Human-induced Land Degradation Research Articles

Understanding the rapid increase in drought stress and its connections with climate desertification since the early 1990s over the Brazilian semi-arid region

The aridity-related drought studies have been carried out extensively in Brazilian semi-arid ecosystems, although there is no report on relating aridity with different drought indices over the recent decades. Due to drought impact on agriculture and natural ecosystems, it has attracted extensive attention in the academic community. In this study, the Standardized Precipitation Evapotranspiration Index (SPEI), the Soil Moisture and Ocean Salinity-derived Soil Water Deficit Index (SWDI), the Meteosat Spinning Enhanced Visible and InfraRed Imager (SEVIRI) radiance-derived solar and infrared estimates, and the SEVIRI-derived Normalized Difference Vegetation Index (NDVI) datasets were employed to investigate the spatial and temporal characteristics of the drought episodes and the vegetation dynamic responses. An approach was implemented for identifying drought using a combination of mathematical and statistical features derived from the SPEI. The results showed that: (1) The frequency, duration, and intensity of drought severity identified by the SPEI and SWDI from 1990 to 2022 showed different characteristics in the Brazilian semi-arid region from 1990 to 2022. The frequency of moderate, severe, and extreme drought ranged from 5% to 92%, the duration was mostly concentrated in 5–6 dry months. (2) The whole Brazilian semi-arid region exhibited an overall drying tendency. (3) The NDVI-derived vegetation in the Brazilian semi-arid ecosystems exhibited an overall decreased trend during 2004–2022, indicating a degrading trend of the vegetation cover. The region where degrading trend of NDVI was negatively correlated with SPEI accounted for approximately 13% of the region. (4) The frequency, and average duration of the flash drought were 21% concentrated in 6 pentads, during the region growing season. (5) The spatial and temporal trends with the significance test of the rainfall, air temperature, and SEVIRI radiance-based estimations suited well with those of the SPEI data. These research results were significant for the region to respond to and prevent drought through human-induced land degradation.

How Can Drones Uncover Land Degradation Hotspots and Restoration Hopespots? An Integrated Approach in the Mount Elgon Region with Community Perceptions

Human-induced land degradation in biodiverse regions like Mount Elgon threatens vital ecosystems. This study employs drone mapping and community insights to assess land use changes, degradation, and restoration in Mount Elgon, Uganda. Drone monitoring (2020–2023) covered six sites, complemented by household surveys (n = 499), Focus Group Discussions (FDGs), and interviews. Drone imagery shows agriculture and planted forest as dominant land use types, gradually replacing tropical high forest, bushland, and grassland. Drone image results showed that smallholder subsistence farming is leading to and enhancing degradation. Landslides and encroachment into the park were detected in three of the six sites. Trenches were the most adopted Soil and Water Conservation (SWC) measure. The trench adoption varied by location and crop type, creating restoration potential, notably in Elgon, Nabyoko, and Shiteka. Interviews and FGDs revealed adoption of trenches, grass strips, and afforestation as remedies to land degradation. Complex interactions exists among land use, degradation, and SWC measures in the upper Manafwa watershed, underscoring the urgency of addressing landslides and encroachment into the forest. Community-based initiatives are vital for hands-on SWC training, emphasizing long-term benefits. Collaboration among government, local communities, and NGOs is crucial to enforce conservation and restore Mt. Elgon National Park, while encouraging diversified income sources can reduce land dependency and mitigate degradation risks.

Apportioning Human-Induced and Climate-Induced Land Degradation: A Case of the Greater Sekhukhune District Municipality

Land degradation (LD) is a global issue that affects sustainability and livelihoods of approximately 1.5 billion people, especially in arid/semi-arid regions. Hence, identifying and assessing LD and its driving forces (natural and anthropogenic) is important in order to design and adopt appropriate sustainable land management interventions. Therefore, using vegetation as a proxy for LD, this study aimed to distinguish anthropogenic from rainfall-driven LD in the Greater Sekhukhune District Municipality from 1990 to 2019. It is widely established that rainfall highly correlates with vegetation productivity. A linear regression was performed between the Normalized Difference Vegetation Index (NDVI) and rainfall. The human-induced LD was then distinguished from that of rainfall using the spatial residual trend (RESTREND) method and the Mann–Kendall (MK) trend. RESTREND results showed that 11.59% of the district was degraded due to human activities such as overgrazing and injudicious rangeland management. While about 41.41% was degraded due to seasonal rainfall variability and an increasing frequency of droughts. Climate variability affected vegetation cover and contributed to different forms of soil erosion and gully formation. These findings provide relevant spatial information on rainfall or human-induced LD, which is useful for policy formulation and the design of LD mitigation measures in semi-arid regions.

Potential of GIS and remote sensing in mapping land degradation: catchment of the Manyame River, Zimbabwe

Abstract The Manyame River Catchment area in Zimbabwe, is experiencing severe land degradation mainly due to legal and illegal land husbandry practices. These practices are negatively impacting on the sustainability of the existing ecosystems. The conditions of land can be inferred using its vegetative cover e.g., Normalised Difference Vegetation Index (NDVI). Quantitative data relating to land degradation based on notable physical features such as gullies, for Manyame River Catchment at landscape scales is poor. This study focused on the distribution and magnitude of land degradation in the Manyame River Catchment area. The study mapped out the contours of human induced land degradation using a residual trend analysis (RESTREND) method. In particular, the study used remote sensed data (NDVI and precipitation time series) to analyse the shifts over period 2000 to 2017. The analysis used R statistical software packages (RESTREND and Kendal) and Geographic Information System (GIS) techniques to quantify the degradation trends. The results indicated extracts of those areas which experienced significant human induced land degradation during the study period. RESTREND effectiveness was assessed using Mann Kendal Test. The results of this study can be used by natural resources practitioners in monitoring, assessing and managing environmental changes using GIS tools.

Green manure for agricultural sustainability and improvement of soil fertility

Many farmers are incorporating green manuring into their practices as a result of the growing issues facing agriculture, such as climate change, extreme weather events, soil deterioration, and land contamination due to the excessive use of chemical fertilizers. Growing green manure is a useful and affordable way to ensure the long-term productivity of agricultural soils. Given the significant loss of soil fertility caused by improper agronomic practices, human-induced land degradation is extremely serious. Due to their positive effects on the physical, chemical, and biological quality of the soil as well as the proper justification of soil fertility, green manures can play a significant role. Utilizing green manure is one of the trends in environmental protection that aids in the agricultural sector's transition to sustainability by conserving resources and meeting consumer demand for food. Modern farmers are faced with a number of challenges as they try to transition to productive and ecologically friendly practices. To preserve soil fertility while utilizing less chemicals is one of them. Green manure crops could be a potential answer to this issue. Back in ancient times, farmers learned that this method had a positive effect on the soil. Thus, for growers who want to reduce the use of hazardous chemicals for soil fertilization, green manure is indispensable. In terms of operations, many farmers must employ green manure to reduce the overuse of chemical fertilizers in agriculture.

Investigating habitat degradation of Ursus arctos using species distribution modelling and remote sensing in Zagros Mountains of Iran

Knowledge on habitat suitability of ecologically important species is central for their conservation management. The occurrence records of brown bear (Ursus arctos) in Kurdestan Province of Iran and environmental data were compiled to predict the ecological niche of this bear using Maxent species distribution algorithm. Satellite imagery of Landsat 5 and Landsat 8 for 1990 and 2017 was also employed to investigate human-induced land degradation in the suitable habitats of the species. The images were pre-processed and classified into seven land use and land cover (LULC) classes using support vector machine (SVM) algorithm. According to Maxent model, about 9% of the study area could be regarded as highly suitable habitat, followed by moderately suitable habitat (11.6%) and marginally suitable habitat (29.3%). The integration of Maxent potential habitat map and LULC changes revealed that more than 6997 ha of the suitable habitats of the species have been degraded due to different human activities mainly related to the conversion of natural lands and Daryan Dam construction.

Effects of human-induced land degradation on water and carbon fluxes in two different Brazilian dryland soil covers

The Brazilian semiarid region presents a physical water scarcity and high seasonal and interannual irregularities of precipitation, known as a region with periodic droughts. This region is mainly covered by the Caatinga biome, recognized as a Seasonally Dry Tropical Forest (SDTF). Soil water availability directly impacts the ecosystem's functioning, characterized by low fertility and sparse vegetation cover during the dry season, making it a fragile ecosystem vulnerable to climatic variations. Additionally, this region has been suffering from several issues due to human activities over the centuries, which has resulted in extensive areas being severely degraded, which aggravates the impacts from climatic variations and the susceptibility to desertification. Thus, studying the soil-plant-atmosphere continuum in this region can help better understand the seasonal and annual behavior of the water and carbon fluxes. This study investigated the dynamics of water and carbon fluxes during four years (2013–2016) by using eddy covariance (EC) measurements within two areas of Caatinga (dense Caatinga (DC) and sparse Caatinga (SC)) that suffered anthropic pressures. The two study areas showed similar behavior in relation to physical parameters (air temperature, incoming radiation, vapor pressure deficit, and relative humidity), except for soil temperature. The SC area presented a surface temperature of 3 °C higher than the DC, related to their vegetation cover differences. The SC area had higher annual evapotranspiration, representing 74% of the precipitation for the DC area and 90% for the SC area. The two areas acted as a carbon sink during the study period, with the SC area showing a lower CO2 absorption capacity. On average, the DC area absorbs 2.5 times more carbon than the SC area, indicating that Caatinga deforestation affects evaporative fluxes, reducing atmospheric carbon fixation and influencing the ability to mitigate the effects of increased greenhouse gas concentrations in the atmosphere.

Does participatory farmer-to-farmer training improve the adoption of sustainable land management practices?

Human-induced land degradation currently threatens the livelihoods of over 3 billion people globally. While Sustainable Land Management (SLM) has emerged as a widely accepted approach for addressing land degradation in agroecosystems, the use of SLM practices remains low among smallholder farmers. Empirical research points to the potentially beneficial role of participatory farmer-to-farmer (F2F) training and knowledge sharing in improving SLM in resource-poor contexts. Drawing theoretical insights from social learning and using cross-sectional data from an agroecology intervention with smallholder farming households in Malawi (n = 609 farming households, comprising 463 households that received F2F training in agroecology and 146 households that did not receive F2F training), we examine the association between participatory agroecology training and the adoption of SLM practices. Findings from tobit regression analysis show that F2F training was positively associated with the use of SLM practices (β = 0.04 p < 0.05) two years following the intervention after accounting for demographic, agricultural and socioeconomic factors. These findings contribute to a growing body of literature that demonstrates the potential of participatory F2F training to improve the uptake and maintenance of SLM technologies. In the context of resource constraints and the associated low agricultural extension in sub-Saharan Africa, participatory F2F training may offer a cost-effective way to reach a wide range of smallholder farmers to promote the use of SLM practices.

Implications of land management practices on selected ecosystem services in the agricultural landscapes of Ethiopia: a review

ABSTRACT Human-induced land degradation and impairment of ecosystem services (ES) are among the serious problems in Ethiopia due to excessive pressure on land and water resources. Thus, various land management (LM) practices have been done to enhance ES and environmental sustainability. However, very little is known about the influence of efficacious LM practices on multiple ES. This study aims at assessing the implication of LM practices on crop yield, soil carbon stock, soil fertility, soil moisture, runoff, soil loss, nutrient loss, and cultural/societal services. A systematic review and synthesis methods were employed. Predefined criteria were used to screen relevant articles; thereby, 572 observations from 94 studies were extracted and synthesized. The results showed that agronomic practices increased grain yields, soil carbon stock, soil fertility, and soil moisture on average by 28, 29, 43 and 14%, respectively. Biological practices regulated runoff, soil loss, and nutrient loss on average by 46, 59 and 94%, respectively. Overall, LM practices were efficacious to enhance delivery of ES bundles but soil bund and fanyaa juu reduced the grain yield on average by 24 and 22%, respectively. This indicates that there are trade-offs between provisioning and other services like regulating, supporting and cultural under physical structures. However, landscape-level collective impact of different LM practices exhibited without any ES trade-offs. The integration of properly designed physical structures with agronomic and biological practices is imperative to enhance ES and balance the trade-offs in the agricultural landscapes. Further, it is important to find out alternative combinations of agricultural LM practices and evaluate for ES delivery.

Land Degradation in Bihar, India: An Assessment Using Rain-Use Efficiency and Residual Trend Analysis

Land degradation, a major environmental problem, poses threats to agricultural, social and economic stability of many regions of the world. In India, ~ 37% of the total land area is estimated to be degraded. Although assessment of land degradation in arid and semiarid regions of India has advanced through remote sensing time-series analysis such as rain-use efficiency (RUE) and residual trend analysis (RESTREND), the sub-humid and other regions mostly remain unexplored in this respect. In this study, land degradation in Bihar, a sub-humid state, was quantitatively assessed through RUE and RESTREND from 1995 to 2011. RUE is the ratio of aboveground net primary productivity to precipitation and has been widely used as a measure of land degradation. RESTREND, on the other hand, examines the trend of NDVI residuals, which is the difference between observed NDVI and predicted NDVI from rainfall data. Results indicate that RESTREND effectively estimated the extent of human-induced land degradation in Bihar as 4.73 M ha. Agro-climatic zone IIIB, the driest zone, has the highest percentage of degraded lands (33%), while Zone IIIA has the lowest percent of degraded lands (17%). Zones I and II each account for 25% of the degraded lands, most of which are affected by waterlogging and salinity. Although other land degradation databases have also indicated a rapid increase in land degradation across Bihar, it needs more ground-based data collection to substantiate it. The problem, however, may further aggravate with global warming, which calls for policy interventions such as adopting agroforestry, practicing sustainable agriculture and making shifts in cropping patterns.

Natural And Human-Induced Land Degradation And Its Impact Using Geospatial Approach In The Kallar Watershed Of Tamil Nadu, India

Land degradation is human-induced and natural process that adversely affects the land, to function effectively within a complex ecosystem. In recent years, the Kallar watershed has encountered various kinds of multifarious problems on both land and water in the urban and its environs. The upper part of the study area is facing water scarcity problems in the past few years, but which included no such rare occurrences in the past. The mid-portion in the vicinity of foothills are highly affected by soil erosion, whereas the lower portion of the area has faced problems like land degradation, such as an unusual increase of wastelands and conversion of good agriculture lands into construction plots. Apart from these, the study area is frequently affected by nature induced disasters like a landslide, forest fire, flooding, and drought. In this complex situation, the qualitative assessment of human-induced land degradation and its impact is essential. For this, Geospatialbased Multi-Criteria Evaluation (MCE) as a multidisciplinary approach has been adopted. To assess land degradation, six major criterions are preferred such as terrain (slope, elevation), environment (landuse/land cover, NDVI), soil erosion, and demography (population density). Considerable weights and ranks were assigned through an empirical MCE method. Based on the criteria, the land degradation was carefully delineated into five significant categories such as low (38.3%), moderately (23.6%), marginally (15.4%), highly (4.8%), and severely degraded (17.8%). The depletion of vegetation cover on hilly terrain and subsequent cultivation without proper protection measures constitute the possible reason for severe soil erosion and land degradation.

Human-induced land degradation dominance in the Nigerian Guinea Savannah between 2003 – 2018

Land degradation poses a persistent challenge to ecosystems and sustainable livelihoods in the Nigerian Guinea Savannah (NGS). While both human activity and climate variability have been implicated as degradation drivers, the lack of research fuels dispute over the causes and status of land degradation in the Savannah. Detailed evidence on the contributions of both rainfall and human activities can, however, help identify appropriate measures to address land degradation. MODIS vegetation “greenness” and TAMSAT rainfall data were employed to achieve the following objectives: (i) provide empirical insights on the pattern of savannah vegetation dynamics; (ii) control for rainfall effects in Savannah degradation; and (iii) characterize the extent, severity and geography of human-induced land degradation. The selected statistical techniques proved useful for highlighting the spatio-temporal dynamics of degradation in the NGS. Controlling for the effect of rainfall on vegetation greenness produces a Normalized Difference Vegetation Index (NDVI) residual that allows to us estimate the human impact on land degradation. Despite no indication of a worsening rainfall regime, inter-annual variation in vegetation greenness exhibits a consistently negative, declining trend. This continuous, negative, declining trend in the NDVI residual strongly suggests ongoing biomass loss in the NGS is the result of unsustainable human activity. Observed improvement is attributable to existing land management programmes (afforestation and the planting of drought tolerant species) initiated by states in the zone. In sum, approximately 38% of the NGS land area, including protected areas such as Kainji Lake National Park, are becoming more degraded, while 14% and 48% of the remaining area shows either improvement or no real change, respectively. These results serve as a baseline information resource for tracking future land use activities, land degradation and potential pathways for achieving more sustainable land management.

Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard

Inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Inland excess water is an interrelated natural and human induced land degradation phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behavior of the occurrence, frequency of inland excess water. Spatial auxiliary information representing inland excess water forming environmental factors were taken into account to support the spatial inference of the locally experienced inland excess water frequency observations. Two hybrid spatial prediction approaches were tested to construct reliable maps, namely Regression Kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use, and climate. Comparing the results of the two approaches, we did not find significant differences in their accuracy. Although both methods are appropriate for predicting inland excess water hazard, we suggest the usage of RFK, since (i) it is more suitable for revealing non-linear and more complex relations than RK, (ii) it requires less presupposition on and preprocessing of the applied data, (iii) and keeps the range of the reference data, while RK tends more heavily to smooth the estimations, while (iv) it provides a variable rank, providing explicit information on the importance of the used predictors.

Assessment of Farmers’ Perception of Land Degradation and Coping Strategies in Yobe State, Nigeria

The study was conducted between December 2019 – February 2020 in Nguru, Po-tiskum and Gujba Local Government Areas of Yobe state, Nigeria with sole aim of assessing farmers’ perception of land degradation and coping strategies in the state. Descriptive Survey design involving both quantitative and qualitative methods us-ing structured questionnaire and Focused Group Discussions was adopted. A total of 110 farmers were sampled using Purposive and Convenient techniques. Respondents’ demographic data revealed that the majority of the respondents were uneducated male youth with good years of experience in farming practicing non-mechanised rain fed commercial farming with vast farmlands. Crops commonly grown were corn (Zea mays), groundnut (Arachis hypogeae), maize (Zea mays), beans (Phaseolus vulgaris), sesame (Sesamum indicum) and sorghum (Sorgum bicolor) and commonly observed forms of land degradation included wind erosions, soil nutrient depletion, loss of soil fertility, loss of vegetation cover and break down of soil structure. Other forms of land degradation peculiar to some of the studied places were bio-invasion and salinization in Nguru and serious water erosion in Potiskum. The most common unsustainable farming practices identified included deforestation, bush burning, mono-cropping, in-creased tillage, continuous cultivation, indiscriminate use of fertilizer and other agro-chemicals. The farmers’ perceptions on the causes of land degradation were not more different and included deforestation, bush burning, natural causes, continuous crop-ping etc. while its indicators according to the farmers’ perceptions included loss of soil fertility, poor crop yield etc. The farmers also believed that the major impacts of land degradation were reduced crop yield which translates into increased poverty among farming communities. With regards to the possible solutions to land degra-dation, the farmers believed that measures such as shifting cultivation, reforestation, stopping bush burning, mulching, use of organic manure, application of inorganic fer-tilizer etc. were adopted by the farmers. Besides, to cope with the impacts of droughts and delayed rains, the farmers usually practice early planting and use of improved seeds especially drought resistant seeds. Based on the outcome of the study, it was concluded that despite being fully aware of the environmental impacts of some farming practices such as bush burning, farmers in these areas still engage in many unfriendly farming practices probably due to poverty, culture or ignorance. Hence, it was against this background that a strong recommendation was proffered that special laws and regulations should be enacted to tackle human induced agricultural land degradation.

Landscape Scale land degradation mapping in the semi-arid areas of the Save catchment, Zimbabwe

ABSTRACT This research investigates the distribution and magnitude of vegetation loss in the Save catchment. The main objective is to map and quantify human-induced land degradation in the Save catchment, using Residual trend analysis (RESTREND) method. The investigation was done using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series data, and gridded precipitation datasets from Climate Research Unit, recorded between 2000 and 2015. NDVI and rainfall time series, as well as ordinary least squares regression models used in the analysis, were computed in R statistical program. Geographic Information System (GIS) techniques were used in the quantification of degradation trends. The study revealed that approximately 18% of Save catchment experienced declining residual trends whilst increasing residual trends covered 34% of the area. These trends covered 1.7 Mha and 3.1 Mha, respectively. The study calculated the statistical significance of the declining trends. The results demonstrate that about 3.6 Mha experienced significant human-induced land degradation during the study period. This area represents 39% of the Save Catchment, 4%, 13%, and 22% of which were classified as severely, moderately, and lightly degraded, respectively. The results indicate the vulnerability of Save catchment to human-induced degradation. Severe degradation was noted in the central districts of the Save Catchment, notably Bikita, Chiredzi, and most parts of Chipinge. These findings demonstrate the effectiveness of RESTREND in removing influence of precipitation changes from land degradation. Based on these results, we recommend for the use of RESTREND method in detecting land degradation that is triggered by human actions.

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Information on the spatial distribution of human disturbance is important for assessing and monitoring land degradation. In the Eastern Mongolian Steppe Ecosystem, one of the major driving factors of human-induced land degradation is the expansion of road networks mainly due to intensifications of oil exploration and exploitation. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Consequently, the study aim is to provide a cost-effective methodology to classify infrastructure and oil exploitation areas from remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. For the classification variables, segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Results show that overall accuracies of land use maps ranged 73%–93% mainly depending on satellites’ spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005–2010. Settlements and croplands remained relatively constant throughout the 13 years. Comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Consequently, the results of this study provide an effective evaluation for the potential of Random Forest for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments.

Spatial-Temporal Pattern of Human-Induced Land Degradation in Northern China in the Past 3 Decades-RESTREND Approach.

Land degradation is one of the world’s most serious environmental issues. Human activities play an important role in it. Therefore, human-induced land degradation monitoring is of crucial scientific significance in revealing the evolution of land degradation and guiding its governance. Based on the residual trend (RESTREND) approach and using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) 3g and monthly precipitation as data sources, a quantitative evaluation is conducted on the conditions of human-induced land degradation during 1982–2012 in northern China. The results indicate that (1) the “optimal cumulative precipitation-NDVImax” regression model constructed herein can improve the capability of recognizing human-induced land degradation of arid and semiarid areas in the RESTREND approach. Moreover, long time-series NDVI and precipitation data may reduce the uncertainty of quantifying human-induced land degradation. (2) In the past 3 decades, northern China has experienced three stages of human-induced land degradation, i.e., rapid development, overall reversal with local development, and continuous reversion. Human-induced land degradation in the agro-pastoral ecotone of northern China has shown a rapid restoration trend since the 1990s. (3) It is believed that the dominant factor of land degradation has a significant spatial-temporal scale effect and spatial heterogeneity. Therefore, concrete issues should be specifically analyzed to improve our understanding of land degradation development and reversal, the spatial-temporal pattern and the driving forces of land degradation in the past 3 decades in northern China. Climate change may be the main driving force of land degradation. However, the influence of human activities on the development and reversal of land degradation in small areas and in a short time is more remarkable.

