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

Diagnosis of the fragmentation of urban ecological network structure and its social-ecological responses

The fragmentation of ecological network structures has become a common problem faced by cities. By establishing the urban ecological network under a specific socio-ecological system framework, we aimed to propose a quantitative index to diagnose the fragmentation of the network structure, and to construct detection model to explore the driving factors and mechanism of the network fragmentation. Using Shenzhen City as an example, we used the Floyd-Prim algorithm to generate the skeleton structure of the ecological network and construct a density discontinuity index to diagnose network fragmentation. Combined with the ecological network scenario, social-ecological system framework and a two-layer indicator system were constructed. The detection models were then established to explore the drivers of network disruption and their mode of impact. The models show that the average degree of network fragmentation in Shenzhen was 0.13, and the density of about 85% of corridor discontinuities was greater than 0.01, reflecting the serious state of structural fragmentation. Corridors with more severe structural fragmentation have poorer social-ecological coordination. The fragmentation in Shenzhen was mainly affected by the activities of actors (A) at the microlevel and the resource system (RS) at the macrolevel. The methods and the framework of socio-ecosystem analysis proposed in this paper can reveal the driving factors and influence modes of network fragmentation, providing decision-making reference for ecological restoration practice in urbanized areas.

Analysis and simulation of the driving mechanism and ecological effects of land cover change in the Weihe River basin, China

Land cover change (LCC) is both a consequence and a cause of global environmental change. This paper attempts to construct a framework to reveal the driving mechanism and ecological effects of different ecological factors under LCC and to explore the ecological characteristics of future LCC. A rule-mining framework based on a land expansion analysis strategy (LEAS) in the patch-generating land use simulation (PLUS) model was used to analyze the drivers of LCC. Neighborhood analysis and ecological effect index were used to investigate multiple ecological effects of LCC. Remote sensing-based ecological indices (RSEI) and the PLUS and stepwise regression model were introduced to explore and predict the integrated ecological effect of LCC. Focusing on the Weihe River basin, study's main drivers of LCC were precipitation, temperature, elevation, population, water table depth, proximity to governments and motorways, GDP, and topsoil organic carbon were the main drivers of LCC. Change directionality were similar for the effects of greenness and biomass formation but opposite for summertime and wintertime temperature. In addition, the conversion of land cover types to cropland had the most significant integrated ecological effect, followed by forest, grassland-shrubland, and other types. The RSEI is predicted to rise to 0.77 in 2030, and the areas where the ecological quality grade will improve and decrease are concentrated on the east and west sides of Ziwuling Mountain, respectively. The findings of this study have practical significance for land management and ecological protection.

Land cover change in global drylands: A review

As a sensitive region, identifying land cover change in drylands is critical to understanding global environmental change. However, the current findings related to land cover change in drylands are not uniform due to differences in data and methods among studies. We compared and judged the spatial and temporal characteristics, driving forces, and ecological effects by identifying the main findings of land cover change in drylands at global and regional scales (especially in China) to strengthen the overall understanding of land cover change in drylands. Four main points were obtained. First, while most studies found that drylands were experiencing vegetation greening, some evidence showed decreases in vegetation and large increases in bare land due to inconsistencies in the datasets and the study phases. Second, the dominant factors affecting land cover change in drylands are precipitation, agricultural activities, and urban expansion. Third, the impact of land cover change on the water cycle, especially the impact of afforestation on water resources in drylands, is of great concern. Finally, drylands experience severe land degradation and require dataset matching (classification standards, resolution, etc.) to quantify the impact of human activities on land cover.

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.

Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

Impact of land use/land cover dynamics on ecosystem service value-a case from Lake Malombe, Southern Malawi

Lake Malombe ecosystem provides a vast range of services that are vital for the sustenance of the riparian communities. Understanding land use and land cover (LULC) dynamics, as well as the associated impacts on the multiple ecosystem service value (ESV), is extremely important in decision-making processes and effective implementation of an ecosystem-based management approach. This study analyzed the LULC dynamics from 1989 to 2019. The primary objective of the study was to assess its impact on ecosystem services (ES). The ESV was determined using LULC analysis and established global ESV coefficient. The LULC analysis showed a reduction in forest cover by 84.73% during the study period. Built-up, cultivated land, bare land, shrubs, and grassland increased considerably. Rapid population growth, climate change, government policy conflicts, and poverty were identified as the most important drivers of LULC dynamics. Based on ESVs estimations, the ES changes instigated by LULC dynamics in the study area result in an average loss of US$45.58 million during the study period. Within the same period, the lake fishery also recorded a net loss of US$8.63 million. The highest net loss of US$79.832 million was recorded from 1999 to 2019 due to increased loss of forest, a decrease in water bodies and marsh areas. The sensitivity analysis (CS) indicated that our estimates were relatively robust. This study findings provide a piece of empirical evidence that LULC dynamics in the Lake Malombe catchment have led to a significant loss of ESVs, with serious implications for the livelihoods of the local population.

Spread of COVID-19 in Zambia: An assessment of environmental and socioeconomic factors using a classification tree approach

The global pandemic emergent from SARS-COV-2 (COVID-19) has continued to cause both health and socio-economic challenges worldwide. However, there is limited information on the factors affecting the dynamics of COVID-19, especially in developing countries, including African countries. In this study, we have focused on understanding the association of COVID-19 cases with environmental and socioeconomic factors in Zambia - a sub-Saharan African country. We used Zambia's district-level COVID-19 data, covering 18 March 2020 (i.e., from first reported cases) to 17 July 2020. Geospatial approaches were used to organize, extract and establish the dataset, while a classification tree (CT) technique was employed to analyze the factors associated with the COVID-19 cases. The analyses were conducted in two stages: (1) the binary analysis of occurrences of COVID-19 (i.e., COVID-19 or No COVID-19), and (2) a risk level analysis which grouped the number of cases into four risk levels (high, moderate, low and very low). The results showed that the distribution of COVID-19 cases in Zambia was significantly influenced by the socioeconomic factors compared to environmental factors. More specifically, the binary model showed that distance to the airport, population density and distance to the town centres were the most combination influential factors, while the risk level analysis indicated that areas with high rates of human immuno-deficient virus (HIV) infection had relatively high chances of having many COVID-19 cases compared to areas with low HIV rates. The districts that are far from major urban establishments and that experience higher temperatures have lower chances of having COVID-19 cases. This study makes two major contributions towards the understanding of COVID-19 dynamics: (1) the methodology presented here can be effectively applied in other areas to understand the association of environmental and socioeconomic factors with COVID-19 cases, and (2), the findings from this study present the empirical evidence of the relationship between COVID-19 cases and their associated environmental and socioeconomic factors. Further studies are needed to understand the relationship of this disease and the associated factors in different cultural settings, seasons and age groups, especially as the COVID-19 cases increase and spread in many countries.

Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.

Sentinel-2 Data for Land Cover/Use Mapping: A Review

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification

Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Copperbelt Province of Zambia. Classification was done using object-based image analysis (OBIA) through the development of rulesets with thresholds defined by the DTs. The performance of the DT algorithms was assessed based on: (1) DT accuracy through cross-validation; (2) land cover classification accuracy of thematic maps; and (3) other structure properties such as the sizes of the tree diagrams and variable selection abilities. The results indicate that only the rulesets developed from DT algorithms with simple structures and a minimum number of variables produced high land cover classification accuracies (overall accuracy > 88%). Thus, algorithms such as Tree and Rpart produced higher classification results as compared to C5.0 and Party DT algorithms, which involve many variables in classification. This high accuracy has been attributed to the ability to minimize overfitting and the capacity to handle noise in the data during training by the Tree and Rpart DTs. The study produced new insights on the formal selection of DT algorithms for OBIA ruleset development. Therefore, the Tree and Rpart algorithms could be used for developing rulesets because they produce high land cover classification accuracies and have simple structures. As an avenue of future studies, the performance of DT algorithms can be compared with contemporary machine-learning classifiers (e.g., Random Forest and Support Vector Machine).