Re-Vegetation of Degraded Hillsides Through Household Tree Planting in Northern Highlands of Ethiopia

2012 ◽  
Vol 2 (3) ◽  
pp. 183-186
Author(s):  
Hailemariam Meaza Gebregergs ◽  
◽  
Girmay Gebresamuel Abraha
BioScience ◽  
2020 ◽  
Author(s):  
Forrest Fleischman ◽  
Shishir Basant ◽  
Ashwini Chhatre ◽  
Eric A Coleman ◽  
Harry W Fischer ◽  
...  

2021 ◽  
pp. 1-24
Author(s):  
Chad F. Hammer ◽  
John S. Gunn

Abstract Non-native invasive plant species are a major cause of ecosystem degradation and impairment of ecosystem service benefits in the United States. Forested riparian areas provide many ecosystem service benefits and are vital to maintaining water quality of streams and rivers. These systems are also vulnerable to natural disturbances and invasion by non-native plants. We assessed whether planting native trees on disturbed riparian sites may increase biotic resistance to invasive plant establishment in central Vermont in the northeastern United States. The density (stems/m2) of invasive stems was higher in non-planted sites (x̄=4.1 stems/m2) compared to planted sites (x̄=1.3 stems/m2). More than 90% of the invasive plants were Japanese knotweed (Fallopia japonica). There were no significant differences in total stem density of native vegetation between planted and non-planted sites. Other measured response variables such as native tree regeneration, species diversity, soil properties and soil function showed no significant differences or trends in the paired riparian study sites. The results of this case study indicate that tree planting in disturbed riparian forest areas may assist conservation efforts by minimizing the risk of invasive plant colonization.


Data in Brief ◽  
2021 ◽  
pp. 107073
Author(s):  
Christine Magaju ◽  
Leigh Ann Winowiecki ◽  
Pietro Bartolini ◽  
Asma Jeitani ◽  
Ibrahim Ochenje ◽  
...  

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 658
Author(s):  
Pratiwi ◽  
Budi H. Narendra ◽  
Chairil A. Siregar ◽  
Maman Turjaman ◽  
Asep Hidayat ◽  
...  

Tropical forests are among the most diverse ecosystems in the world, completed by huge biodiversity. An expansion in natural resource extraction through open-pit mining activities leads to increasing land and tropical forest degradation. Proper science-based practices are needed as an effort to reclaim their function. This paper summarizes the existing practice of coal mining, covering the regulatory aspects and their reclamation obligations, the practices of coal mining from various sites with different land characteristics, and the reclamation efforts of the post-mining landscapes in Indonesia. The regulations issued accommodate the difference between mining land inside the forest area and outside the forest area, especially in the aspect of the permit authority and in evaluating the success rate of reclamation. In coal-mining practices, this paper describes starting from land clearing activities and followed by storing soil layers and overburden materials. In this step, proper handling of potentially acid-forming materials is crucial to prevent acid mine drainage. At the reclamation stage, this paper sequentially presents research results and the field applications in rearranging the overburden and soil materials, controlling acid mine drainage and erosion, and managing the drainage system, settling ponds, and pit lakes. Many efforts to reclaim post-coal-mining lands and their success rate have been reported and highlighted. Several success stories describe that post-coal-mining lands can be returned to forests that provide ecosystem services and goods. A set of science-based best management practices for post-coal-mine reforestation is needed to develop to promote the success of forest reclamation and restoration in post-coal-mining lands through the planting of high-value hardwood trees, increasing trees’ survival rates and growth, and accelerating the establishment of forest habitat through the application of proper tree planting technique. The monitoring and evaluation aspect is also crucial, as corrective action may be taken considering the different success rates for different site characteristics.


2021 ◽  
Vol 124 ◽  
pp. 102387
Author(s):  
Geoffrey H. Donovan ◽  
Jeffrey P. Prestemon ◽  
David T. Butry ◽  
Abigail R. Kaminski ◽  
Vicente J. Monleon
Keyword(s):  

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.


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