Evaluating the trade-off between food and timber resulting from the conversion of Miombo forests to agricultural land in Angola using multi-temporal Landsat data

Disentangling the numbers behind agriculture-driven tropical deforestation

Tropical deforestation continues at alarming rates with profound impacts on ecosystems, climate, and livelihoods, prompting renewed commitments to halt its continuation. Although it is well established that agriculture is a dominant driver of deforestation, rates and mechanisms remain disputed and often lack a clear evidence base. We synthesize the best available pantropical evidence to provide clarity on how agriculture drives deforestation. Although most (90 to 99%) deforestation across the tropics 2011 to 2015 was driven by agriculture, only 45 to 65% of deforested land became productive agriculture within a few years. Therefore, ending deforestation likely requires combining measures to create deforestation-free supply chains with landscape governance interventions. We highlight key remaining evidence gaps including deforestation trends, commodity-specific land-use dynamics, and data from tropical dry forests and forests across Africa.

Place-Based Analysis of Satellite Time Series Shows Opposing Land Change Patterns in the Copperbelt Region of Zambia

The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion.

Assessing the conservation of Miombo timber species through an integrated index of anthropogenic and climatic threats

AIM

Angolan Miombo woodlands, rich in timber species of the Leguminosae family, go through one of the highest rates of deforestation in sub-Saharan Africa. This study presents, on the basis of updated information of the distribution of Leguminosae timber species native to Angola, an integrated index framing the main threats for trees, which aims to support new conservation measures.

LOCATION

Sub-Saharan Africa, Republic of Angola.

METHODS

The current distribution areas of six Leguminosae timber species (i.e., Afzelia quanzensis, Brachystegia spiciformis, Guibourtia coleosperma, Isoberlinia angolensis, Julbernardia paniculata, and Pterocarpus angolensis) were predicted through ensemble modeling techniques. The level of threat to each species was analyzed, comparing the species potential distribution with a threat index map and with the protected areas. The threat index of anthropogenic and climatic factors encompasses the effects of population density, agriculture, proximity to roads, loss of tree cover, overexploitation, trends in wildfires, and predicted changes in temperature and precipitation.

RESULTS

Our results revealed that about 0.5% of Angola's area is classified as of "Very high" threat, 23.9% as "High" threat, and 66.5% as "Moderate" threat. Three of the studied species require special conservation efforts, namely B. spiciformis and I. angolensis, which have a large fraction of predicted distribution in areas of high threat, and G. coleosperma since it has a restricted distribution area and is one of the most valuable species in international markets. The priority areas for the conservation of Leguminosae timber species were found in Benguela and Huíla.

MAIN CONCLUSIONS

This study provides updated data that should be applied to inform policymakers, contributing to national conservation planning and protection of native flora in Angola. Moreover, it presents a methodological approach for the predictions of species distribution and for the creation of a threat index map that can be applied in other poorly surveyed tropical regions.

Ansorge’s cusimanse in Angola: 100 years apart, new records contribute to the species known range

Ansorge’s cusimanse, Crossarchus ansorgei Thomas, 1910, has until recently been known in Angola only from a single specimen collected in 1908, the holotype. During a camera trap survey conducted in Quiçama National Park (Angola) in 2017, we recorded the presence of the species 115 km south-west of the type locality – 40 km from the edge of the current known distribution range, – extending it south of the Cuanza river and possibly following the woodlands along the Angolan Escarpment. We combine our records of Ansorge’s cusimanse with the available published and unpublished records from Angola and compare with other vertebrate taxa that follow a similar pattern, in which Central African species extend their southern distribution into Angola, through the more forested areas in northwestern Angola and further south along the Escarpment. Furthermore, we discuss the urgent need for more research on this species and the impact bushmeat harvesting may have on its conservation.

