts of climate change. In addition, robust scientific methods are needed to assess coastal
vulnerability and risks to climate variability and understanding the interaction of climate change with socio-economic and environmental systems is of increasing importance for coastal policy makers in the Mediterranean. As part of the ClimVar & ICZM Project “Integration of climatic variability and
change into national strategies to implement the ICZM protocol in the Mediterranean”, the present study is designed to contribute to strengthen the regional knowledge concerning the impacts of climate variability and change on coastal ecosystems and local communities in the Mediterranean. This study developed and implemented an integrated methodology to assess risk and vulnerability to physical and socio-economic impacts of climate variability and change in the Mediterranean with the aim of identifying coastal hot-spots. The proposed methodology has the potential to assist the involved countries to better assess climate-related risks to their marine and coastal zones by facilitating the analysis of the physical impacts of climate variability
and change and the consequences on socio-economic sectors and ecosystems, as well as the identification of strategic adaptation options in the coastal zones of the Mediterranean.
Deforestation in forest reserves which is mainly driven by socio-economic factors is considered as one of the key issues of the sustainable use and management of biodiversity. This paper analyses socio-economic factors influencing deforestation in the Bereku forest reserve in Tanzania. Data were collected through questionnaires, interviews, focus group discussions and field observations. Descriptive and inferential methods were used to analyse and explain quantitative data while content analysis was used to analyse qualitative data. The results revealed that livelihood activities, period of residence close to forest and distance from homestead to the forest significantly influence deforestation in the study area. In addition, farm land size, household size, education, awareness of the management of the reserve and its boundaries insignificantly influence deforestation. It was also revealed that the reserve fringe communities are aware of the negative consequences of deforestation and forest degradation. The paper concludes that the causes of deforestation in the Reserve are deeply rooted in the daily livelihood needs of communities and the growing population. Forestry education and extension services should be directed at institutional strengthening catalyzing local communities to actively participate in decision making processes aimed at conserving the forest and improving the livelihoods of rural communities.
For Africa's developing countries the agricultural system is among the most vulnerable due to extensive use of rainfed crop production, presence of droughts and floods that affect crops as well as initial poverty of population that limits the capacity to adapt. In this study were realized the analysis of long-term rainfall data and its impact on main crop products in Rwanda. Some rainfall data was infilled for the period of 1926-2013. It was done using the monitoring data of a neighbor weather station with relatively the same elevation above sea level and with a monitoring record of no less than 40 years. The neighboring station with the best correlation was selected for the infilling. The missing rainfall data was infilled for all the stations with resulting regression coefficients ranging from 0.55 to 0.80. This indicates the acceptability of the performed regression. Also were constructed different-cumulative curves of rainfall and sort out cycles of decline and increment of rainfall. Similar different-cumulative curves were constructed for main crops in Rwanda. Correlation and regression analysis were used to determine the relationship between rainfall, arable land expansion, fertilizer use and crop yield. Particularly for Rwandan conditions, the rainfall variations are determinant for the crop yield increment. The intensification of extreme flood's and, as rule, flooding of agricultural lands in connection with rainfall augmentation was also allocated.
The classification of an annual time series by using data from past years is investigated in this letter. Several classification schemes based on data fusion, sparse learning, and semisupervised learning are proposed to address the problem. Numerical experiments are performed on a Moderate Resolution Imaging Spectroradiometer image time series and show that while several approaches have statistically equivalent performances, a support vector machine with I1 regularization leads to a better interpretation of the results due to their inherent sparsity in the temporal domain.
This paper examines the effects of deforestation and its associated consequences as a result of demographic pressure in Rwanda. It came out that demographic pressure led to forest clearance and biodiversity disturbances in general, thereby leading to climate change. Using over 30 and 80 years of climatic variables and demographic data respectively, the analysis of the output of ORIGIN PRO 8.0 showed that the population has increased 4 times in the period of 80 years and this in turn has affected the environment. The forest ecosystems, which occupied 30% of the total surface of the country in 1930 has been reduced to 8.9% as by the year 2000. This severe loss of forests has remarkably led to more than 90% decline of fauna species in Akagera National Park. The analysis of deforestation has revealed some associated consequences on regulating services where a monthly increase of 0.5°C and a monthly decrease of 10 mm in rainfall has been remarked in the study period of 1970 to 2010. Finally, this study suggests the State to put in place tight mechanisms to control the population growth for the attainment of sustainable environmental development and reinforce management of ecosystems so as to inherit an enjoyable and productive environment to the future generations.