Conserving terrestrial linkages that connect natural landscapes of the Korean Peninsula.

Human-induced land degradation fragments natural ecosystems, hinders ecological processes, and threatens biodiversity. Maintaining or restoring ecological flows across landscapes through landscape linkages may provide a solution. Here, we identify a peninsula-wide ecological connectivity network for the Korean Peninsula using two linkage mapping models. We found three major north-south axes of connectivity traversing the Demilitarized Zone (DMZ), which emerged as an important east-west linkage. Only 7% of the highest-ranked connections are currently secured by protected areas. We found 120 linkages in North and South Korea that are intersected by road networks consisting of motorways and trunk roads under both models. These locations should be the focus of immediate attention for conservation planners, as well as 274 and 1130 additional road-impacted linkages under one model or the other. The results can be used for policy support, and potentially as a basis for the two countries to engage in discussions about ecosystem health and climate change adaptation. The approach presented here can also be efficiently used to assess and map natural landscape linkages.

Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation.

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil

Land degradation (LD) is one of the most catastrophic outcomes of long-lasting drought events and anthropogenic activities. Assessing climate and human-induced impacts on land can provide information for decision makers to mitigate the effects of these phenomena. The Northeastern region of Brazil (NEB) is the most populous dryland on the planet, making it a highly vulnerable ecosystem especially when considering the lingering drought that started in 2012. The present work consisted of detecting trends in biomass [leaf area index (LAI)] anomalies as indicators of LD in NEB. We also assessed how the loss of vegetation impacts the LD cycle, by measuring trends in albedo and evapotranspiration (ET). LAI, albedo and ET data were derived from MODIS sensors at 8-day temporal and 500 m spatial resolutions. For precipitation anomalies, we relied on CHIRPS-v2 10-day temporal at 5 km spatial resolution data. For detecting trends, we applied the Theil-Sen slope analysis on time series of MODIS LAI, albedo and ET images. Trend analysis was performed for the periods ranging from 2002–2012 (no severe droughts) to 2002–2016 (including the last drought). LAI trends were more pronounced and had a stronger signal than ET and albedo, therefore, LAI was our choice for mapping LD. The first analysis highlighted the human-induced LD prone areas whereas the last detected drought-induced LD prone areas. Considering only the trending areas, which was about 23.4% of the total, 4.5% of this area has undergone human-induced degradation whereas drought was responsible for 73%, although, not mutually exclusive. As reported in the literature and official data, grazing intensification might be a factor driving human-induced degradation. We noticed that the range of variation of LAI is narrow and even narrower for albedo, which demonstrates that land surface response is more influenced by soil reflectivity rather than the characteristic sparse vegetation coverage (LAI ranging from 0.04 to 0.4 in the Caatinga biome), which can barely alter albedo. Finally, the effects of LD on ET anomalies were assessed by Granger causality and impulse-response analyses as means to link land surface feature changes to the hydrological cycle. Albedo had a slightly weaker impulse than LAI on ET whereas precipitation played a major role. These relations are site-specific and, land surface features (biomass and albedo) showed to have a more substantial influence on ET in severely degraded areas. We concluded that drought led to trends indicating LD prone areas in NEB and the degradation cycle has positive feedback derived from ET reduction resulting in an increased net moisture deficit, although the latter statement has yet to be further investigated. The study warns of the desertification risk that NEB is facing and the need for the authorities to take action to mitigate degradation and drought effects on both traditionally surveyed (desertification nuclei) and newfound LD prone areas. We also highlight the limitation of confirming LD, as to date there is no post-drought data available and, lessons learned from the Sahel case make us cautious about claiming that an area is in fact degraded.