Spatial and Temporal Trends of Burnt Area in Angola: Implications for Natural Vegetation and Protected Area Management

Fire is a key driver of natural ecosystems in Africa. However, human activity and climate change have altered fire frequency and severity, with negative consequences for biodiversity conservation. Angola ranks among the countries with the highest fire activity in sub-Saharan Africa. In this study, we investigated the spatial and temporal trends of the annual burnt area in Angola, from 2001 to 2019, and their association with terrestrial ecoregions, land cover, and protected areas. Based on satellite imagery, we analyzed the presence of significant trends in burnt area, applying the contextual Mann–Kendall test and the Theil–Sen slope estimator. Data on burnt areas were obtained from the moderate-resolution imaging spectroradiometer (MODIS) burnt area product and the analyses were processed in TerrSet. Our results showed that ca. 30% of the country’s area burned every year. The highest percentage of annual burnt area was found in northeast and southeast Angola, which showed large clusters of decreasing trends of burnt area. The clusters of increasing trends were found mainly in central Angola, associated with savannas and grasslands of Angolan Miombo woodlands. The protected areas of Cameia, Luengue-Luiana, and Mavinga exhibited large areas of decreasing trends of burnt area. Conversely, 23% of the Bicuar National Park was included in clusters of increasing trends. Distinct patterns of land cover were found in areas of significant trends, where the clusters of increasing trends showed a higher fraction of forest cover (80%) than the clusters of decreasing trends (55%). The documentation of burnt area trends was very important in tropical regions, since it helped define conservation priorities and management strategies, allowing more effective management of forests and fires in countries with few human and financial resources.

Long-term land cover change in Zambia: An assessment of driving factors

Land cover change (LCC) has significant effects on the global ecosystem diversity and function. This topic has received increasing attention due, in part, to its relationship with climate change, and the availability of remotely-sensed imagery that is used to monitor LCC. However, studies analysing the factors that drive LCC at large spatial scales and over long temporal scales are uncommon. This study aimed to identify the factors driving long-term (44 years, 1972-2016) national level LCC in Zambia. Two analyses were conducted, with the first considering factors that led to any LCC. The second scenario identified factors associated with changes from forests to other land covers, and the reversion to forests from non-forested covers. Candidate factors considered in both analyses include accessibility, proximity, topography, climate, conservation and socioeconomics. A classification tree (CT) approach was used to relate the explanatory candidate factors to LCC. The results showed that the CT models predicted LCC with accuracies ranging from 71 to 85%. The first analysis showed that the major factors determining LCC were percentage of area under agriculture, distance to water bodies, change in crop yield, mean temperature and elevation. Meanwhile, the second analysis showed that primary, secondary and plantation forest cover losses were mainly influenced by human population density, crop yield per hectare and mean crop yield, respectively. Protection status was the most important factor for forest reversion and recovery, while a variety of factors including distance to the railway, elevation and total precipitation also influenced forest reversion and recovery. The findings from this study provide insights into the factors that influence LCC and are important for developing effective policies to reduce the negative impacts of LCC and to promote forest reversion and recovery through effective management of protected areas. While this study focused on factors associated with historical LCC, the findings will also help to predict and understand future LCC scenarios.

Effects and relationships of grazing intensity on multiple ecosystem services in the Inner Mongolian steppe

Grassland ecosystems are one of the most important terrestrial ecosystems in the world, producing essential both goods and ecosystem services (ES) for human beings. The Inner Mongolian steppe is a major grassland ecosystem in Northern China, covering 13.5% of the northern Chinese grassland area, and playing important ecological roles for the adjacent region of the capital Beijing-Tianjin-Hebei. Quantification of grassland ES under the different utilization patterns is vital for the maintenance of multiple ES and mitigation against ES loss in this region. We made a manipulative experiment with four grazing intensities (grazing exclusion, GE; light grazing intensity, LG; medium grazing intensity, MG; heavy grazing intensity, HG). We then quantified the intensities of eight different grassland ES (1. herbage intake, HT; 2. biodiversity conservation, BI; 3. soil nutrient retention, SN; 4 soil carbon stocks SC; 5. soil erosion prevention, SEP; 6. soil water storage, SWC; 7. potential nutrient recycling, PNC; 8. carbon sequestration from atmosphere. CS) and total ES via a series of field measurements. Pearson coefficients and trade-offs index were used to access the above ES relationships and degree of trade-offs between ES. Grazing intensities significantly (p < 0.05) affected the grassland intensities of 'regulating', 'culture' and 'provisioning' services, but the 'supporting' services. We found three types of relationships (trade-offs, synergy or neutral) have been found in this study. Trade-offs occurred between 'provisioning' and 'regulating' services. Although GE management presented significantly higher intensity of total ES (0.64) than LG (0.52), LG management significantly weakened the trade-offs between 'provisioning' and 'regulating' services (Trade-offs index 0.22) in comparison with GE (Trade-offs index 11.02). Our study suggests, therefore, that LG is the most suitable grassland utilization practice in the Inner Mongolian steppe.