This study promotes the idea of future-oriented thinking in disaster risk management and describes how forward-looking activities can play a role in reduction of future risks and impacts. It explicitly highlights how a common future-oriented proactive attitude built-up in participative stakeholder communication networks can assist in shaping and creating a vision of a less uncertain and ambiguous future. First, the advancement of the disaster management concept and its various stages, including risk analysis, mitigation and response are explained. Forward-looking activities on short- and long-term temporal scales are introduced and put into a disaster risk management context.
Recent developments in risk and crisis communication are discussed as evolving in a new environment that emphasizes network-centric communication shaped by the increasing influence of web 2.0 social media platforms. It is further described how future-oriented processes and inherent communication approaches can improve disaster risk management. Finally, the various time-scale-dependent future-oriented activities are conceptually integrated into a framework of risk governance. Active and participative communication is thereby seen as the key for successfully implementing risk governance and favor disaster mitigation and future risk reduction.
The largest known economic impact of climate change is upon agriculture because of the size and sensitivity of the sector. Warming causes the greatest harm to agriculture in developing countries primarily because many farms in the low latitudes already endure climates that are too hot. This paper reviews several studies that measure the size of the impact of warming on farms in developing countries. Even though adaptation will blunt some of the worst predicted outcomes, warming is expected to cause large damages to agriculture in developing countries over the next century.
The long term contribution of forests to the livelihoods of the rural poor had been long appreciated. More than half of Africa's fast-growing population rely directly and indirectly on forests for their livelihoods. As the continent faces stresses from poverty and economic development, another major uncertainty is looming that could alter many of the relationships between people and forests. This uncertainty is climate change. Climate impacts such as changes in temperature and rainfall patterns resulting in drought, flooding, all exert significant effect on forest ecosystems and their provision of goods and services, which form the safety nets for many African rural poor. Building adaptation strategies becomes an option for forest-dependent households and communities, and even countries whose economies largely depend on the related sectors. The review details cases of impacts, underlying causes of vulnerability, and identified coping and adaptation strategies, as reported in their National Communications by many African countries to the United Nations Framework Convention for Climate Change.
The Forest Health Monitoring Program in the United States is a science-based, comprehensive monitoring system that provides statistically precise and accurate baseline and forest health trend information to determine detrimental changes or improvements that occur in our forests over time. This program, initiated in 1990 to provide information on forest health and sustainability, consists of four separate, interrelated activities, including detection monitoring, evaluation monitoring, research on monitoring techniques, and intensive site monitoring. Descriptions and examples are provided for each activity.
Changes in climate during the 20th century differ from region to region across the United States. We provide strong evidence that spatial variations in US temperature trends are linked to the hydrologic cycle, and we also present unique information on the seasonal and latitudinal structure of the linkage. We show that there is a statistically significant inverse relationship between trends in daily temperature and average daily precipitation across regions. This linkage is most pronounced in the southern United States (30-40 degrees N) during the May-June time period and, to a lesser extent, in the northern United States (40-50 degrees N) during the July-August time period. It is strongest in trends in maximum temperatures (T(max)) and 90th percentile exceedance trends (90PET), and less pronounced in the T(max) 10PET and the corresponding T(min) statistics, and it is robust to changes in analysis period. Although previous studies suggest that areas of increased precipitation may have reduced trends in temperature compared with drier regions, a change in sign from positive to negative trends suggests some additional cause. We show that trends in precipitation may account for some, but not likely all, of the cause point to evidence that shows that dynamical patterns (El Niño/Southern Oscillation, North Atlantic Oscillation, etc.) cannot account for the observed effects during May-June. We speculate that changing aerosols, perhaps related to vegetation changes, and increased strength of the aerosol direct and indirect effect may play a role in the observed linkages between these indices of temperature change and the hydrologic cycle.
Climate change refers to the long-term changes in temperature and weather due to human activities. Increase in average global temperature and extreme and unpredictable weather are the most common manifestations of climate change. In recent years, it has acquired the importance of global emergency and affecting not only the wellbeing of humans but also the sustainability of other lifeforms. Enormous increase in the emission of greenhouse gases (CO2, methane and nitrous oxide) in recent decades largely due to burning of coal and fossil fuels, and deforestation are the main drivers of climate change. Marked increase in the frequency and intensity of natural disasters, rise in sea level, decrease in crop productivity and loss of biodiversity are the main consequences of climate change. Obvious mitigation measures include significant reduction in the emission of greenhouse gases and increase in the forest cover of the landmass. Conference of Parties (COP 21), held in Paris in 2015 adapted, as a legally binding treaty, to limit global warming to well below 2 °C, preferably to 1.5 °C by 2100, compared to pre-industrial levels. However, under the present emission scenario, the world is heading for a 3–4 °C warming by the end of the century. This was discussed further in COP 26 held in Glasgow in November 2021; many countries pledged to reach net zero carbon emission by 2050 and to end deforestation, essential requirements to keep 1.5 °C target. However, even with implementation of these pledges, the rise is expected to be around 2.4 °C. Additional measures are urgently needed to realize the goal of limiting temperature rise to 1.5 °C and to sustain biodiversity and human welfare.
