scholarly journals Impacts of REDD+ in Mexico: Experiences of Two Local Communities in Campeche

2021 ◽  
pp. 1-33
Author(s):  
Jovanka Špirić ◽  
Ana Edith Merlo Reyes ◽  
Ma. Liliana Ávalos Rodríguez ◽  
M. Isabel Ramírez

In 2010, the Mexican National Forestry Commission (Spanish acronym CONAFOR) implemented REDD+ early action activities in priority states, including Campeche. This article explores the impact of the forestry programs promoted under REDD+ on the diversification of household activities, benefit-sharing among local groups, and forest cover changes in two local communities in Campeche. It examines whether the design and implementation of these programs responded to local aspirations for equity and rural development by combining ethnographic and documental methods. In addition, it quantifies land-cover change (2013-2018) using high-resolution imagery and spatial analysis. It found no intracommunity equity or sustainable activity diversification resulting from the REDD+ implementation. Deforestation for livestock and agricultural mechanization was the dominant process observed both in dense and open forests. Although it has not made the situation worse, REDD+ has yet to provide social benefits for these two communities. To be considered a viable option locally, the program design under REDD+ must combine the implementation of several sustainable productive activities over a longer period and provide net monetary benefits to all local groups.

2021 ◽  
Vol 13 (11) ◽  
pp. 2172
Author(s):  
Sarah Carter ◽  
Martin Herold ◽  
Inge Jonckheere ◽  
Andres Espejo ◽  
Carly Green ◽  
...  

Four workshops and a webinar series were organized, with the aim of building capacity in countries to use Earth Observation Remote Sensing data to monitor forest cover changes and measure emissions reductions for REDD+ results-based payments. Webinars and workshops covered a variety of relevant tools and methods. The initiative was collaboratively organised by a number of Global Forest Observations Initiative (GFOI) partner institutions with funding from the World Bank’s Forest Carbon Partnership Facility (FCPF). The collaborative approach with multiple partners proved to be efficient and was able to reach a large audience, particularly in the case of the webinars. However, the impact in terms of use of tools and training of others after the events was higher for the workshops. In addition, engagement with experts was higher from workshop participants. In terms of efficiency, webinars are significantly cheaper to organize. A hybrid approach might be considered for future initiatives; and, this study of the effectiveness of both in-person and online capacity building can guide the development of future initiatives, something that is particularly pertinent in a COVID-19 era.


2021 ◽  
Author(s):  
Arthur Depicker ◽  
Liesbet Jacobs ◽  
Nicholus Mboga ◽  
Benoît Smets ◽  
Anton Van Rompaey ◽  
...  

<p>On the nexus of humans and their environment, landslide risk is in essence dynamic. In mountainous areas over the world, the need for agricultural land incites people to settle on steeper (more landslide-prone) terrain at the expense of ecosystems. At the same time, the degradation of ecosystems, for example through deforestation, leads to a considerable increase in landslide hazard. Although the link between deforestation and landslide hazard/risk has been widely recognized, it remains poorly quantified. This is especially the case in the Global South where historical land cover and landslide records are scarce.  </p><p>In this study, we investigate 58 years of forest cover changes, population dynamics, and landslide risk in the Kivu Rift. This mountainous region presents similar geomorphic and climatic conditions across three countries: Burundi, the eastern part of the Democratic Republic of the Congo (DRC), and Rwanda. First, we use contemporary landslide and deforestation data (2000-2016) to explicitly quantify the interactions between these two processes. Second, we reconstruct the annual forest cover changes between 1958 and 2016 by means of a cellular automaton of which the output converges to four forest cover products (1958, 1988, 2001, 2016). We derive the 1958 forest data from an inventory of nearly 2,400 panchromatic aerial photographs, available at the Royal Museum for Central Africa. The forest data for 1988, 2001, and 2016 are readily available and derived from satellite imagery. Next, we estimate the yearly historical landslide hazard dynamics by applying the contemporary deforestation-landslide relationship to the historical forest cover changes. Finally, an approximation of the landslide risk (expected fatalities per 100,000 inhabitants), is calculated for four epochs (1975, 1990, 2000, 2015) and derived from the product of the corresponding hazard map and population density grids.</p><p>During our entire period of observation, the landslide risk is higher in the DRC than in Rwanda and Burundi. While the risk in Rwanda and Burundi displays a slightly decreasing trend, the risk seems more volatile in the DRC. Here, the initial risk in 1975 is high due to the concentration of a small population along the steep northwestern coast of Lake Kivu. In the following 15 years, the risk in the DRC decreases sharply, only to soar again in the nineties. This sudden increase in risk can be linked to two factors: demographic changes and environmental degradation. During the nineties, the location of the Congolese people shifted towards steeper terrain. This shift is explained by the relocation of hundreds of thousands of Rwandan refugees and internally displaced people following the First and Second Congo War, but also by the economic opportunities provided by the booming, often informal, mining industry. Deforestation has also contributed to the higher landslide risk in the DRC, as large parts of the primary forest have been cut to satisfy the land and fuelwood demand of the fast-growing population.</p><p>With our analysis, we demonstrate that a landslide risk assessment is more than the reflection of the current environmental conditions. The legacy of environmental and societal dynamics resonates in contemporary landslide risk.</p>


2017 ◽  
Vol 5 (1) ◽  
pp. 117
Author(s):  
Anisa Awalul Khoiriah ◽  
Samsul Bakri ◽  
Trio Santoso

In each region that are transitioning from an agricultural economic activity to industrialeconomic activity has always faced deforestation or forest cover changes in LampungProvince. Changes in forest cover can affect the value of the Human Development Index(HDI). One of the cause of forest decline is a result of poverty sourced by low access to landresources. Furthermore, revenue in the sectors of the economy if hypothesized was alsoinfluenced by changes in the value of the HDI. This study aims to determine the impact ofchanges in forest cover and land, poverty, and income in the economic sectors of the IPM. This study was conducted in September 2015-January 2016, which consists of laboratoryactivity, namely the determination of land cover change in 2002, 2009, and 2013 throughprocessing of satellite imagery data and then continued with field checks. Data of the poverty,income in the economic sectors and IPM obtained through the acquisition of data from theBPS. Multiple linear regression modelling applied to the response variable HDI withexplanatory variables such as changes in forest cover and human welfare indicators.Optimization parameters used Minitab 11. The conclusions of the results of the regressionmodelling showed that the proportion of state forests(p value = 0,037), community forests(pvalue =0,009), fields(p value = 0,040), open land(p value = 0,307), poverty kemiskinan (pvalue = 0,595), disadvantaged families(p value = 0,034)and economic growth(p value =0,146)could decrease the performance of HDI next two years. While the land up(p value =0,675), GDP in the transport sector(p value = 0,002), communications(p value = 0,071),services sector(p value = 0,067)and in others(p value = 0,066)can markedly increase thenumber IPM in Lampung province.Keywords: Forest cover, HDI, Poverty, Sector of Economy.


2021 ◽  
Author(s):  
Rahayu Adzhar ◽  
Douglas I. Kelley ◽  
Ning Dong ◽  
Mireia Torello Raventos ◽  
Elmar Veenendaal ◽  
...  

Abstract. The Moderate Resolution Imaging Spectroradiometer vegetation continuous fields (MODIS VCF) Earth observation product is widely used to estimate forest cover changes, parameterise vegetation and Earth System models, and as a reference for validation or calibration where field data is limited. However, although limited independent validations of MODIS VCF have shown that MODIS VCF's accuracy decreases when estimating tree cover in sparsely-vegetated areas, such as in tropical savannas, no study has yet assessed the impact this may have on the VCF based tree cover distributions used by many in their research. Using tropical forest and savanna inventory data collected by the TROpical Biomes In Transition (TROBIT) project, we produce a series of corrections that take into account (i) the spatial disparity between the in-situ plot size and the MODIS VCF pixel, and (ii) the trees' spatial distribution within in-situ plots. We then applied our corrections to areas identified as forest or savanna in the International Geosphere-Biosphere Programme (IGBP) land cover mapping product. All IGBP classes identified as savanna show substantial increases in cover after correction, indicating that the most recent version of MODIS VCF consistently underestimates woody cover in tropical savannas. We estimate that MODIS VCF could be underestimating tropical tree cover by between 9–15 %. Models that use VCF as their benchmark could be underestimating the carbon uptake in forest-savanna areas and misrepresenting forest-savanna dynamics. While more detailed in-situ field data is necessary to produce more accurate and reliable corrections, we recommend caution when using MODIS VCF in tropical savannas.


2018 ◽  
Vol 14 (1) ◽  
pp. 51-60
Author(s):  
Emilian DANILA ◽  
VALENTIN Hahuie ◽  
Puiu Lucian GEORGESCU ◽  
Luminița MORARU

2020 ◽  
Vol 12 (19) ◽  
pp. 3226
Author(s):  
Daniel Cunningham ◽  
Paul Cunningham ◽  
Matthew E. Fagan

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.


Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 265
Author(s):  
Mihnea Cățeanu ◽  
Arcadie Ciubotaru

Laser scanning via LiDAR is a powerful technique for collecting data necessary for Digital Terrain Model (DTM) generation, even in densely forested areas. LiDAR observations located at the ground level can be separated from the initial point cloud and used as input for the generation of a Digital Terrain Model (DTM) via interpolation. This paper proposes a quantitative analysis of the accuracy of DTMs (and derived slope maps) obtained from LiDAR data and is focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) and Thin-Plate Spline (TPS). Research was mainly focused on the issue of point data density. To analyze its impact on the quality of ground surface modelling, the density of the filtered data set was artificially lowered (from 0.89 to 0.09 points/m2) by randomly removing point observations in 10% increments. This provides a comprehensive method of evaluating the impact of LiDAR ground point density on DTM accuracy. While the reduction of point density leads to a less accurate DTM in all cases (as expected), the exact pattern varies by algorithm. The accuracy of the LiDAR-derived DTMs is relatively good even when LiDAR sampling density is reduced to 0.40–0.50 points/m2 (50–60 % of the initial point density), as long as a suitable interpolation algorithm is used (as IDW proved to be less resilient to density reductions below approximately 0.60 points/m2). In the case of slope estimation, the pattern is relatively similar, except the difference in accuracy between IDW and the other two algorithms is even more pronounced than in the case of DTM accuracy. Based on this research, we conclude that LiDAR is an adequate method for collecting morphological data necessary for modelling the ground surface, even when the sampling density is significantly reduced.


Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 959
Author(s):  
Benjamin Clark ◽  
Ruth DeFries ◽  
Jagdish Krishnaswamy

As part of its nationally determined contributions as well as national forest policy goals, India plans to boost tree cover to 33% of its land area. Land currently under other uses will require tree-plantations or reforestation to achieve this goal. This paper examines the effects of converting cropland to tree or forest cover in the Central India Highlands (CIH). The paper examines the impact of increased forest cover on groundwater infiltration and recharge, which are essential for sustainable Rabi (winter, non-monsoon) season irrigation and agricultural production. Field measurements of saturated hydraulic conductivity (Kfs) linked to hydrological modeling estimate increased forest cover impact on the CIH hydrology. Kfs tests in 118 sites demonstrate a significant land cover effect, with forest cover having a higher Kfs of 20.2 mm hr−1 than croplands (6.7mm hr−1). The spatial processes in hydrology (SPHY) model simulated forest cover from 2% to 75% and showed that each basin reacts differently, depending on the amount of agriculture under paddy. Paddy agriculture can compensate for low infiltration through increased depression storage, allowing for continuous infiltration and groundwater recharge. Expanding forest cover to 33% in the CIH would reduce groundwater recharge by 7.94 mm (−1%) when converting the average cropland and increase it by 15.38 mm (3%) if reforestation is conducted on non-paddy agriculture. Intermediate forest cover shows however shows potential for increase in net benefits.


Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Adrien Michez ◽  
Stéphane Broset ◽  
Philippe Lejeune

In the context of global biodiversity loss, wildlife population monitoring is a major challenge. Some innovative techniques such as the use of drones—also called unmanned aerial vehicle/system (UAV/UAS)—offer promising opportunities. The potential of UAS-based wildlife census using high-resolution imagery is now well established for terrestrial mammals or birds that can be seen on images. Nevertheless, the ability of UASs to detect non-conspicuous species, such as small birds below the forest canopy, remains an open question. This issue can be solved with bioacoustics for acoustically active species such as bats and birds. In this context, UASs represent an interesting solution that could be deployed on a larger scale, at lower risk for the operator, and over hard-to-reach locations, such as forest canopies or complex topographies, when compared with traditional protocols (fixed location recorders placed or handled by human operators). In this context, this study proposes a methodological framework to assess the potential of UASs in bioacoustic surveys for birds and bats, using low-cost audible and ultrasound recorders mounted on a low-cost quadcopter UAS (DJI Phantom 3 Pro). The proposed methodological workflow can be straightforwardly replicated in other contexts to test the impact of other UAS bioacoustic recording platforms in relation to the targeted species and the specific UAS design. This protocol allows one to evaluate the sensitivity of UAS approaches through the estimate of the effective detection radius for the different species investigated at several flight heights. The results of this study suggest a strong potential for the bioacoustic monitoring of birds but are more contrasted for bat recordings, mainly due to quadcopter noise (i.e., electronic speed controller (ESC) noise) but also, in a certain manner, to the experimental design (use of a directional speaker with limited call intensity). Technical developments, such as the use of a winch to safely extent the distance between the UAS and the recorder during UAS sound recordings or the development of an innovative platform, such as a plane–blimp hybrid UAS, should make it possible to solve these issues.


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