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

Defining Deforestation Patterns Using Satellite Images from 2000 and 2017: Assessment of Forest Management in Miombo Forests—A Case Study of Huambo Province in Angola

A few studies have recently been published on changes in land use/land cover (LU/LC) of Angolan Miombo forests, however, none have attempted to offer forest management solutions for degraded Miombo forests. Landscapes are witness to past and present natural and social processes influencing the environment, where each period in the past leaves footprints on the landscape’s development, which can be described by a continual decrease in forest area over time. The expansion of degraded areas from 2000 to 20017 began near urban areas where many Miombo forests have been eliminated or highly degraded, particularly in the southwest and northeast of the Huambo province. Large areas of degraded forests were observed along the Benguela railway (Caminho de ferro de Benguela). Our detailed analysis of the landcover map suggests that the impact has been devastating and there is no form of forest protection, which leads to unregulated exploitation. Descriptions of the Miombo forest dynamics are explained using height–diameter curves developed for different vegetation types that provide important insights about forest structures in the management zones. The height–diameter models differed for all vegetation types, and four management zones (MZ) were created based on a set of particular attributes. The vegetation types differed in each management zone, which included agricultural land and bare soil (MZ–E), grassland or savanna (MZ–C), open Miombo forests (MZ–B), and closed Miombo forests (Miombo forests). The four management zones were easily identified on the available maps and the height–diameter models developed represent a fundamental tool for future studies on forest planning.

Value Chain of Charcoal Production and Implications for Forest Degradation: Case Study of Bié Province, Angola

Forest degradation and forest loss threaten the survival of many species and reduce the ability of forests to provide vital services. Clearing for agriculture in Angola is an important driver of forest degradation and deforestation. Charcoal production for urban consumption as a driver of forest degradation has had alarming impacts on natural forests, as well as on the social and economic livelihood of the rural population. The charcoal impact on forest cover change is in the same order of magnitude as deforestation caused by agricultural expansion. However, there is a need to monitor the linkage between charcoal production and forest degradation. The aim of this paper is to investigate the sequence of the charcoal value chain as a systematic key to identify policies to reduce forest degradation in the province of Bié. It is a detailed study of the charcoal value chain that does not stop on the production and the consumption side. The primary data of this study came from 330 respondents obtained through different methods (semi-structured questionnaire survey and market observation conducted in June to September 2013–2014). A logistic regression (logit) model in IBM SPSS Statistics 24 (IBM Corp, Armonk, NY, USA) was used to analyze the factors influencing the decision of the households to use charcoal for domestic purposes. The finding indicates that 21 to 27 thousand hectares were degraded due to charcoal production. By describing the chain of charcoal, it was possible to access the driving factors for charcoal production and to obtain the first-time overview flow of charcoal from producers to consumers in Bié province. The demand for charcoal in this province is more likely to remain strong if government policies do not aim to employ alternative sources of domestic energy.

Land management influences trade-offs and the total supply of ecosystem services in alpine grassland in Tibet, China

Developing sustainable use patterns for alpine grassland in Tibet is the primary challenge related to conserving these vulnerable ecosystems of the 'world's third pole' and guaranteeing the well-being of local inhabitants. This challenge requires researchers to think beyond the methods of most current studies that are limited to a single aspect of conservation or productivity, and focus on balancing various needs. An analysis of trade-offs involving ecosystem services provides a framework that can be used to quantify the type of balancing needed. In this study, we measured variations in four types of ecosystem services under five types of grassland management including grazing exclusion, sowing, combined plowing and grazing exclusion, combined plowing and sowing, and natural grassland, from 2013 to 2015. In addition, we accessed the existence and changing patterns of ecosystem service trade-offs using Spearman coefficients and a trade-off index. The results revealed the existence of trade-offs among provisioning and regulating services. Plowing and sowing could convert the trade-off relationships into synergies immediately. Grazing exclusion reduced the level of trade-offs gradually over time. Thus, the combined plowing and sowing treatment promoted the total supply of multiple ecosystem services when compared with natural grassland. We argue that the variations in dry matter allocation to above- and belowground serve as one cause of the variation in trade-off relationships. Another cause for variation in trade-offs is the varied species competition between selection effects and niche complementarity. Our study provides empirical evidence that the effects of trade-offs among ecosystem services could be reduced and even converted into synergies by optimizing management techniques.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies.