ArticlePublisher preview available

Global land change from 1982 to 2016

August 2018
Nature 560(7720)

DOI:10.1038/s41586-018-0411-9

Authors:

Xiao-Peng Song

Texas Tech University

Stephen V. Stehman

State University of New York College of Environmental Science and Forestry

Peter Potapov

University of Maryland, College Park

Show all 7 authorsHide

Land change is a cause and consequence of global environmental change1,2. Changes in land use and land cover considerably alter the Earth's energy balance and biogeochemical cycles, which contributes to climate change and-in turn-affects land surface properties and the provision of ecosystem services1-4. However, quantification of global land change is lacking. Here we analyse 35 years' worth of satellite data and provide a comprehensive record of global land-change dynamics during the period 1982-2016. We show that-contrary to the prevailing view that forest area has declined globally5-tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level). This overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (-3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover. The mapped land changes and the driver attributions reflect a human-dominated Earth system. The dataset we developed may be used to improve the modelling of land-use changes, biogeochemical cycles and vegetation-climate interactions to advance our understanding of global environmental change1-4,6.

Satellite-derived, long-term (1982–2016) changes in land cover show strong coupling and symmetry in change detection a, Global map of co-located ∆TC and ∆SV. Pixels showing a statistically significant trend (n = 35 years, two-sided Mann–Kendall test, P < 0.05) in both TC and SV are depicted on the map. b, Global map of co-located ∆TC and ∆BG. c, Global map of co-located ∆SV and ∆BG. d, From left to right, intensity plot of change area for ΔTC versus ΔSV, ΔTC versus ΔBG and ΔSV versus ΔBG, corresponding to a, b and c, respectively. To create these intensity plots, paired per cent change layers (Fig. 1b) are used to construct a 2D histogram with bin size of 1% for both axes. Then, the total change area in each bin is calculated and plotted.

…

Long-term (1982–2016) gross land-change dynamics vary considerably between biomes a–p, Gross land-change dynamics per biome. Mountain systems (c, f, i, n) all exhibit larger area of TC gain than TC loss, larger area of SV loss than SV gain and larger area of BG loss than BG gain. q, Geographical distribution of all biomes, from a previous publication³⁰, reproduced with permission. See Fig. 3 for other biomes and Extended Data Table 2 for change area estimates.

…

Attributing direct human impact versus indirect drivers to detected changes in land cover Indirect drivers include both natural drivers and human-induced climate change. a, Spatial distribution of the probability sample used for the attribution estimates (n = 1,500). b, Direct human impact (DHI) of each sample unit interpreted using a time-series of high-resolution images in Google Earth. c, Estimated DHI as a per cent of all change area at the global scale. Global average is calculated by weighting the human impact of each type by each respective global total area provided in Extended Data Table 1. The standard error (SE) for the estimated per cent of DHI is provided in the parentheses. d, e, Estimated DHI at the continental and biome scales. See Extended Data Fig. 4 for some representative sample examples.

…

Selected sample examples for driver attribution Screenshots are taken from Google Earth. Each panel is 0.05° × 0.05° in size, corresponding to one AVHRR pixel. a, Deforestation for industrial agriculture expansion in Mato Grosso, Brazil (11.275° S, 52.125° W). b, Expanding shifting agriculture in northern Zambia (11.625° S, 28.625° E). c, Intensification of small-holder agriculture in Punjab, Pakistan (30.025° N, 71.675° E). d, Short vegetation gain in low-intensity agricultural lands in northern Nigeria (12.825° N, 7.825° E). e, Short vegetation increase due to effective fire suppression in pasture lands in Omaheke, Namibia³¹ (22.175° S, 18.925° E). f, Managed pasture lands in western Kazakhstan (49.475° N, 47.725° E). g, Forestry in southern Finland (61.075° N, 24.475° E). h, Urbanization in Shanghai, China (30.925° N, 121.175° E). i, Oil extraction in New Mexico, USA (32.875° N, 104.275° W). j, Herbaceous vegetation increase owing to glacial retreat in Chuy, Kyrgyzstan (42.575° N, 74.775° E). k, Bare ground cover variation along Mar Chiquita lake shore in Cordoba, Argentina (30.675° S, 63.025° W). l, Forest fires in Saskatchewan, Canada (55.225° N, 102.225° W). m, Tree cover increase in unpopulated savannahs in Western Equatoria, South Sudan16,17 (6.575° N, 27.725° E). n, Climate-change-driven woody encroachment in Quebec, Canada¹⁵ (59.475° N, 73.225° W). Examples a–i show various types of land use, whereas examples j–n do not show visible signs of human activity. Map data: Google, DigitalGlobe, CNES/Airbus, Landsat/Copernicus.

…

Global trends in land cover during 1982–2016 a, Trends in TC cover. b, Trends in SV cover. c, Trends in BG cover. The following steps were taken for each cover type using TC as the example. The TC gain layer (Fig. 1b) was overlaid on the annual TC% stack to compute annual global TC area within the gain mask (solid dark blue lines); the TC loss layer (Fig. 1b) was overlaid on the annual TC% stack to compute annual global TC area within the loss mask (solid dark red lines). Gross gain estimates from 1986 to 2016 are marked by blue arrows and dashed lines; gross loss estimates from 1986 to 2016 are marked by red arrows and dashed lines. See Extended Data Table 1 for exact gross change estimates.

…

Figures - available from: Nature

This content is subject to copyright. Terms and conditions apply.

A preview of this full-text is provided by Springer Nature.
Learn more

Preview content only

Content available from Nature

This content is subject to copyright. Terms and conditions apply.

LETTER https://doi.org/10.1038/s41586-018-0411-9

Global land change from 1982 to 2016

Xiao-Peng Song1*, Matthew C. Hansen1, Stephen V. Stehman2, Peter V. Potapov1, Alexandra Tyukavina1, Eric F. Vermote3

& John R. Townshend1

Land change is a cause and consequence of global environmental

change

1,2

. Changes in land use and land cover considerably alter the

Earth’s energy balance and biogeochemical cycles, which contributes

to climate change and—in turn—affects land surface properties and

the provision of ecosystem services1–4. However, quantification

of global land change is lacking. Here we analyse 35years’ worth

of satellite data and provide a comprehensive record of global

land-change dynamics during the period 1982–2016. We show

that—contrary to the prevailing view that forest area has declined

globally5—tree cover has increased by 2.24millionkm2 (+7.1%

relative to the 1982 level). This overall net gain is the result of a net

loss in the tropics being outweighed by a net gain in the extratropics.

Global bare ground cover has decreased by 1.16millionkm2

(−3.1%), most notably in agricultural regions in Asia. Of all land

changes, 60% are associated with direct human activities and 40%

with indirect drivers such as climate change. Land-use change

exhibits regional dominance, including tropical deforestation and

agricultural expansion, temperate reforestation or afforestation,

cropland intensification and urbanization. Consistently across

all climate domains, montane systems have gained tree cover and

many arid and semi-arid ecosystems have lost vegetation cover. The

mapped land changes and the driver attributions reflect a human-

dominated Earth system. The dataset we developed may be used to

improve the modelling of land-use changes, biogeochemical cycles

and vegetation–climate interactions to advance our understanding

of global environmental change1–4,6.

Humanity depends on land for food, energy, living space and

development. Land-use change—traditionally a local-scale human

practice—is increasingly affecting Earth system processes, including

the surface energy balance, the carbon cycle, the water cycle and species

diversity1–4. Land-use change is estimated to have contributed a quarter

of cumulative carbon emissions to the atmosphere since industriali-

zation

. As population and per capita consumption continue to grow,

so does demand for food, natural resources and consequent stress to

ecosystems.

Because of their synoptic view and recurrent monitoring of the

Earth’s surface, satellite observations contribute substantially to our

current understanding of the global extent and change of land cover

and land use. Previous global-scale studies have mainly focused on

annual forest cover change (stand-replacement disturbance) for the

time period after 20007, or focused on sparse temporal intervals8. Long-

term gradual changes in undisturbed forests as well as areal changes in

cropland, grassland and other non-forested land are less well quantified.

We create an annual, global vegetation continuous fields product9

for the time period 1982 to 2016, consisting of tall vegetation (≥5m in

height; hereafter referred to as tree canopy (TC)) cover, short vegetation

(SV) cover and bare ground (BG) cover, at 0.05°×0.05° spatial resolu-

tion (for details of definitions, seeSupplementary Methods). For each

year, every land pixel is characterized by its per cent cover of TC, SV

and BG, representing the vegetation composition at the time of the local

peak growing season. The dataset is produced by combining optical

observations from multiple satellite sensors, including the Advanced

Very High Resolution Radiometer (AVHRR), the Moderate Resolution

Imaging Spectroradiometer, the Landsat Enhanced Thematic Mapper

Plus and various sensors with very high spatial resolution. We use

non-parametric trend analysis to detect and quantify changes in tree

canopy, short vegetation and bare ground over the full time period at

pixel (0.05° × 0.05°), regional and global scales. Observed changes are

attributed to direct human activities or indirect drivers on the basis of a

global probability sample and interpretation of high-resolution images

from Google Earth.

The total area of tree cover increased by 2.24million km

from 1982

to 2016 (90% confidence interval (CI): 0.93, 3.42million km2), which

represents a +7.1% change relative to 1982 tree cover (Extended Data

Table1). Bare ground area decreased by 1.16 million km2 (90% CI:

−1.78, −0.34million km

), which represents a decrease of 3.1% relative

to 1982 bare ground cover. The total area of short vegetation cover

decreased by 0.88million km2 (90% CI: −2.20, 0.52million km2), which

indicates a decrease of 1.4% relative to 1982 short vegetation cover. A

global net gain in tree canopy contradicts current understanding of

long-term forest area change; the Food and Agriculture Organization of

the United Nations (FAO) reported a net forest loss between 1990 and

20155. However, our gross tree canopy loss estimate (−1.33million km2,

−4.2%, Extended Data Table1) agrees in magnitude with the

FAO’s estimate of net forest area change (−1.29million km2,

−3%), despite differences in the time period covered and definition

of forest (the FAO defines ‘forest’ as tree cover ≥10%; see details

inSupplementary Methods).

The mapped land change (Fig.1) consists of all changes in land

cover and land use induced by natural or anthropogenic drivers.

Land change themes are also inherently linked in the tree cover–short

vegetation–bare ground nexus. For example, deforestation for agricultural

expansion is often manifested as tree canopy loss and short vegetation

gain, whereas land degradation may simultaneously result in short

vegetation loss and bare ground gain. Pairs of changes in TC (ΔTC), SV

(ΔSV) and BG (ΔBG) show strong coupling and symmetry in change

direction but vary substantially over space (Fig.1b and Extended Data

Fig.1). That is, the globally dominant, coupled land changes are ΔTC

co-located with ΔSV and ΔSV co-located with ΔBG.

The overall net gain in tree canopy is a result of a net loss in the tropics

being outweighed by a net gain in the subtropical, temperate and boreal

climate zones (Extended Data Table2). A latitudinal north (gain)–south

(loss) contrast in tree cover change is evident (Fig.2a). Conversely,

for short vegetation tropical net gain is exceeded by extratropical net

loss. The latitudinal profile of ΔSV largely mirrors that of ΔTC, most

obviously in the northern mid-to-high latitudes (45°N–75°N) and

low latitudes (30°S–10°N) (Fig.2b). For bare ground, subtropical

net gain partially offsets losses in all other climate domains. In

the northern low-to-mid latitudes (10°N–45°N), the profile

of bare ground loss (Fig.2c) closely corresponds to that of short

vegetation gain (Fig.2b).

Changes were unevenly distributed across biomes (Fig.3, Extended

Data Fig.2 and Extended Data Table2). The largest area of net tree

canopy loss occurred in the tropical dry forest biome (−95,000km

−8%) (Extended Data Fig.2a), closely followed by tropical moist decid

uous forest (−84,000 km2, −2%) (Fig.3c) (all per cent net changes

1Department of Geographical Sciences, University of Maryland, College Park, MD, USA. 2College of Environmental Science and Forestry, State University of New York, Syracuse, NY, USA. 3NASA

Goddard Space Flight Center, Greenbelt, MD, USA. *e-mail: xpsong@umd.edu

Corrected: Author Correction

30 AUGUST 2018 | VOL 560 | NATURE | 639

Autoeficacia en el ahorro, frugalidad y satisfacción vital. ¿Influyen los ingresos en su relación? Self-Efficacy, Frugality, and Life Satisfaction. Does income affect the relationship?

Article

Full-text available

Jul 2022

La conducta frugal es un comportamiento centrado en la reducción voluntaria del consumo como resultado del uso ingenioso de los recursos con los que la persona cuenta y de la restricción voluntaria del gasto en nuevos productos y servicios. No obstante, para que el comportamiento frugal sea una alternativa realista, debe estar asociado con elementos psicológicos positivos en lugar de un esfuerzo constante. En este estudio, se analiza la relación entre la conducta frugal, la autoeficacia en el ahorro y la satisfacción con la vida, teniendo en cuenta los recursos económicos de las personas. Se realizaron dos estudios correlacionales con 186 estudiantes universitarios y con 154 participantes de población general, respectivamente. Los resultados obtenidos en ambos estudios señalan que la realización de conductas de frugalidad requiere que las personas perciban que son capaces de ahorrar y competentes en el aprovechamiento de recursos. También se observaron relaciones significativas entre la conducta frugal y la satisfacción con la vida, no obstante, en el segundo estudio se advirtió que esta relación está moderada por el nivel de ingresos. La conducta frugal se relaciona con mayor satisfacción con la vida en personas con ingresos más altos, pero se relaciona con menor satisfacción con la vida en personas con ingresos más bajos. En conclusión, el consumo frugal puede ser una alternativa positiva de consumo asociada al bienestar, en la medida en que los recursos percibidos y objetivos sean suficientes para que la persona pueda elegir su estilo de consumo.

Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat

Article

Full-text available

Jun 2022

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Urban green and blue space changes: A spatiotemporal evaluation of impacts on ecosystem service value in Bangladesh

Article

Jun 2022
ECOL INFORM

The rapid decline in urban green (UGS) and blue space (UBS) in developing countries has led to a widespread degradation of available ecosystem services (ES). However, impacts of UGS and UBS changes on ES tend to vary over space and time, and to date these impacts have not been studied in sufficient detail in emerging economies. By comparing UGS and UBS change patterns with multitemporal Landsat data recorded during the past 30 years (1991–2021), this study has examined the impact of several factors on ES in some of the world's climate hotspots. Although obtaining relevant and accurate information on ES is difficult in many parts of the developing world, this work has developed baseline data suitable for assessing ES loss over five densely populated cities in Bangladesh – Dhaka, Chattogram, Khulna, Rajshahi, and Sylhet. ES loss was quantified in monetary terms using adjusted value coefficients. The topographic and anthropogenic factors driving spatial differences in ES degradation in these cities were analyzed with a geographical detector. The results indicated that the cities experienced a combined monetary loss of USD 628.58 million as a result of specific ES degradation, primarily due to the decline of UGS and UBS. The value of ES loss was notably higher in Dhaka and Chattogram than in the other cities due to marked differences in anthropogenic activities. Population growth, extensive urban sprawl, and the development of dense road networks were identified as the major causes of urban green and blue space loss and consequent reduction of ES. The findings of this study provide important insights which can be used to support the formulation of public policies and management plans aimed at restoring and maintaining sustainable urban ecosystems.

Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh

Article

Full-text available

Jun 2022

Ecosystem service supply-demand and socioecological drivers at different spatial scales in Zhejiang Province, China

Article

Full-text available

Jun 2022
ECOL INDIC

Understanding the scale effects of ecosystem service (ES) supply-demand balances and drivers is critical to hierarchical ecosystem management. However, it remains unclear how the relationships of ES supply-demand and driving factors change with the scale. In this study, we first quantified food production (FP), water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ) at pixel and county scales in 2000 and 2020 in Zhejiang Province. Then, we analyzed the ES supply-demand balances and trade-offs/synergies at different scales. Finally, we performed correlation analysis and applied a random forest model to explore the socio-ecological drivers of these ESs. Our work showed that the supplies of FP, WY, and SC increased, while those of CS and HQ decreased from 2000 to 2020. ESs at the pixel scale were more spatially heterogeneous than those at the county scale. FP and CS were in short supply, and the gaps between their supply and demand grew over time. Some ES supply-demand mismatches at the pixel scale disappeared at the county scale. From the pixel scale to the county scale, the correlation directions of the ES trade-offs/synergies changed slightly, but their intensities changed significantly. The temperature, altitude, percentage of forestland and normalized difference vegetation index (NDVI) had positive effects on HQ, CS and SC, while the population density (POP), gross domestic product and percentage of artificial land (PA) had negative effects. The degree of influence of most socioecological drivers on the ESs increased with increasing scale. NDVI was the most important factor for CS, while precipitation was the most important for WY. The importance of POP and PA increased with both time and scale. Ultimately , overall ES supply-demand balances should be considered at the county scale, while more accurate management measures should be implemented at the pixel scale to promote effective hierarchical ES management. This study emphasizes the necessity of considering the scale effects for ES supply-demand balances in sustainable ecosystem management.

Landuse/landcover monitoring and spatiotemporal modelling using multilayer perceptron and ‘multilayer perceptron’-Markov Chain ensemble models: A case study of Dausa City, Rajasthan

Article

Jun 2022

The present work is an attempt to the LULC classification, monitoring, and spatiotemporal prediction using Artificial Neural Network - Multi-Layer Perceptron (MLP) and MLP-Markov Chain (MC) models. Dausa city and its surroundings of Rajasthan, India has been selected for this study for several reasons including arid climatic setting being a sensitive precursor to the climate change scenarios and the huge population pressure experienced by the area. The MLP based supervised classification for two periods 2001 and 2018 have been analyzed using Landsat 7 Thermal Mapper (TM) and Landsat 8 OLI satellite images. The images were classified into six LULC categories viz. Built-up (Settlements), Cultivated Lands (Agricultural/Cropland), Water Body, Uncultivated/Fallow Lands, Barren Lands, and Forest/Vegetation cover. The accuracy assessment for both classified images was performed using confusion matrix led Kappa Coefficient (K) technique. Reasonable accuracies, K=0.82 (2001) & K = 0.91 (2018), have been achieved for datasets selected for both periods of time. The MLP-MC model based spatiotemporal LULC prediction for the year 2045, using the trends in the classified LULC results for the period 2001-2018, prophecies that the ‘built-up land’ would increase to reach 76.10 km2 (67.60% increase) in 2045 with the reference year 2001 whereas the increase in this class of LULC would only be 39.34% during the period 2018-2045. The ‘cultivated land’ (2001-2045: -83.86%; 2018-2045: -65.20%), ‘barren land’, (2001-2045: -54.70%; 2018-2045: -4.86%), ‘water body’ (2001-2045: -96.43%; 2018-2045: -84.42%), and ‘forest/vegetation’ (2001-2045: -81.94%; 2018-2045: -20.59%), categories would experience continuous areal decline over this period, though some at faster pace and other at comparatively lower rate. The projected unprecedented exponential increase in ‘follow land/uncultivated land’ (2001-2045: +372.45%; 2018-2045: +6.39%) presents worrisome future picture of this ecologically sensitive and fragile region. The results of this study indicate and warrant intensive management and policy, and local level participation of communities to help maintain the deteriorating ecological balance in this ecologically sensitive arid ecosystem with fragile agricultural and natural vegetation traits.

Fiabilidad en la detección de las superficies selladas empleando datos del programa/Copernicus Reliability of sealed surfaces detection using Copernicus data

Article

Full-text available

Jul 2022
B ASOC GEOGR ESP

Durante los últimos 50 años se han producido cambios significativos en las cubiertas y usos del suelo, principalmente aquellos catalogados como artificiales. Este proceso, y su generalización a escala global, afectan de forma directa a las funciones básicas del suelo, acrecentando otros problemas como pueden ser la pérdida de biodiversidad, contaminación, degradación edáfica, inundaciones, o los efectos del cambio climático. En el área de estudio (Mazarrón, Región de Murcia) el problema anterior resulta ejemplar: el binomio desarrollo urbano asociado al turismo de sol y playa y la agricultura intensiva (bajo invernaderos) alteran de forma drástica la naturaleza del suelo. El objetivo es establecer un modelo de clasificación supervisada que distinga, con un error asumible, las distintas clases establecidas, destacando sobre todas ellas las que supongan.

Leveraging the use of labeled benchmark datasets for urban area change mapping and area estimation: a case study of the Washington DC–Baltimore region

Article

Jun 2022
INT J DIGIT EARTH

Response of Land Use and Net Primary Productivity to Coal Mining: A Case Study of Huainan City and Its Mining Areas

Article

Jun 2022

The terrestrial ecosystem carbon cycle is essential to the global carbon cycle. Mining activities have seriously damaged the terrestrial ecosystem and destroyed the carbon sequestration ability of vegetation, which is of great significance to studying the effect of coal mining on land structure change and carbon sink function in cities and mining areas. However, the existing research lacks the targeted analysis of the carbon sink level of the mining area combined with the mining data. Based on the coal-mining information, land-use data, and MODIS NPP data, this study analyzed the spatio-temporal change characteristics of land use and NPP in Huainan City and its mining areas from 2001 to 2020. The results showed that: (1) 22.5% of the land types in the mining area have changed, much higher than 3.2% in Huainan; 40.08 km2 of the cropland in the mining area has been transformed into waterbodies, seriously affecting regional food security. (2) NPP fluctuates with rainfall, has a weak correlation with temperature, and is restricted by coal-mining factors. The average NPP of most coal mines is significantly lower than that of non-mining areas. The NPP of Huainan City showed an overall growth trend of 2.20 g/(m2 × a), which was much higher than the average value of 0.43 g/(m2 × a) in the mining area. Especially in the Guqiao mine, the difference in NPPslope before and after mining was as high as 16.92 g/(m2 × a). (3) The probability integral method was used to estimate that 195.16 km2 of land in Huainan would be damaged by mining in 2020. The distribution of damage degree was negatively correlated with NPPslope, which meant the more serious the damage was, the less NPPslope was. This study revealed the characteristics of land-use change and NPP spatio-temporal response in resource-based cities and mining-disturbed areas. It quantitatively estimated the impact of mining activities on regional carbon sink function. It can provide theory and data support for mining areas to carry out ecological protection and restoration, improve the environmental service function of resource-based cities, and formulate sustainable development strategies.

Temporal and Spatial Evolution Characteristics and Its Driving Mechanism of Land Use/Cover in Vietnam from 2000 to 2020

Article

Jun 2022

Research on the spatial distribution and dynamic evolution of land use/land cover (LULC) is the basis for land management and ecological protection. However, there is currently a lack of long-term analysis on the evolution of LULC on the national scale in Vietnam. Based on the GLC_FCS30 dataset, this paper analyzed the temporal and spatial evolution of LULC in Vietnam from 2000 to 2020 as well as its driving mechanism using methods such as dynamicity, flow direction diagrams, principal component analysis, and multivariate stepwise regression. The results show that: (1) cropland, forest, and shrubland are Vietnam’s most important land-cover types. In 2020, the above three types of land area accounted for 34.77%, 32.36%, and 26.13% of the total land area, respectively. (2) From 2000 to 2020, the area of cropland and forest areas continued to shrink (−5.64%, −3.96%); the area of shrubland, water bodies, and other land areas expanded (+4.87%, +12.29%, +15.04%); and the area of impervious surfaces expansion was the most significant (+100.40%). (3) The integrated dynamic degree of LULC in Vietnam shows a spatial differentiation of high in the south, followed by the north, and lowest in the center. In the early period (2000–2010), the LULC rate of change in each region was rapid, while it gradually decreased in the later period (2010–2020). The most important LULC changes in Vietnam can be divided into two parts: (a) the mutual conversion of forest, cropland, and shrubland and (b) one-way conversion of cropland to impervious surfaces. (4) LULC changes in Vietnam are mainly affected by economic development and human activities, especially the GDP, population, and urbanization rate. There is no reliable statistical relationship between LULC and climatic factors. The results of this study contribute to the analysis of LULC processes in similar regions, and will also help the Vietnamese government strengthen national land management and planning in a targeted manner.

Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013

Article

Full-text available

Apr 2017

Deforestation rates in primary humid tropical forests of the Brazilian Legal Amazon (BLA) have declined significantly since the early 2000s. Brazil’s national forest monitoring system provides extensive information for the BLA but lacks independent validation and systematic coverage outside of primary forests. We use a sample-based approach to consistently quantify 2000–2013 tree cover loss in all forest types of the region and characterize the types of forest disturbance. Our results provide unbiased forest loss area estimates, which confirm the reduction of primary forest clearing (deforestation) documented by official maps. By the end of the study period, nonprimary forest clearing, together with primary forest degradation within the BLA, became comparable in area to deforestation, accounting for an estimated 53% of gross tree cover loss area and 26 to 35% of gross aboveground carbon loss. The main type of tree cover loss in all forest types was agroindustrial clearing for pasture (63% of total loss area), followed by small-scale forest clearing (12%) and agroindustrial clearing for cropland (9%), with natural woodlands being directly converted into croplands more often than primary forests. Fire accounted for 9% of the 2000–2013 primary forest disturbance area, with peak disturbances corresponding to droughts in 2005, 2007, and 2010. The rate of selective logging exploitation remained constant throughout the study period, contributing to forest fire vulnerability and degradation pressures. As the forest land use transition advances within the BLA, comprehensive tracking of forest transitions beyond primary forest loss is required to achieve accurate carbon accounting and other monitoring objectives.

Deforestation risk due to commodity crop expansion in sub-Saharan Africa

Article

Full-text available

Apr 2017
ENVIRON RES LETT

Rapid integration of global agricultural markets and subsequent cropland displacement in recent decades increased large-scale tropical deforestation in South America and Southeast Asia. Growing land scarcity and more stringent land use regulations in these regions could incentivize the offshoring of export-oriented commodity crops to sub-Saharan Africa (SSA). We assess the effects of domestic- and export-oriented agricultural expansion on deforestation in SSA in recent decades. Analyses were conducted at the global, regional and local scales. We found that commodity crops are expanding in SSA, increasing pressure on tropical forests. Four Congo Basin countries, Sierra Leone, Liberia, and Côte d'Ivoire were most at risk in terms of exposure, vulnerability and pressures from agricultural expansion. These countries averaged the highest percent forest cover (58% ± 17.93) and lowest proportions of potentially available cropland outside forest areas (1% ± 0.89). Foreign investment in these countries was concentrated in oil palm production (81%), with a median investment area of 41 582 thousand ha. Cocoa, the fastest expanding export-oriented crop across SSA, accounted for 57% of global expansion in 2000–2013 at a rate of 132 thousand ha yr⁻¹. However, cocoa only amounted to 0.89% of foreign land investment. Commodity crop expansion in SSA appears largely driven by small- and medium-scale farmers rather than industrial plantations. Land-use changes associated with large-scale investments remain to be observed in many countries. Although domestic demand for commodity crops was associated with most agricultural expansion, we provide evidence of a growing influence of distant markets on land-use change in SSA.

The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013

Article

Full-text available

Jan 2017

An intact forest landscape (IFL) is a seamless mosaic of forest and naturally treeless ecosystems with no remotely detected signs of human activity and a minimum area of 500 km². IFLs are critical for stabilizing terrestrial carbon storage, harboring biodiversity, regulating hydrological regimes, and providing other ecosystem functions. Although the remaining IFLs comprise only 20% of tropical forest area, they account for 40% of the total aboveground tropical forest carbon. We show that global IFL extent has been reduced by 7.2% since the year 2000. An increasing rate of global IFL area reduction was found, largely driven by the tripling of IFL tropical forest loss in 2011–2013 compared to that in 2001–2003. Industrial logging, agricultural expansion, fire, and mining/resource extraction were the primary causes of IFL area reduction. Protected areas (International Union for Conservation of Nature categories I to III) were found to have a positive effect in slowing the reduction of IFL area from timber harvesting but were less effective in limiting agricultural expansion. The certification of logging concessions under responsible management had a negligible impact on slowing IFL fragmentation in the Congo Basin. Fragmentation of IFLs by logging and establishment of roads and other infrastructure initiates a cascade of changes that lead to landscape transformation and loss of conservation values. Given that only 12% of the global IFL area is protected, our results illustrate the need for planning and investment in carbon sequestration and biodiversity conservation efforts that target the most valuable remaining forests, as identified using the IFL approach.

Global Carbon Budget 2016

Article

Full-text available

Nov 2016

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), EFF was 9.3 ± 0.5 GtC yr⁻¹, ELUC 1.0 ± 0.5 GtC yr⁻¹, GATM 4.5 ± 0.1 GtC yr⁻¹, SOCEAN 2.6 ± 0.5 GtC yr⁻¹, and SLAND 3.1 ± 0.9 GtC yr⁻¹. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr⁻¹, showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr⁻¹ that took place during 2006–2015. Also, for 2015, ELUC was 1.3 ± 0.5 GtC yr⁻¹, GATM was 6.3 ± 0.2 GtC yr⁻¹, SOCEAN was 3.0 ± 0.5 GtC yr⁻¹, and SLAND was 1.9 ± 0.9 GtC yr⁻¹. GATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Niño conditions of 2015–2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565 ± 55 GtC (2075 ± 205 GtCO2) for 1870–2016, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2016).

Global Carbon Budget 2016

Article

Full-text available

Oct 2016

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), EFF was 9.3 ± 0.5 GtC yr−1, ELUC 1.0 ± 0.5 GtC yr−1, GATM 4.5 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 3.1 ± 0.9 GtC yr−1. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1, showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr−1 that took place during 2006–2015. Also, for 2015, ELUC was 1.3 ± 0.5 GtC yr−1, GATM was 6.3 ± 0.2 GtC yr−1, SOCEAN was 3.0 ± 0.5 GtC yr−1, and SLAND was 1.9 ± 0.9 GtC yr−1. GATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Niño conditions of 2015–2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565 ± 55 GtC (2075 ± 205 GtCO2) for 1870–2016, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center.

Earth science data records of global forest cover and change: Assessment of accuracy in 1990, 2000, and 2005 epochs

Article

Full-text available

Oct 2016
REMOTE SENS ENVIRON

The Global Land Cover Facility (GLCF) global forest-cover and -change dataset is a multi-temporal depiction of long-term (multi-decadal), global forest dynamics at high (30-m) resolution. Based on per-pixel estimates of percentage tree cover and their associated uncertainty, the dataset currently represents binary forest cover in nominal 1990, 2000, and 2005 epochs, as well as gains and losses over time. A comprehensive accuracy assessment of the GLCF dataset was performed using a global, design-based sample of 27,988 independent, visually interpreted reference points collected through a two-stage, stratified sampling design wherein experts visually identified forest cover and change in each of the 3 epochs based on Landsat and high-resolution satellite images, vegetation index profiles, and field photos. Consistent across epochs, the overall accuracy of the static forest-cover layers was 91%, and the overall accuracy of forest-cover change was >88% —among the highest accuracies reported for recent global forest- and land-cover data products. Both commission error (CE) and omission error (OE) were low for static forest cover in each epoch and for the stable classes between epochs (CE<3%, OE<22%), but errors were larger for forest loss (45%≤CE<62%, 47%<OE<55%) and gain (66%≤CE<85%, 61%<OE<84%). Accuracy was lower in sparse forests and savannahs, i.e., where tree cover was at or near the 30% threshold used to discriminate forest from non-forest cover. Discrimination of forest had a low rate of commission error and slight negative bias, especially in areas with low tree cover. After adjusting global area estimates to reference data, 39.28±1.34 million km2 and 38.81±1.34 million km2 of forest were respectively identified in 2000 and 2005 globally, and 33.16±1.36 million km2 of forest were estimated in the available coverage of Landsat data circa-1990. Forest loss and gain were estimated to have been 0.73±0.38 and 0.28±0.26 million km2 between 2000 and 2005, and 1.08±0.53 and 0.53±0.47 million km2 between 1990 and 2000. These estimates of accuracy are required for rigorous use of the data in the Earth sciences (e.g., ecology, economics, hydrology, climatology) as well as for fusion with other records of global change. The GLCF forest -cover and -change dataset is available for free public download at the GLCF website (http://www.landcover.org).

Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models

Article

Feb 2018

Many global change stresses on terrestrial and marine ecosystems affect not only ecosystem services that are essential to humankind, but also the trajectory of future climate by altering energy and mass exchanges with the atmosphere. Earth system models, which simulate terrestrial and marine ecosystems and biogeochemical cycles, offer a common framework for ecological research related to climate processes; analyses of vulnerability, impacts, and adaptation; and climate change mitigation. They provide an opportunity to move beyond physical descriptors of atmospheric and oceanic states to societally relevant quantities such as wildfire risk, habitat loss, water availability, and crop, fishery, and timber yields. To achieve this, the science of climate prediction must be extended to a more multifaceted Earth system prediction that includes the biosphere and its resources.

Global bare ground gain from 2000 to 2012 using Landsat imagery

Article

Jun 2017
REMOTE SENS ENVIRON

Bare ground gain, or vegetative cover loss, is an important component of global land cover change resulting from economic drivers such as urbanization and resource extraction. In this study, we characterized global bare ground gain from Landsat time series. The maps were then used to stratify the globe in creating a sample-based estimate of global bare ground gain extent, land cover/land use outcomes, and associated uncertainties from 2000 to 2012. An estimated total of 93,896 km² (± 9317 km² for 95% confidence interval) of bare ground gain occurred over the study period. Human-induced bare ground gain accounted for 95% of the total and consisted of the following components: 39% commercial and residential development, 23% resource extraction, 21% infrastructure development, 11% transitional, and 1% greenhouses. East Asia and the Pacific accounted for nearly half of all global bare ground gain area (45%), with China alone accounting for 35% of global gain. The United States was second to China, accounting for 17% of total bare ground gain. Land cover/land use outcomes of bare ground gain varied between regions and countries, reflecting different stages of development and the possible use of bare ground gain as an indicator of economic activity.

Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa

Article

Mar 2017
Nat. Ecol. Evol.

The rapidly growing human population in sub-Saharan Africa generates increasing demand for agricultural land and forest products, which presumably leads to deforestation. Conversely, a greening of African drylands has been reported, but this has been difficult to associate with changes in woody vegetation. There is thus an incomplete understanding of how woody vegetation responds to socio-economic and environmental change. Here we used a passive microwave Earth observation data set to document two different trends in land area with woody cover for 1992–2011: 36% of the land area (6,870,000 km2) had an increase in woody cover largely in drylands, and 11% had a decrease (2,150,000 km2), mostly in humid zones. Increases in woody cover were associated with low population growth, and were driven by increases in CO2 in the humid zones and by increases in precipitation in drylands, whereas decreases in woody cover were associated with high population growth. The spatially distinct pattern of these opposing trends reflects, first, the natural response of vegetation to precipitation and atmospheric CO2, and second, deforestation in humid areas, minor in size but important for ecosystem services, such as biodiversity and carbon stocks. This nuanced picture of changes in woody cover challenges widely held views of a general and ongoing reduction of the woody vegetation in Africa.

The Emergence of Land Change Science for Global Environmental Change and Sustainability

Article