Environmental changes are especially pronounced in Sub-Saharan Africa (SSA). Land degradation is nowadays a major concern for 32 countries in Africa, and over 300 million people in SSA face water scarcity (UNEP, 2008). To what extent are environmental factors likely to trigger migration in SSA? To shed some light on the question, this paper provides the latest figures and information. The evidence from different branches of the literature - environmental sciences, migration research as well as development economics - is analysed. A focus on the four countries: Ghana, Mozambique, Niger, and Senegal, offers more specific perspectives from different regions in SSA.
This paper describes regional forestry and forestry-related policies of the Sub-Saharan Region of Africa, with a view to reveal and discuss their influence on both regionional cooperation, national programmes and the participation of women, children and the vulnerable in forestry practice. All Regional Economic Commissions (RECs) of Sub-Saharan Africa have regional forestry policies which are reflected in Forest Convergence Plans (Central and West Africa), special commissions such as COMIFAC of Central Africa, policies and protocols (East and Southern Africa). The Great Green Wall of the Sahara and Sahel Initiative (GGWSSI) is also relevant. Despite promising developments in Central Africa, through programmes promoted under COMIFAC and those promoted in southern Africa by SADC, the influence of regional policies on the development of national actions still remains weak and should be strengthened by increased national actions. Linking forestry to climate change adaption and mitigation in addition to traditional economic activities associated efforts would seem to offer new opportunities. Recommendations on how to improve the influence of these policies and promote the gainful participation of women, children and the vulnerable in forestry are suggested. Spanish Este artículo describe las políticas forestales regionales y otras relacionadas con el sector forestal de la región subsahariana de África, con el fin de dar a conocer y debatir su influencia tanto en la cooperación regional, los programas nacionales y la participación de las mujeres, los niños y las personas vulnerables en la práctica forestal. Todas las Comisiones Económicas Regionales (CER) del África subsahariana tienen políticas forestales regionales que se reflejan en los Planes de Convergencia Forestales (África Central y Occidental), las comisiones especiales, como la COMIFAC de África Central, y las políticas y protocolos (África Oriental y del Sur). La Iniciativa de la Gran Muralla Verde para el Sáhara y el Sahel (GMVSS) es también relevante. A pesar de avances prometedores en el África Central, gracias a los programas promovidos por la COMIFAC y los promovidos en el sur de África por la Comunidad de África Meridional para el Desarrollo (SADC), la influencia de las políticas regionales en el desarrollo de acciones a nivel nacional sigue siendo débil y se debe fortalecer con el aumento de acciones nacionales. La vinculación de la silvicultura a la adaptación al cambio climático y su mitigación, además de las labores asociadas a las actividades económicas tradicionales parece ofrecer nuevas oportunidades. Se sugieren recomendaciones sobre cómo mejorar la influencia de estas políticas y promover la participación beneficiosa de las mujeres, los niños y las personas vulnerables en el sector forestal. French Ce papier décrit les politiques de foresterie régionale et celles liées à la foresterie dans la région subsaharienne de l'Afrique, et vise à révéler et examiner leur influence sur la coopération régionale, les programmes nationaux et la participation des femmes, des enfants et des personnes vulnérables dans la pratique de la foresterie. Toutes les Commissions économiques régionales (RECs) de l'Afrique subsaharienne ont des politiques de foresterie régionales qui sont reflétées dans les Plans de convergence forestiers (Afrique centrale et de l'ouest), dans des commissions spéciales, telles que le COMIFAC de l'Afrique centrale, des politiques et des protocoles (Afrique de l'est et du sud). L'initiative du Grand mur vert du Sahara et du Sahel (GGWSSI) est également pertinente. Malgré des développements prometteurs en Afrique centrale, au travers des programmes promus sous l'égide du COMIFAC et ceux promus dans le sud du continent par le SADC, l'influence des politiques régionales sur le développement d'actions au niveau national demeure faible et devrait être renforcée par un accroissement des actions nationales. Il semblerait qu'une association de la foresterie à l'adaptation au changement climatique et à son atténuation, ajoutée aux efforts traditionnels d'activités économiques y étant associés, pourrait offrir de nouvelles opportunités. Des recommandations pour améliorer l'influence de ces politiques et promouvoir la participation rémunératrice en foresterie des femmes, des enfants et des personnes vulnérables sont suggérées.
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